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CHIU, CHUANG-CHIEN, TONG-HONG LIN, and BEN-YI LIAU. "USING CORRELATION COEFFICIENT IN ECG WAVEFORM FOR ARRHYTHMIA DETECTION." Biomedical Engineering: Applications, Basis and Communications 17, no. 03 (June 25, 2005): 147–52. http://dx.doi.org/10.4015/s1016237205000238.
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Arrhythmia is one kind of diseases that gives rise to the death and possibly forms the immedicable danger. The most common cardiac arrhythmia is the ventricular premature beat. The main purpose of this study is to develop an efficient arrhythmia detection algorithm based on the morphology characteristics of arrhythmias using correlation coefficient in ECG signal. Subjects for experiments included normal subjects, patients with atrial premature contraction (APC), and patients with ventricular premature contraction (PVC). So and Chan's algorithm was used to find the locations of QRS complexes. When the QRS complexes were detected, the correlation coefficient and RR-interval were utilized to calculate the similarity of arrhythmias. The algorithm was tested using MIT-BIH arrhythmia database and every QRS complex was classified in the database. The total number of test data was 538, 9 and 24 for normal beats, APCs and PVCs, respectively. The results are presented in terms of, performance, positive predication and sensitivity. High overall performance (99.3%) for the classification of the different categories of arrhythmic beats was achieved. The positive prediction results of the system reach 99.44%, 100% and 95.35% for normal beats, APCs and PVCs, respectively. The sensitivity results of the system are 99.81%, 81.82% and 95.83% for normal beats, APCs and PVCs, respectively. Results revealed that the system is accurate and efficient to classify arrhythmias resulted from APC or PVC. The proposed arrhythmia detection algorithm is therefore helpful to the clinical diagnosis.
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Zhai, Yuyun, Jinwei Li, and Quan Zhang. "Network pharmacology and molecular docking analyses of the potential target proteins and molecular mechanisms underlying the anti-arrhythmic effects of Sophora Flavescens." Medicine 102, no. 30 (July 28, 2023): e34504. http://dx.doi.org/10.1097/md.0000000000034504.
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The objective was to investigate the potential cardiac arrhythmia-related target proteins and molecular mechanisms underlying the anti-arrhythmic effects of Sophora flavescens using network pharmacology and molecular docking. The bioactive ingredients and related target proteins of S flavescens obtained from the Traditional Chinese medicine systems pharmacology data platform, and gene names for target proteins were obtained from the UniProt database. Arrhythmia-related genes were identified by screening GeneCards and Online Mendelian inheritance in man databases. A Venn diagram was used to identify the key arrhythmia-related genes that are potentially targeted by the bioactive ingredients of S flavescens. Furthermore, CytoScape 3.7.2 software was used to construct an “ingredient-target” network diagram and the “drug-ingredient-target-disease” network diagram. We performed gene ontology and Kyoto encyclopedia of genes and genomes enrichment analysis in the Metascape database and performed the docking analysis using CB-Dock software. We identified 45 main bioactive ingredients, from S flavescens and 66 arrhythmia-related target proteins. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis showed that these targets were related to the chemical carcinogenesis-receptor activation signaling pathway, lipid and atherosclerosis signaling pathway, and fluid shear stress and atherosclerosis signaling pathway. Molecular docking showed that the target protein had good binding power with the main active components of the compound of S flavescens. Our study demonstrated the synergistic effects of multiple bioactive components of S flavescens on multiple arrhythmia-related target proteins and identified potential therapeutic mechanisms underlying the anti-arrhythmic effects of S flavescens, providing new clinical ideas for arrhythmia treatment.
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Deal, Barbara J., Constantine Mavroudis, Jeffrey Phillip Jacobs, Melanie Gevitz, and Carl Lewis Backer. "Arrhythmic complications associated with the treatment of patients with congenital cardiac disease: consensus definitions from the Multi-Societal Database Committee for Pediatric and Congenital Heart Disease." Cardiology in the Young 18, S2 (December 2008): 202–5. http://dx.doi.org/10.1017/s104795110800293x.
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AbstractA detailed hierarchal nomenclature of arrhythmias is offered with definition of its applications to diagnosis and complications. The conceptual and organizational approach to discussion of arrhythmias employs the following sequence: location – mechanism – aetiology – duration. The classification of arrhythmias is heuristically divided into an anatomical hierarchy: atrial, junctional, ventricular, or atrioventricular. Mechanisms are most simplistically classified as either reentrant, such as macro-reentrant atrial tachycardia, previously described as atrial flutter, or focal, such as automatic or micro-reentrant tachycardia, for example, junctional ectopic tachycardia. The aetiology of arrhythmias can be either iatrogenic, such as postsurgical, or non-iatrogenic, such as genetic or congenital, and in many cases is multi-factorial. Assigning an aetiology to an arrhythmia is distinct from understanding the mechanism of the arrhythmia, yet assignment of a possible aetiology of an arrhythmia may have important therapeutic implications in certain clinical settings. For example, postoperative atrial arrhythmias in patients after cardiac transplantation may be harbingers of rejection or consequent to remediable imbalances of electrolytes. The duration, frequency of, and time to occurrence of arrhythmia are temporal measures that further refine arrhythmia definition, and may offer insight into ascription of aetiology. Finally, arrhythmias do not occur in a void, but interact with other organ systems. Arrhythmias not only can result from perturbations of other organ systems, such as renal failure, but can produce dysfunction in other organ systems due to haemodynamic compromise or embolic phenomena.
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Moreland-Head, Lindsay N., James C. Coons, Amy L. Seybert, Matthew P. Gray, and Sandra L. Kane-Gill. "Use of Disproportionality Analysis to Identify Previously Unknown Drug-Associated Causes of Cardiac Arrhythmias Using the Food and Drug Administration Adverse Event Reporting System (FAERS) Database." Journal of Cardiovascular Pharmacology and Therapeutics 26, no. 4 (January 6, 2021): 341–48. http://dx.doi.org/10.1177/1074248420984082.
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Introduction: Drug-induced QTc-prolongation is a well-known adverse drug reaction (ADR), however there is limited knowledge of other drug-induced arrhythmias. Purpose: The objective of this study is to determine the drugs reported to be associated with arrhythmias other than QTc-prolongation using the FAERS database, possibly identifying potential drug causes that have not been reported previously. Methods: FAERS reports from 2004 quarter 1 through 2019 quarter 1 were combined to create a dataset of approximately 11.6 million reports. Search terms for arrhythmias of interest were selected from the Standardized MedDRA Queries (SMQ) Version 12.0. Frequency of the cardiac arrhythmias were determined for atrial fibrillation, atrioventricular block, bradyarrhythmia, bundle branch block, supraventricular tachycardia, and ventricular fibrillation and linked to the reported causal medications. Reports were further categorized by prior evidence associations using package inserts and established drug databases. A reporting odds ratio (ROR) and confidence interval (CI) were calculated for the ADRs for each drug and each of the 6 cardiac arrhythmias. Results: Of the 11.6 million reports in the FAERS database, 68,989 were specific to cardiac arrhythmias of interest. There were 61 identified medication-reported arrhythmia pairs for the 6 arrhythmia groups with 33 found to have an unknown reported association. Rosiglitazone was the most frequently medication reported across all arrhythmias [ROR 6.02 (CI: 5.82-6.22)]. Other medications with significant findings included: rofecoxib, digoxin, alendronate, lenalidomide, dronedarone, zoledronic acid, adalimumab, dabigatran, and interferon beta-1b. Conclusion: Upon retrospective analysis of the FAERS database, the majority of drug-associated arrhythmias reported were unknown suggesting new potential drug causes. Cardiac arrhythmias other than QTc prolongation are a new area of focus for pharmacovigilance and medication safety. Consideration of future studies should be given to using the FAERS database as a timely pharmacovigilance tool to identify unknown adverse events of medications.
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Zeng, Yuni, Hang Lv, Mingfeng Jiang, Jucheng Zhang, Ling Xia, Yaming Wang, and Zhikang Wang. "Deep arrhythmia classification based on SENet and lightweight context transform." Mathematical Biosciences and Engineering 20, no. 1 (2022): 1–17. http://dx.doi.org/10.3934/mbe.2023001.
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<abstract> <p>Arrhythmia is one of the common cardiovascular diseases. Nowadays, many methods identify arrhythmias from electrocardiograms (ECGs) by computer-aided systems. However, computer-aided systems could not identify arrhythmias effectively due to various the morphological change of abnormal ECG data. This paper proposes a deep method to classify ECG samples. Firstly, ECG features are extracted through continuous wavelet transform. Then, our method realizes the arrhythmia classification based on the new lightweight context transform blocks. The block is proposed by improving the linear content transform block by squeeze-and-excitation network and linear transformation. Finally, the proposed method is validated on the MIT-BIH arrhythmia database. The experimental results show that the proposed method can achieve a high accuracy on arrhythmia classification.</p> </abstract>
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Kapoor, Ankita, Samarthkumar Thakkar, Lucas Battel, Harsh P. Patel, Nikhil Agrawal, Shipra Gandhi, Pritika Manaktala, et al. "The Prevalence and Impact of Arrhythmias in Hospitalized Patients with Sickle Cell Disorders: A Large Database Analysis." Blood 136, Supplement 1 (November 5, 2020): 5–6. http://dx.doi.org/10.1182/blood-2020-142099.
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Introduction: Sickle cell disorders (SCD) is associated with progressive dysfunction of vital organs, including the cardiovascular system. While the development of pulmonary hypertension and left ventricular dysfunction have been previously studied, the burden of arrhythmias in SCD patients remains largely unknown. Thus, we aim to describe and analyze the prevalence and impact of arrhythmias in hospitalized adult patients with SCD and their impact in patient-oriented outcomes. Methods: We identified incident arrhythmias in patients with SCD in the National Inpatient Sample (NIS) database in 2 years (2016-2017), using ICD-10 codes. We compared major patient characteristics, outcomes, and economic impact between groups of SCD patients with and without documented arrhythmias. A logistic regression model was used to control for age, sex, race, admission type, hospital characteristics, and relevant comorbidities. To increase statistical robustness, propensity-score matching for age, sex, income, and comorbidities was used to match 1144 SCD patients with arrhythmia and 1144 patients without. Results: Among inpatients with SCD in the database, 5,930 of 174,450 patients with SCD had documented arrhythmias (3.4%). The arrhythmia group consisted mostly of patients' sickle cell disease (5,650; 95%), while 245 had sickle cell trait, and 35 were classified as having other sickle cell disorders. Individuals in the arrhythmia group were significantly older (mean age 41.3, SD 14.1) than those with no arrhythmia (mean age 31.5, SD 10.3). Further, arrhythmia group had higher prevalence of hypertension (44.2% vs 19.1%, p<0.001), congestive heart failure (25.8% vs 4.1%, p<0.001), chronic kidney disease (24.0% vs 5.6%, p<0.001), valvular heart disease (9.3% vs 1.5%, p<0.001), myocardial infarction (4.1% vs 1.25%, p<0.001), type 2 diabetes mellitus (3.5% vs 1.7%, p<0.001), and pulmonary hypertension (3.5% vs 1.2%, p<0.001). When looking at major outcomes, after adjusting for confounders, arrhythmias were positively associated with all cause in-hospital mortality with an adjusted OR of 53.6 (95% CI 44.3, 65.1). After propensity-matching (Table 1), the arrhythmia group had a higher rate of all-cause in-hospital mortality (6.12% vs 0.35%, p<0.001), higher median length of stay (6.8 days vs 5.8 days, p<0.001), and mean total hospital charges ($13,441 vs $10,255, p<0.001). There was no statistically significant difference in the rate of stroke between both groups. Conclusions: The presence of arrhythmias in patients with SCD was associated with markedly increased all cause in-hospital mortality, even after adjusting for confounders via logistic regression and propensity-score matching analyses. Despite a relatively low overall prevalence of 3.4% among this large inpatient cohort, this data suggests that arrhythmias may confer an important excess disease burden in SCD patients, including higher mortality. Intuitively, arrythmias confer higher mortality in patients with chronic disorders and multiple cardiovascular risks, whether this is specific to SCD deserves further study. Also, studies are needed to better understand the occurrence particularly in relation to active vaso-occlusive crisis and to evaluate whether SCD individuals with arrhythmias could potentially benefit from more intensive monitoring and/or better cardiovascular disease control. Disclosures No relevant conflicts of interest to declare.
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OTHMAN, MOHD AFZAN, and NORLAILI MAT SAFRI. "CHARACTERIZATION OF VENTRICULAR ARRHYTHMIAS USING A SEMANTIC MINING ALGORITHM." Journal of Mechanics in Medicine and Biology 12, no. 03 (June 2012): 1250049. http://dx.doi.org/10.1142/s0219519412004946.
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Ventricular arrhythmia, especially ventricular fibrillation, is a type of arrhythmia that can cause sudden death. The aim of this paper is to characterize ventricular arrhythmias using semantic mining by extracting their significant characteristics (frequency, damping coefficient and input signal) from electrocardiogram (ECG) signals that represent the biological behavior of the cardiovascular system. Real data from an arrhythmia database are used after noise filtering and were statistically classified into two groups; normal sinus rhythm (N) and ventricular arrhythmia (V). The proposed method achieved high sensitivity and specificity (98.1% and 97.7%, respectively) and was capable of describing the differences between the N and V types in the ECG signal.
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Xu, Gang, Guangxin Xing, Juanjuan Jiang, Jian Jiang, and Yongsheng Ke. "Arrhythmia Detection Using Gated Recurrent Unit Network with ECG Signals." Journal of Medical Imaging and Health Informatics 10, no. 3 (March 1, 2020): 750–57. http://dx.doi.org/10.1166/jmihi.2020.2928.
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Background: Arrhythmia is a kind of heart disorder characterized by irregular heartbeats which can be detected with Electrocardiographic (ECG) signals. Accurate and early detection along with differentiation of arrhythmias is of great importance in a clinical setting. However, visual analysis of ECG signal is a challenging and timeconsuming work. We have developed an automatic arrhythmia detection model with deep learning framework to expedite the diagnosis of arrhythmia with a high degree of accuracy. Methods: We proposed a novel automatic arrhythmia detection model utilizing a combination of 1D convolutional neural network (1D-CNN) and Gated Recurrent Unit (GRU) network for the diagnosis of five different arrhythmia on ECG signals taken from the MITBIT arrhythmia physio bank database. Results: The proposed system showed a high classification performance in handling variable length ECG signal data, achieving an accuracy rate of 99.45%, sensitivity of 98.35% and specificity of 99.21% and a F1-Score of 98.95% using a five-fold cross validation strategy. Conclusions: Combining 1D-CNN and GRU networks yielded a higher degree of accuracy compared with other deep learning networks. Our proposed arrhythmia detection method may be a powerful tool to aid clinicians in accurately detecting common arrhythmias on routine ECG screening.
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N. S. V Rama Raju, N., V. Malleswara Rao, and I. Srinivasa Rao. "Automatic detection and classification of cardiac arrhythmia using neural network." International Journal of Engineering & Technology 7, no. 3 (July 11, 2018): 1482. http://dx.doi.org/10.14419/ijet.v7i3.14084.
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This paper proposes a Neural Network classifier model for the automatic identification of the ventricular and supraventricular arrhythmias cardiac arrhythmias. The wavelet transform (DWT) and dual tree complex wavelet transform (DTCWT) is applied for QRS complex detec-tion. After segmentation both feature of DWT and DTCWT is combined for feature extraction, statistical feature has been calculated to re-duce the overhead of classifier. An adaptive filtering with the soft computed wavelet thersholding to the signals before the extraction is done in pre-processing. Different ECG database is considered to evaluate the propose work with MIT-BIH database Normal Sinus Rhythm Da-tabase (NSRD) , and MIT-BIH Supraventricular Arrhythmia Database (svdb) .The evaluated outcomes of ECG classification claims 98 -99 % of accuracy under different training and testing situation.
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Herman, Jeffrey N., Richard I. Fogel, Philip J. Podrid, and Gary R. Garber. "Entropy: A cardiac arrhythmia multimedia database." Journal of the American College of Cardiology 17, no. 2 (February 1991): A10. http://dx.doi.org/10.1016/0735-1097(91)91008-3.
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Umapathi, Krishna Kishore, Aravind Thavamani, Harshitha Dhanpalreddy, and Hoang H. Nguyen. "Prevalence of cardiac arrhythmias in cannabis use disorder related hospitalizations in teenagers from 2003 to 2016 in the United States." EP Europace 23, no. 8 (March 16, 2021): 1302–9. http://dx.doi.org/10.1093/europace/euab033.
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Abstract Aims Cannabis is an increasingly common recreational substance used by teenagers. However, there is limited data probing association of cardiac arrhythmias with marijuana use in this population. Methods and Results We provide prevalence trends, disease burden and healthcare utilization of cardiac arrhythmias associated with cannabis use disorder (CUD) in hospitalized teenagers (13–20 years) using a large national administrative database in the United States from 2003–2016. We used partial least square regression analysis for assessing trends in prevalence of cardiac arrhythmias and multiple logistic regression to elucidate independent predictors of arrhythmias associated with CUD. Among all CUD related hospitalizations (n = 876, 431), 0.5% had arrhythmias. Prevalence trends of arrhythmias among CUD increased six-fold during the study period (P < 0.001). CUD was more prevalent in males and older teens (both P < 0.001). There was a significant risk for mortality when CUD was associated with arrhythmia (7.4% vs. 0.1%, P < 0.001). While mean length-of-stay (LOS) was shorter (4.4 vs. 5.4 days, P < 0.001) for patients with CUD, they incurred three times higher mean hospitalization charges when compared to CUD patients without arrhythmia ($45 959 vs. $18 986, P < 0.001). Both LOS and hospitalization charges showed an uptrend during the study period (P < 0.001). Congenital heart disease, congestive heart failure, hypertension, and obesity independently predicted arrhythmias in CUD while other substance abuse did not change the risk of arrhythmia in CUD. Conclusion Arrhythmia burden is increasing among teenagers with CUD, and co-occurrence of arrhythmia and CUD worsens hospital outcomes.
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Giriprasad Gaddam, P., A. Sanjeeva reddy, and R. V. Sreehari. "Automatic Classification of Cardiac Arrhythmias based on ECG Signals Using Transferred Deep Learning Convolution Neural Network." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012058. http://dx.doi.org/10.1088/1742-6596/2089/1/012058.
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Abstract In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Four different categories of ECG waveform were selected from four PhysioNet MIT-BIH databases, namely arrhythmia database, Normal Sinus Rhythm database, Malignant Ventricular Ectopy database and BIDMC Congestive heart failure database to examine the proposed technique. The major interest of the present study is to develop a transferred deep learning algorithm for automatic categorization of the mentioned four different heart diseases. Final results proved that the 2-D scalogram images trained with a deep convolutional neural network CNN with transfer learning technique (AlexNet) pepped up with a prominent accuracy of 95.67%. Hence, it is worthwhile to say the above stated algorithm demonstrates as an effective automated heart disease detection tool
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Soniwala, Mujtaba, Saadia Sherazi, Susan Schleede, Scott McNitt, Tina Faugh, Jeremiah Moore, Justin Foster, et al. "Arrhythmia Burden in Patients with Indolent Lymphoma." Blood 136, Supplement 1 (November 5, 2020): 6–7. http://dx.doi.org/10.1182/blood-2020-140053.
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Introduction Indolent Non-Hodgkin lymphomas (NHL) comprise a heterogeneous group of diseases including marginal zone lymphoma (MZL), lymphoplasmacytic lymphoma (LPL), small lymphocytic lymphoma/chronic lymphocytic leukemia (SLL/CLL), and follicular lymphoma (FL). These compose a heterogenous group of disorders that frequently measures survival in years due to the long natural history of these diseases. Frequency and morbidity of cardiac arrhythmias in patients with indolent lymphoma is unknown, but recent observations note that arrhythmias are an increasing problem. Due to advances in treatment for indolent NHL with emergence of novel therapeutics, combined with an aging population and a long natural history, understanding of arrhythmia burden in indolent lymphoma is an area of research with important implications for patients undergoing active treatment as well as for long term lymphoma survivors. Methods Adult patients 18 years or older with indolent NHL treated at the University of Rochester Wilmot Cancer Institute between 2013-2019 were included in the Cardio-Oncology Lymphoid Malignancies Database and analyzed. The primary objective of this study was to define the rate of arrhythmic events and sudden cardiac death in patients with indolent lymphoma during treatment. Cardiac arrhythmias including ventricular arrhythmias (VT/VF), atrial arrhythmias (atrial fibrillation (afib), flutter, SVT and atrial tachycardia), and bradyarrhythmias were identified using ICD-10 codes. Kaplan-Meier survival analysis was used to assess cumulative probability of arrhythmia. Results There were nine hundred and eighteen patients who were diagnosed with indolent lymphoma. Diagnoses included: CLL, N=414; FL, N=284; MZL, N=144; LPL, N=76. Median age was 64, and 43% were female. There were 383 (42%) patients who received treatment. Treatments were classified as chemotherapy, targeted therapy, monoclonal antibodies/immunotherapy, and combination therapy. There were no significant differences in baseline characteristics between treated and never treated patients. At the time of diagnosis, 277 patients (30%) had hypertension, 101 (11%) had prior history of arrhythmia. During median follow up of 24 months, 168 patients (18%) developed a new or recurrent arrhythmia based on ICD-10 codes documented in the electronic medical record. Sixty-three out of one hundred sixty-eight patients had both prior history of and recurrence of arrhythmia, while one hundred five had a new diagnosis of arrhythmia. Afib was the most common arrhythmia, noted in 81 patients (9%). At 6 months from diagnosis, cumulative probability of developing any arrhythmia was 8% (Figure 1). Of all arrhythmias, 89/168 (53%) occurred in SLL/CLL, 35/168 (21%) in FL, 17/168 (10%) in LPL, 27/168 (16%) in MZL. Arrhythmias on treatment occurred in 4/95 patients receiving chemotherapy alone (4.2%), 12/95 patients receiving monoclonal antibodies/immunotherapy (12.6%), and 28/95 patients receiving targeted therapy (29.4%). Most arrhythmias (51/95; 53.6%) occurred in patients receiving combination therapy (chemoimmunotherapy or targeted/immunotherapy). Overall, there were 80 (9%) deaths. Ten deaths were related to cardiovascular diseases; of which 8/10 (80%) were from sudden cardiac death. Conclusions This real-world cohort demonstrates that patients with indolent lymphoma could have an increased risk of cardiac arrhythmias that is increased by treatment. Afib was the most common arrhythmia identified in this study and appears increased compared to the incidence in the general age matched population (1-1.8 per 100 person-years). Surprisingly, of 80 deaths, 8 (10%) were attributed to sudden cardiac death. This data set contributes important information that can help identify patients at increased risk of cardiovascular morbidity and mortality that can impact treatment. Prospective monitoring in these patients may better define the incidence and associated risks of arrhythmias. Future directions will focus on risk factors for arrhythmias and developing an approach to prevent and treat arrhythmias in this patient population. Updated results will be presented at the meeting. Disclosures Zent: Acerta / Astra Zeneca: Research Funding; TG Therapeutics, Inc: Research Funding; Mentrik Biotech: Research Funding. Barr:Janssen: Consultancy; Abbvie/Pharmacyclics: Consultancy, Research Funding; Verastem: Consultancy; Celgene: Consultancy; Seattle Genetics: Consultancy; TG therapeutics: Consultancy, Research Funding; Morphosys: Consultancy; Gilead: Consultancy; AstraZeneca: Consultancy, Research Funding; Merck: Consultancy; Genentech: Consultancy. Reagan:Kite, a Gilead Company: Consultancy; Seattle Genetics: Research Funding; Curis: Consultancy. Friedberg:Seattle Genetics: Research Funding; Roche: Other: Travel expenses; Bayer: Consultancy; Astellas: Consultancy; Acerta Pharma - A member of the AstraZeneca Group, Bayer HealthCare Pharmaceuticals.: Other; Kite Pharmaceuticals: Research Funding; Portola Pharmaceuticals: Consultancy.
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Hammad, Mohamed, Souham Meshoul, Piotr Dziwiński, Paweł Pławiak, and Ibrahim A. Elgendy. "Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification." Sensors 22, no. 23 (December 1, 2022): 9347. http://dx.doi.org/10.3390/s22239347.
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An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system’s effectiveness is examined by using a publicly accessible database and a comparison to the current methodologies for arrhythmia classification. The results we achieved by using our multimodel are better than those obtained by using a single model and better than most of the previous detection methods. It is worth mentioning that this model produced accurate classification results on small collection of data. Experts in this field can use this model as a guide to help them make decisions and save time.
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Kozieł, Paweł, Maria Grodkiewicz, Klaudia Artykiewicz, Kamila Gorczyca, Marcin Czarkowski, Aleksandra Słupczyńska, Weronika Urbaś, Klaudia Podgórska, Aleksandra Puła, and Urszula Krzysiek. "Does the watch can detect cardiac arrhythmias?" Journal of Education, Health and Sport 13, no. 2 (January 8, 2023): 293–98. http://dx.doi.org/10.12775/jehs.2023.13.02.042.
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Introduction and purpose:The prevalence of cardiac arrhythmias in the population is not exactly known. Since cardiac arrhythmias are often episodic, they cannot be detected by conventional methods such as electrocardiography (ECG), which takes only a few seconds to record. The purpose of this review is to analyze the latest information regarding the use of smart watches to detect cardiac arrhythmias.Material and methods:This review is based on available data collected in the PubMed database published between 2015 and 2022. The search was performed by browsing keywords such as: "smartwatch", "cardiac arrhythmia", "mHealth", "screening", "wearable devices".Results: The results suggest that the detection of atrial fibrillation (AF) using commercially available smartwatches shows very high diagnostic accuracy. Accuracy in smartwatch arrhythmia detection demonstrated a cumulative sensitivity of 100% (95% CI 1.00-1.00) in 16 studies with 5,050 participants. Sensitivity ranged from 25% (95% CI 0.14-0.36) to 100% (95% CI 1.00-1.00), specificity ranged from 68% (95% CI 0.65 -0.70) to 100% (95% Cl 1.00-1.00). Insufficient quality of the PPG signal resulted in the exclusion of some data in many studies. The analyzes showed no difference in diagnostic accuracy between photoplethysmography (PPG) and single-lead electrocardiography used in these devices.Conclusions: The prevalence of arrhythmias in the form of atrial fibrillation and other forms of arrhythmia in the middle-aged and older population is significant. This review highlights the increasing role of electronic devices in the detection of cardiac arrhythmias. Smartwatches show promising accuracy in detecting arrhythmias, but more research is needed to make this method of arrhythmia recognition a common screening method.
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DeCamilla, J., X. Xia, M. Wang, J. Wade, B. Mykins, W. Zareba, and J. P. Couderc. "The multiple arrhythmia dataset evaluation database (M.A.D.A.E.)." Journal of Electrocardiology 51, no. 6 (November 2018): S106—S112. http://dx.doi.org/10.1016/j.jelectrocard.2018.08.005.
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Linghu, Rongqian, and Ke Zhang. "Real-time Automatic Arrhythmia Detection System based on Extreme Gradient Boosting and Neural Network Algorithm." Journal of Physics: Conference Series 2449, no. 1 (March 1, 2023): 012033. http://dx.doi.org/10.1088/1742-6596/2449/1/012033.
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Abstract Arrhythmia and other diseases are puzzling more and more people. Accurate detection is the key to realizing intelligent diagnosis of electrocardiogram(ECG) monitoring systems. It can prevent heart disease and effectively reduce mortality. An efficient and accurate arrhythmia detection method is urgent. In this work, a real-time automatic arrhythmia detection technology based on extreme gradient boosting (XGboost) and convolutional neural network (CNN) algorithm were developed. First, ECG signals in the MIT-BIH Arrhythmia database are preprocessed: 1) EMG interference filtering; 2) Power frequency interference suppression; 3) Baseline drift correction. Secondly, We use the cyclic singular spectrum (CISSA) algorithm to decompose the ECG after pretreatment. From the original ECG and the 7 simple signals obtained from decomposition, 23 features about the time domain, frequency domain, nonlinear dynamics and statistics are extracted respectively. Finally, XGboost and CNN algorithms are used to build a classification model, and the extracted features are classified, trained and recognized to achieve automatic detection of arrhythmia. The experimental results show that XGboost and CNN algorithms can automatically detect 98.40%, 95.65% and 97.60%, 95.12% of Category 2 and Category 4 arrhythmias, respectively.
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Cintra, Fatima Dumas, Marcia Regina Pinho Makdisse, Wercules Antônio Alves de Oliveira, Camila Furtado Rizzi, Francisco Otávio de Oliveira Luiz, Sergio Tufik, Angelo Amato Vincenzo de Paola, and Dalva Poyares. "Exercise-induced ventricular arrhythmias: analysis of predictive factors in a population with sleep disorders." Einstein (São Paulo) 8, no. 1 (March 2010): 62–67. http://dx.doi.org/10.1590/s1679-45082010ao1469.
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ABSTRACT Objective: To assess the prevalence of ventricular arrhythmias induced by exercise in a population with sleep disorders and to analyze the triggering factors. Methods: Patients were consecutively selected from the database of the Sleep Clinic of Universidade Federal de São Paulo. All subjects were submitted to basal polysomnography, blood sample collection, physical examination, 12-lead ECG, spirometry, cardiorespiratory exercise study on a treadmill, and echocardiogram. The Control Group was matched for age and gender. Results: A total of 312 patients were analyzed. Exercise-induced ventricular arrhythmia was observed in 7%. The aortic diameter was larger (3.44 ± 0.30, 3.16 ± 0.36, p = 0.04) and the minimal saturation was lower (92.75 ± 3.05, 95.50 ± 1.73, p=0.01) in the ventricular arrhythmia group when compared to controls, respectively. After correction of the aortic root to body surface, there was only a trend to a larger diameter being associated with the emergence of arrhythmia. Conclusions: Exercise-induced ventricular arrhythmia was observed in 7% of sample and it was associated with lower oxygen saturation during exercise.
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Liu, Feifei, Chengyu Liu, Xinge Jiang, Zhimin Zhang, Yatao Zhang, Jianqing Li, and Shoushui Wei. "Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases." Journal of Healthcare Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/9050812.
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A systematical evaluation work was performed on ten widely used and high-efficient QRS detection algorithms in this study, aiming at verifying their performances and usefulness in different application situations. Four experiments were carried on six internationally recognized databases. Firstly, in the test of high-quality ECG database versus low-quality ECG database, for high signal quality database, all ten QRS detection algorithms had very high detection accuracy (F1 >99%), whereas the F1 results decrease significantly for the poor signal-quality ECG signals (all <80%). Secondly, in the test of normal ECG database versus arrhythmic ECG database, all ten QRS detection algorithms had good F1 results for these two databases (all >95% except RS slope algorithm with 94.24% on normal ECG database and 94.44% on arrhythmia database). Thirdly, for the paced rhythm ECG database, all ten algorithms were immune to the paced beats (>94%) except the RS slope method, which only output a low F1 result of 78.99%. At last, the detection accuracies had obvious decreases when dealing with the dynamic telehealth ECG signals (all <80%) except OKB algorithm with 80.43%. Furthermore, the time costs from analyzing a 10 s ECG segment were given as the quantitative index of the computational complexity. All ten algorithms had high numerical efficiency (all <4 ms) except RS slope (94.07 ms) and sixth power algorithms (8.25 ms). And OKB algorithm had the highest numerical efficiency (1.54 ms).
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Khalaf, Akram Jaddoa, and Samir Jasim Mohammed. "Verification and comparison of MIT-BIH arrhythmia database based on number of beats." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 4950. http://dx.doi.org/10.11591/ijece.v11i6.pp4950-4961.
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<span lang="EN-US">The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database.</span>
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Lin, Shih-Yi, Wu-Huei Hsu, Cheng-Chieh Lin, Cheng-Li Lin, Chun-Hao Tsai, Chih-Hsueh Lin, Der-Cherng Chen, Tsung-Chih Lin, Chung-Y. Hsu, and Chia-Hung Kao. "Association of Arrhythmia in Patients with Cervical Spondylosis: A Nationwide Population-Based Cohort Study." Journal of Clinical Medicine 7, no. 9 (August 23, 2018): 236. http://dx.doi.org/10.3390/jcm7090236.
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Background: Sympathetic activity, including cervical ganglia, is involved in the development of cardiac arrhythmias. Objective: The present study investigated the association between cervical spondylosis and arrhythmia, which has never been reported before. Methods: Patients newly diagnosed with cervical spondylosis (CS) with an index date between 2000 and 2011 were identified from the National Health Insurance Research Database. We performed a 1:1 case-control matched analysis. Cases were matched to controls according to their estimated propensity scores, based on demographics and existing risk factors. Cox proportional hazard models were applied to assess the association between CS and arrhythmia. Results: The CS cohort comprised 22,236 patients (males, 42.6%; mean age, 54.4 years) and non-CS cohort comprised 22,236 matched controls. There were 1441 events of arrhythmia in CS cohort and 537 events of arrhythmia in non-CS cohort, which 252 and 127 events of atrial fibrillation in CS and non-CS cohort, 33 and 12 events of ventricular tachycardia in CS cohort and non-CS cohort, 78 and 35 events of supraventricular tachycardia in CS cohort and non-CS cohort. The CS cohort had an arrhythmia incidence of 11.1 per 1000 person-years and a higher risk [adjusted hazard ratio (aHR) = 3.10, 95% confidence interval (CI) = 2.80–3.42] of arrhythmia, 2.54-fold aHR of ventricular tachycardia (95% CI = 1.70–3.79), and 2.22-fold aHR of atrial fibrillation (95% CI = 1.79–2.76) compared with non-CS cohort. Conclusions: Cervical spondylosis is associated with a higher risk of arrhythmia.
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Abdou, Abdoul-Dalibou, Ndeye Fatou Ngom, and Oumar Niang. "Arrhythmias Prediction Using an Hybrid Model Based on Convolutional Neural Network and Nonlinear Regression." International Journal of Computational Intelligence and Applications 19, no. 03 (September 2020): 2050024. http://dx.doi.org/10.1142/s1469026820500248.
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In biomedical signal processing, artificial intelligence techniques are used for identifying and extracting relevant information. However, it lacks effective solutions based on machine learning for the prediction of cardiac arrhythmias. The heart diseases diagnosis rests essentially on the analysis of various properties of ECG signal. The arrhythmia is one of the most common heart diseases. A cardiac arrhythmia is a disturbance of the heart rhythm. It occurs when the heart beats too slowly, too fast or anarchically, with no apparent cause. The diagnosis of cardiac arrhythmias is based on the analysis of the ECG properties, especially, the durations (P, QRS, T), the amplitudes (P, Q, R, S, T), the intervals (PQ, QT, RR), the cardiac frequency and the rhythm. In this paper we propose a system of arrhythmias diagnosis assistance based on the analysis of the temporal and frequential properties of the ECG signal. After the features extraction step, the ECG properties are then used as input for a convolutional neural network to detect and classify the arrhythmias. Finally, the classification results are used to perform a prediction of arrhythmias with nonlinear regression model. The method is illustrated using the MIT-BIH database.
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Bae, Tae Wuk, Sang Hag Lee, and Kee Koo Kwon. "An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis." Sensors 20, no. 21 (October 29, 2020): 6144. http://dx.doi.org/10.3390/s20216144.
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With the advancement of the Internet of Medical Things technology, many vital sign-sensing devices are being developed. Among the diverse healthcare devices, portable electrocardiogram (ECG) measuring devices are being developed most actively with the recent development of sensor technology. These ECG measuring devices use different sampling rates according to the hardware conditions, which is the first variable to consider in the development of ECG analysis technology. Herein, we propose an R-point detection method using an adaptive median filter based on the sampling rate and analyze major arrhythmias using the signal characteristics. First, the sliding window and median filter size are determined according to the set sampling rate, and a wider median filter is applied to the QRS section with high variance within the sliding window. Then, the R point is detected by subtracting the filtered signal from the original signal. Methods for detecting major arrhythmias using the detected R point are proposed. Different types of ECG signals were used for a simulation, including ECG signals from the MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, signals generated by a simulator, and actual measured signals with different sampling rates. The experimental results indicated the effectiveness of the proposed R-point detection method and arrhythmia analysis technique.
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Moody, G. B., and R. G. Mark. "The impact of the MIT-BIH Arrhythmia Database." IEEE Engineering in Medicine and Biology Magazine 20, no. 3 (2001): 45–50. http://dx.doi.org/10.1109/51.932724.
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Shen, Qin, Hongxiang Gao, Yuwen Li, Qi Sun, Minglong Chen, Jianqing Li, and Chengyu Liu. "An Open-Access Arrhythmia Database of Wearable Electrocardiogram." Journal of Medical and Biological Engineering 40, no. 4 (July 22, 2020): 564–74. http://dx.doi.org/10.1007/s40846-020-00554-3.
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kamil, Sarah, and Lamia Muhammed. "Arrhythmia Classification Using One Dimensional Conventional Neural Network." International Journal of Advances in Soft Computing and its Applications 13, no. 3 (November 28, 2021): 43–58. http://dx.doi.org/10.15849/ijasca.211128.04.
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Arrhythmia is a heart condition that occurs due to abnormalities in the heartbeat, which means that the heart's electrical signals do not work properly, resulting in an irregular heartbeat or rhythm and thus defeating the pumping of blood. Some cases of arrhythmia are not considered serious, while others are very dangerous, life-threatening, and cause death in a short period of time. In the clinical routine, cardiac arrhythmia detection is performed by electrocardiogram (ECG) signals. The ECG is a significant diagnosis tool that is used to record the electrical activity of the heart, and its signals can reveal abnormal heart activity. However, because of their small amplitude and duration, visual interpretation of ECG signals is difficult. As a result, we present a significant approach for identifying arrhythmias using ECG signals. In this study, we proposed an approach based on Deep Learning (DL) technology that is a framework of nine-layer one-dimension Conventional Neural Network (1D CNN) for classifying automatically ECG signals into four cardiac conditions named: normal (N), Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The practical test of this work was executed with the benchmark MIT-BIH database. We achieved an average accuracy of 99%, precision of 98%, recall of 96.5%, specificity of 99.08%, and an F1-score of 95.75%. The obtained results were compared with some relevant models, and they showed that the proposed framework outperformed those models in some measures. The new approach’s performance indicates its success. Also, it has been shown that deep convolutional neural networks can be used efficiently in automated detection and, therefore, cardiovascular disease protection as well as help cardiologists in medical practice by saving time and effort. Keywords: 1-D CNN, Arrhythmia, Cardiovascular Disease, Classification, Deep learning, Electrocardiogram(ECG), MIT-BIH arrhythmia database.
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Ma, Shuai, Jianfeng Cui, Weidong Xiao, and Lijuan Liu. "Deep Learning-Based Data Augmentation and Model Fusion for Automatic Arrhythmia Identification and Classification Algorithms." Computational Intelligence and Neuroscience 2022 (August 11, 2022): 1–17. http://dx.doi.org/10.1155/2022/1577778.
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Automated ECG-based arrhythmia detection is critical for early cardiac disease prevention and diagnosis. Recently, deep learning algorithms have been widely applied for arrhythmia detection with great success. However, the lack of labeled ECG data and low classification accuracy can have a significant impact on the overall effectiveness of a classification algorithm. In order to better apply deep learning methods to arrhythmia classification, in this study, feature extraction and classification strategy based on generative adversarial network data augmentation and model fusion are proposed to address these problems. First, the arrhythmia sparse data is augmented by generative adversarial networks. Then, aiming at the identification of different types of arrhythmias in long-term ECG, a spatial information fusion model based on ResNet and a temporal information fusion model based on BiLSTM are proposed. The model effectively fuses the location information of the nearest neighbors through the local feature extraction part of the generated ECG feature map and obtains the correlation of the global features by autonomous learning in multiple spaces through the BiLSTM network in the part of the global feature extraction. In addition, an attention mechanism is introduced to enhance the features of arrhythmia-type signal segments, and this mechanism can effectively focus on the extraction of key information to form a feature vector for final classification. Finally, it is validated by the enhanced MIT-BIH arrhythmia database. The experimental results demonstrate that the proposed classification technique enhances arrhythmia diagnostic accuracy by 99.4%, and the algorithm has high recognition performance and clinical value.
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Qin, Qin, Jianqing Li, Yinggao Yue, and Chengyu Liu. "An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm." Journal of Healthcare Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/5980541.
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R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.
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Hamarsheh, Qadri. "Autoregressive Modeling based ECG Cardiac Arrhythmias’ Database System." International Journal of Circuits, Systems and Signal Processing 16 (July 26, 2022): 1074–83. http://dx.doi.org/10.46300/9106.2022.16.130.
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This article proposes an ECG (electrocardiography) database system based on linear filtering, wavelet transform, PSD analysis, and adaptive AR modeling technologies to distinguish 19 ECG beat types for classification. This paper uses the Savitzky-Golay filter and wavelet transform for noise reduction, and wavelet analysis and AR modeling techniques for feature extraction to design a database system of AR coefficients describing the ECG signals with different arrhythmia types. In the experimental part of this work, the proposed algorithm performance is evaluated using an ECG dataset containing 19 different types including normal sinus rhythm, atrial premature contraction, ventricular premature contraction, ventricular tachycardia, ventricular fibrillation, supraventricular tachycardia, and other types from the MIT-BIH Arrhythmia Database. The simulation is performed in a MATLAB environment.
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Chintalapati, Usha Kumari, Md Aqeel Manzar, Tarun Varma N, Reethika A, Priya Samhitha B, Rohitha Sivani J, Kamran Ali Mirza, and Pranav Kumar S. "Automated Detection of Depolarization and Repolarization of Cardiac Signal for Arrhythmia Classification." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 02 (February 12, 2021): 173. http://dx.doi.org/10.3991/ijoe.v17i02.18955.
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Irregular heartbeat results in heart diseases. Cardiac deaths are most seen across the globe. Detecting the heart problems in early stage can reduce the death rate. Electrocardiogram (ECG) is one of the most popular method for diagnosing different arrhythmias. Arrhythmia means irregular activity of heart or abnormal heart rhythm. In this paper, cardiac signal peaks P-wave, QRS complex and T-wave are detected for classifying the type of arrhythmia. These are the main components of ECG signal. P-wave is of very small duration, it is ex- plains about the atrial depolarization. The QRS complex may include combination of Q-wave, R-wave, and S-wave. But every QRS complex may not contain Q-R-S waves. It explains about ventricular depolarization. Whereas T wave is about ventricular re-polarization. S-Golay filter is used for denoising. This is used for smoothing the data which thereby, increases the precision of data without distortion of signal tendency. The patient data is collected from MIT-BIH Arrhythmia database for analysis. The simulation is done in Matlab software
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Mathunjwa, Bhekumuzi M., Yin-Tsong Lin, Chien-Hung Lin, Maysam F. Abbod, Muammar Sadrawi, and Jiann-Shing Shieh. "ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features." Sensors 22, no. 4 (February 20, 2022): 1660. http://dx.doi.org/10.3390/s22041660.
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In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.
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Anwar, Syed Muhammad, Maheen Gul, Muhammad Majid, and Majdi Alnowami. "Arrhythmia Classification of ECG Signals Using Hybrid Features." Computational and Mathematical Methods in Medicine 2018 (November 12, 2018): 1–8. http://dx.doi.org/10.1155/2018/1380348.
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Automatic detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiac conditions. In this paper, a novel method for classification of various types of arrhythmia using morphological and dynamic features is presented. Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features. It provides better time and frequency resolution of the electrocardiogram (ECG) signal, which helps in decoding important information of a quasiperiodic ECG using variable window sizes. RR interval information is used as a dynamic feature. The nonlinear dynamics of RR interval are captured using Teager energy operator, which improves the arrhythmia classification. Moreover, to remove redundancy, DWT subbands are subjected to dimensionality reduction using independent component analysis, and a total of twelve coefficients are selected as morphological features. These hybrid features are combined and fed to a neural network to classify arrhythmia. The proposed algorithm has been tested over MIT-BIH arrhythmia database using 13724 beats and MIT-BIH supraventricular arrhythmia database using 22151 beats. The proposed methodology resulted in an improved average accuracy of 99.75% and 99.84% for class- and subject-oriented scheme, respectively, using three-fold cross validation.
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ZHANG, JIA-WEI, XIA LIU, and JUN DONG. "CCDD: AN ENHANCED STANDARD ECG DATABASE WITH ITS MANAGEMENT AND ANNOTATION TOOLS." International Journal on Artificial Intelligence Tools 21, no. 05 (October 2012): 1240020. http://dx.doi.org/10.1142/s0218213012400209.
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Standard Electrocardiogram (ECG) database is created for validating and comparing different algorithms on feature detection and disease classification. At present, there are four frequently used standard databases: MIT-BIH arrhythmia database, QT database, CSE multi-lead database and AHA database. With the development in equipment and diagnosis approach, severe deficiencies are discovered and a new modern ECG database is needed for further research. So Chinese Cardiovascular Disease Database (CCDD or CCD database), which contains 12-Lead ECG data, detailed annotation features and beat diagnosis result is proposed. It is advanced for not only improving the raw ECG data's technical parameters, but also introducing valuable morphology features which are utilized by experienced cardiologists effectively. CCDD is employed by our group as well as aiming for supporting other research groups that work in automated ECG analysis.
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Soni, Ekta, Arpita Nagpal, Puneet Garg, and Plácido Rogerio Pinheiro. "Assessment of Compressed and Decompressed ECG Databases for Telecardiology Applying a Convolution Neural Network." Electronics 11, no. 17 (August 29, 2022): 2708. http://dx.doi.org/10.3390/electronics11172708.
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Incalculable numbers of patients in hospitals as a result of COVID-19 made the screening of heart patients arduous. Patients who need regular heart monitoring were affected the most. Telecardiology is used for regular remote heart monitoring of such patients. However, the resultant huge electrocardiogram (ECG) data obtained through regular monitoring affects available storage space and transmission bandwidth. These signals can take less space if stored or sent in a compressed form. To recover them at the receiver end, they are decompressed. We have combined telecardiology with automatic ECG arrhythmia classification using CNN and proposed an algorithm named TELecardiology using a Deep Convolution Neural Network (TELDCNN). Discrete cosine transform (DCT), 16-bit quantization, and run length encoding (RLE) were used for compression, and a convolution neural network (CNN) was applied for classification. The database was formed by combining real-time signals (taken from a designed ECG device) with an online database from Physionet. Four kinds of databases were considered and classified. The attained compression ratio was 2.56, and the classification accuracies for compressed and decompressed databases were 0.966 and 0.990, respectively. Comparing the classification performance of compressed and decompressed databases shows that the decompressed signals can classify the arrhythmias more appropriately than their compressed-only form, although at the cost of increased computational time.
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Zhou, Haiying, Xiancheng Zhu, Sishan Wang, Kui Zhou, Zheng Ma, Jian Li, Kun-Mean Hou, and Christophe De Vaulx. "A Novel Cardiac Arrhythmias Detection Approach for Real-Time Ambulatory ECG Diagnosis." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 10 (March 9, 2017): 1758004. http://dx.doi.org/10.1142/s0218001417580046.
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In view of requirements of low-resource consumption and high-efficiency in real-time Ambulatory Electrocardiograph Diagnosis (AED) applications, a novel Cardiac Arrhythmias Detection (CAD) algorithm is proposed. This algorithm consists of three core modules: an automatic-learning machine that models diagnostic criteria and grades the emergency events of cardiac arrhythmias by studying morphological characteristics of ECG signals and experiential knowledge of cardiologists; a rhythm classifier that recognizes and classifies heart rhythms basing on statistical features comparison and linear discriminant with confidence interval estimation; and an arrhythmias interpreter that assesses emergency events of cardia arrhythmias basing on a two rule-relative interpretation mechanisms. The experiential results on off-line MIT-BIH cardiac arrhythmia database as well as online clinical testing explore that this algorithm has 92.8% sensitivity and 97.5% specificity in average, so that it is suitable for real-time cardiac arrhythmias monitoring.
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Shadhon Chandra Mohonta and Md. Firoj Ali. "A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network." MIST INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY 10 (December 29, 2022): 37–41. http://dx.doi.org/10.47981/j.mijst.10(03)2022.341(37-41).
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Electrocardiogram (ECG) signal is informative as well as non-invasive clinical tool to diagnose cardiac diseases of human heart. However, the diagnosis requires professionals’ clarification and is also time-consuming. To make the diagnosis proficient, a novel convolutional neural network (CNN) has been proposed for automatic arrhythmia detection. In this work, the ECG data collected from the MIT-BIH database have been preprocessed, and segmented in short ECG segments of 60 s. Then, all these segments have been transformed into scalogram images obtained from time-frequency analysis using continuous wavelet transform (CWT). Finally, these scalogram images have been exploited as an input for our designed CNN classifier to classify cardiac arrhythmia. In this approach, the overall accuracy, sensitivity, and specificity are 99.39%, 98.79%, and 100% respectively. Proposed CNN model has significant advantages, and it can be used to differentiate the healthy and arrhythmic patients effectively.
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Qi, Meng, Hongxiang Shao, Nianfeng Shi, Guoqiang Wang, and Yifei Lv. "Arrhythmia classification detection based on multiple electrocardiograms databases." PLOS ONE 18, no. 9 (September 27, 2023): e0290995. http://dx.doi.org/10.1371/journal.pone.0290995.
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According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.
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Yan, Wei, and Zhen Zhang. "Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database." Journal of Healthcare Engineering 2021 (December 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/1819112.
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Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.
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Meng, Yang, Guoxin Liang, and Mei Yue. "Deep Learning-Based Arrhythmia Detection in Electrocardiograph." Scientific Programming 2021 (May 13, 2021): 1–7. http://dx.doi.org/10.1155/2021/9926769.
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This study aimed to explore the application of electrocardiograph (ECG) in the diagnosis of arrhythmia based on the deep convolutional neural network (DCNN). ECG was classified and recognized with the DCNN. The specificity (Spe), sensitivity (Sen), accuracy (Acc), and area under curve (AUC) of the DCNN were evaluated in the Chinese Cardiovascular Disease Database (CCDD) and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, respectively. The results showed that in the CCDD, the original model tested by the small sample set had an accuracy (Acc) of 82.78% and AUC of 0.882, while the Acc and AUC of the translated model were 85.69% and 0.893, respectively, so the difference was notable ( P < 0.05); the Acc of the original model and the translated model was 80.12% and 82.63%, respectively, in the large sample set, so the difference was obvious ( P < 0.05). In the MIT-BIH database, the Acc of normal (N) heart beat (HB) (99.38%) was higher than that of the atrial premature beat (APB) (87.45%) ( P < 0.05). In a word, applying the DCNN could improve the Acc of ECG for classification and recognition, so it could be well applied to ECG signal classification.
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Wang, Liang-Hung, Ze-Hong Yan, Yi-Ting Yang, Jun-Ying Chen, Tao Yang, I.-Chun Kuo, Patricia Angela R. Abu, Pao-Cheng Huang, Chiung-An Chen, and Shih-Lun Chen. "A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia." Sensors 21, no. 15 (August 1, 2021): 5222. http://dx.doi.org/10.3390/s21155222.
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Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.
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Xiao, Qiao, Khuan Lee, Siti Aisah Mokhtar, Iskasymar Ismail, Ahmad Luqman bin Md Pauzi, Qiuxia Zhang, and Poh Ying Lim. "Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review." Applied Sciences 13, no. 8 (April 14, 2023): 4964. http://dx.doi.org/10.3390/app13084964.
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Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.
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MANDAL, SAURAV, and NABANITA SINHA. "ARRHYTHMIA DIAGNOSIS FROM ECG SIGNAL ANALYSIS USING STATISTICAL FEATURES AND NOVEL CLASSIFICATION METHOD." Journal of Mechanics in Medicine and Biology 21, no. 03 (March 18, 2021): 2150025. http://dx.doi.org/10.1142/s0219519421500251.
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This study aims to present an efficient model for autodetection of cardiac arrhythmia by the diagnosis of self-affinity and identification of governing processes of a number of Electrocardiogram (ECG) signals taken from MIT-BIH database. In this work, the proposed model includes statistical methods to find the diagnosis pattern for detecting cardiac abnormalities which is useful for the computer aided system for arrhythmia detection. First, the Rescale Range (R/S) analysis has been employed for ECG signals to understand the scaling property of ECG signals. The value of Hurst exponent identifies the presence of abnormality in ECG signals taken for consideration with 92.58% accuracy. In this study, Higuchi method which deals with unifractality or monofractality of signals has been applied and it is found that unifractality is sufficient to detect arrhythmia with 91.61% accuracy. The Multifractal Detrended Fluctuation Analysis (MFDFA) has been used over the present signals to identify and confirm the multifractality. The nature of multifractality is different for arrhythmia patients and normal heart condition. The multifractal analysis is useful to detect abnormalities with 93.75% accuracy. Finally, the autocorrelation analysis has been used to identify the prevalent governing process in the present arrhythmic ECG signals and study confirms that all the signals are governed by stationary autoregressive methods of certain orders. In order to increase the overall efficiency, this present model deals with analyzing all the statistical features extracted from different statistical techniques for a large number of ECG signals of normal and abnormal heart condition. Finally, the result of present analysis altogether possibly indicates that the proposed model is efficient to detect cardiac arrhythmia with 99.3% accuracy.
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Sarshar, Nazanin Tataei, and Mohammad Mirzaei. "Premature Ventricular Contraction Recognition Based on a Deep Learning Approach." Journal of Healthcare Engineering 2022 (March 26, 2022): 1–7. http://dx.doi.org/10.1155/2022/1450723.
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Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose heart diseases. An ECG signal allows the demonstration of the cyclical contraction and relaxation of human heart muscles. This signal is a primary and noninvasive tool employed to recognize the actual life threat related to the heart. Abnormal ECG heartbeat and arrhythmia are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is one of the most common arrhythmias which begins from the lower chamber of the heart and can cause cardiac arrest, palpitation, and other symptoms affecting all activities of a patient. Nowadays, computer-assisted techniques reduce doctors' burden to assess heart arrhythmia and heart disease automatically. In this study, we propose a PVC recognition based on a deep learning approach using the MIT-BIH arrhythmia database. Firstly, 10 heartbeat and statistical features including three morphological features (RS amplitude, QR amplitude, and QRS width) and seven statistical features are computed for each signal. The extraction process of these features is conducted for 20 s of ECG data that create a feature vector. Next, these features are fed into a convolutional neural network (CNN) to find unique patterns and classify them more effectively. The obtained results prove that our pipeline improves the diagnosis performance more effectively.
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Ullah, Wusat, Imran Siddique, Rana Muhammad Zulqarnain, Mohammad Mahtab Alam, Irfan Ahmad, and Usman Ahmad Raza. "Classification of Arrhythmia in Heartbeat Detection Using Deep Learning." Computational Intelligence and Neuroscience 2021 (October 19, 2021): 1–13. http://dx.doi.org/10.1155/2021/2195922.
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The electrocardiogram (ECG) is one of the most widely used diagnostic instruments in medicine and healthcare. Deep learning methods have shown promise in healthcare prediction challenges involving ECG data. This paper aims to apply deep learning techniques on the publicly available dataset to classify arrhythmia. We have used two kinds of the dataset in our research paper. One dataset is the MIT-BIH arrhythmia database, with a sampling frequency of 125 Hz with 1,09,446 ECG beats. The classes included in this first dataset are N, S, V, F, and Q. The second database is PTB Diagnostic ECG Database. The second database has two classes. The techniques used in these two datasets are the CNN model, CNN + LSTM, and CNN + LSTM + Attention Model. 80% of the data is used for the training, and the remaining 20% is used for testing. The result achieved by using these three techniques shows the accuracy of 99.12% for the CNN model, 99.3% for CNN + LSTM, and 99.29% for CNN + LSTM + Attention Model.
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Ben Itzhak, Sagi, Shir Sharony Ricon, Shany Biton, Joachim A. Behar, and Jonathan A. Sobel. "Effect of temporal resolution on the detection of cardiac arrhythmias using HRV features and machine learning." Physiological Measurement 43, no. 4 (April 28, 2022): 045002. http://dx.doi.org/10.1088/1361-6579/ac6561.
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Abstract Objective. Arrhythmia is an abnormal cardiac rhythm that affects the pattern and rate of the heartbeat. Wearable devices with the functionality to measure and store heart rate (HR) data are growing in popularity and enable diagnosing and monitoring arrhythmia on a large scale. The typical sampling resolution of HR data available from non-medical grade wearable devices varies from seconds to several minutes depending on the device and its settings. However, the impact of sampling resolution on the performance and quality of arrhythmia detection has not yet been quantified. Approach. In this study, we investigated the detection and classification of three arrhythmias, namely atrial fibrillation, bradycardia, tachycardia, from down-sampled HR data with various temporal resolution (5-, 15-, 30- and 60 s averages) in 1 h segments extracted from an annotated Holter ECG database acquired at the University of Virginia Heart Station. For the classification task, a total of 15 common heart rate variability (HRV) features were engineered based on the HR time series of each patient. Three different types of machine learning classifiers were evaluated, namely logistic regression, support vector machine and random forest. Main results. A decrease in temporal resolution drastically impacted the detection of atrial fibrillation but did not substantially affect the detection of bradycardia and tachycardia. A HR resolution up to 15 s average demonstrated reasonable performance with a sensitivity of 0.92 and a specificity of 0.86 for a multiclass random forest classifier. Significance. HRV features extracted from low resolution long HR recordings have the potential to increase the early detection of arrhythmias in undiagnosed individuals.
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Willy, Kevin, Julia Köbe, Florian Reinke, Benjamin Rath, Christian Ellermann, Julian Wolfes, Felix K. Wegner, et al. "Usefulness of the MADIT-ICD Benefit Score in a Large Mixed Patient Cohort of Primary Prevention of Sudden Cardiac Death." Journal of Personalized Medicine 12, no. 8 (July 28, 2022): 1240. http://dx.doi.org/10.3390/jpm12081240.
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Background: Decision-making in primary prevention is not always trivial and many clinical scenarios are not reflected in current guidelines. To help evaluate a patient’s individual risk, a new score to predict the benefit of an implantable defibrillator (ICD) for primary prevention, the MADIT-ICD benefit score, has recently been proposed. The score tries to predict occurrence of ventricular arrhythmias and non-arrhythmic death based on data from four previous MADIT trials. We aimed at examining its usefulness in a large single-center register of S-ICD patients with various underlying cardiomyopathies. Methods and results: All S-ICD patients with a primary preventive indication for ICD implantation from our large single-center database were included in the analysis (n = 173). During a follow-up of 1227 ± 978 days, 27 patients developed sustained ventricular arrhythmias, while 6 patients died for non-arrhythmic reasons. There was a significant correlation for patients with ischemic cardiomyopathy (ICM) (n = 29, p = 0.04) to the occurrence of ventricular arrhythmia. However, the occurrence of ventricular arrhythmias could not sufficiently be predicted by the MADIT-ICD VT/VF score (p = 0.3) in patients with (n = 142, p = 0.19) as well as patients without structural heart disease (n = 31, p = 0.88) and patients with LV-EF < 35%. Of the risk factors included in the risk score calculation, only non-sustained ventricular tachycardias were significantly associated with sustained ventricular arrhythmias (p = 0.02). Of note, non-arrhythmic death could effectively be predicted by the proposed non-arrhythmic mortality score as part of the benefit score (p = 0.001, r = 0.3) also mainly driven by ICM patients. Age, diabetes mellitus, and a BMI < 23 kg/m2 were key predictors of non-arrhythmic death implemented in the score. Conclusion: The MADIT-ICD benefit score adds a new option to evaluate expected benefit of ICD implantation for primary prevention. In a large S-ICD cohort of primary prevention, the value of the score was limited to patients with ischemic cardiomyopathy. Future research should evaluate the performance of the score in different subgroups and compare it to other risk scores to assess its value for daily clinical practice.
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Yang, Xiong, Xin Yu Jin, and Jian Feng Shen. "A PVC Identification Method of ECG Signal Based on Improved BPNN." Applied Mechanics and Materials 738-739 (March 2015): 578–81. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.578.
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Computer-aided diagnosis of Premature Ventricular Contraction (PVC) plays an important role in timely detection and treatment of arrhythmias. Conventional identification methods based on back propagation neural network (BPNN) get problems of overlong training time and local optimum. This paper proposes an application of improved BPNN on PVC identification and the improvements of BPNN are based on self-adaptive learning rate and momentum in training. Denoising and feature extraction of ECG signal obtained from MIT-BIH arrhythmia database are processed first. A comparison between standard BPNN and improved BPNN shows that the latter gets less training time and better accuracy.
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Ilbeigipour, Sadegh, Amir Albadvi, and Elham Akhondzadeh Noughabi. "Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming." Journal of Healthcare Engineering 2021 (April 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/6624829.
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One of the major causes of death in the world is cardiac arrhythmias. In the field of healthcare, physicians use the patient’s electrocardiogram (ECG) records to detect arrhythmias, which indicate the electrical activity of the patient’s heart. The problem is that the symptoms do not always appear and the physician may be mistaken in the diagnosis. Therefore, patients need continuous monitoring through real-time ECG analysis to detect arrhythmias in a timely manner and prevent an eventual incident that threatens the patient’s life. In this research, we used the Structured Streaming module built top on the open-source Apache Spark platform for the first time to implement a machine learning pipeline for real-time cardiac arrhythmias detection and evaluate the impact of using this new module on classification performance metrics and the rate of delay in arrhythmia detection. The ECG data collected from the MIT/BIH database for the detection of three class labels: normal beats, RBBB, and atrial fibrillation arrhythmias. We also developed three decision trees, random forest, and logistic regression multiclass classifiers for data classification where the random forest classifier showed better performance in classification than the other two classifiers. The results show previous results in performance metrics of the classification model and a significant decrease in pipeline runtime by using more class labels compared to previous studies.
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Tejedor, Javier, David G. Marquez, Constantino A. Garcia, and Abraham Otero. "A Tandem Feature Extraction Approach for Arrhythmia Identification." Electronics 10, no. 8 (April 19, 2021): 976. http://dx.doi.org/10.3390/electronics10080976.
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Heart disease is currently the leading cause of death in the world. The electrocardiogram (ECG) is the recording of the electrical activity generated by the heart. Its low cost and simplicity have made it an essential test for monitoring heart disease, especially for the identification of arrhythmias. With the advances in electronic technology, there are nowadays sensors that enable the recording of the ECG during the daily life of the patient and its wireless transmission to healthcare facilities. This type of information has a great potential to detect cardiac diseases in their early stages and to permit early interventions before the patient’s health deteriorates. However, to usefully exploit the large volume of information obtained from ambulatory ECG, pattern recognition techniques that are capable of automatically analyzing it are required. Tandem feature extraction techniques have proven to be useful for the processing of physiological parameters such as the electroencephalogram (EEG) and speech. However, to the best of our knowledge, they have never been applied to the ECG. In this paper, the utility of tandem feature extraction for the identification of arrhythmias is studied. The coefficients of a regression using Hermite functions are used to create a feature vector that represents the heartbeat. A multiple-layer perceptron (MLP) is trained using these features and its posterior probability outputs are used to extend the original feature vector. Finally, a Gaussian mixture model (GMM) is trained on the extended feature vectors, which is then used in a GMM-based arrhythmia identification system. This approach has been validated using the MIT-BIH Arrhythmia database. The accuracy of the Gaussian mixture model increased by 15.8% when applied over the extended feature vectors, compared to its application over the original feature vectors, showing the potential of tandem feature extraction for ECG analysis and arrhythmia identification.
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SATHYAMANGALAM NATARAJAN, SHIVAPPRIYA, ARUN KUMAR SHANMUGAM, JUDE HEMANTH DURAISAMY, and HARIKUMAR RAJAGURU. "PREDICTION OF CARDIAC ARRHYTHMIA USING MULTI CLASS CLASSIFIERS BY INCORPORATING WAVELET TRANSFORM BASED FEATURES." DYNA 97, no. 4 (July 1, 2022): 418–24. http://dx.doi.org/10.6036/10458.
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Timely diagnosis and earlier detection of the dangerous heart conditions will reduce the mortality rate and save life of the patient. For that, it is necessary to automate the classi?cation and prediction of Cardiac Arrhythmia. Raw ECG signal is extracted from the MIT-BIH Arrhythmia database, followed by preprocessing and feature extraction using wavelet transform method. Further the extracted features are used for the classification of four different cardiac arrhythmias such as Bradycardia, Tachycardia, Left and Right Bundle Branch Block. Comparative study on the five different classifiers namely Decision trees, Support Vector Machine (SVM), Discriminant Analysis, k-Nearest Neighbor Classifiers (KNN), Ensemble Classifiers, and its variants are experimented in the proposed work. Among these, the weighted KNN classifier gives higher accuracy (90.3%) and prediction speed (10,000 observations per second) with reduced training time (4.329 seconds), compared with the existing state of the art methods. The prediction speed is 10,000 numbers of observations per second which identifies the heart problem earlier, and so appropriate treatment can be given to the patient. To further improve the classification accuracy, three optimizable classifiers namely Optimizable KNN, optimizable SVM, optimizable ensemble are used for the hyper parameter tunning and weight optimization. The optimizable SVM provides better perform (accuracy 93.4 %) among the three optimizable classifiers as well as the existing state of the art works. Therefore, the proposed work used for earlier Cardiac arrhythmia disease diagnosis and prognosis. Keywords: ECG, Cardiac Arrhythmia, Wavelet Transform, Multi class Classifiers, Decision trees, Support Vector Machine (SVM), Discriminant Analysis, k-Nearest Neighbor Classifiers, Ensemble Classifiers, Optimizable classifier.