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Journal articles on the topic 'ECG BEATS'
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Jonkman, M., F. de Boer, and A. Matsuyama. "Improved ECG Signal Analysis Using Wavelet and Feature Extraction." Methods of Information in Medicine 46, no. 02 (2007): 227–30. http://dx.doi.org/10.1055/s-0038-1625412.
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Summary Objectives : Automatic detection of arrhythmias is important for diagnosis of heart problems. However, in ECG signals, there is significant variation of waveforms in both normal and abnormal beats. It is this phenomenon, which makes it difficult to analyse ECG signals. The aim of developing methodology is to distinguish between normal beats and abnormal beats in an ECG signal. Methods : ECG signals were first decomposed using wavelet transform. The feature vectors were then extracted from these decomposed signals as normalised energy and entropy. To improve the classification of the feature vectors of normal and abnormal beats, the normal beats which occur before and after the abnormal beats were eliminated from the group of normal beats. Results : With our proposed methods, the normal beats and abnormal beats formed different clusters of vector points. By eliminating normal beats which occur before and after the abnormal beats, the clusters of different types of beats showed more apparent separation. Conclusions : The combination of wavelet decomposition and the classification using feature vectors of the beats in ECG signals separate abnormal beats from normal beats. The elimination of the normal beats which occur before and after the abnormal beats succeeded in minimising the size of normal beats cluster.
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Yang, Lulu, Junjiang Zhu, Tianhong Yan, Zhaoyang Wang, and Shangshi Wu. "A Modified Convolutional Neural Network for ECG Beat Classification." Journal of Medical Imaging and Health Informatics 10, no. 3 (March 1, 2020): 654–60. http://dx.doi.org/10.1166/jmihi.2020.2913.
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Most convolutional neural networks (CNNs) used to classify electrocardiogram (ECG) beats tend to focus only on the beat, ignoring its relationships with its surrounding beats. This study aimed to propose a hybrid convolutional neural network (HCNN) structure, which classified ECG beats based on the beat's morphology and relationship such as RR intervals. The difference between the HCNN and the traditional CNN lies in the fact that the relationship can be added to any layer in the former. The HCNN was fed with RR intervals at 3 different positions, trained using data from 2170 patients. It was then evaluated with labeled clinical data from 2102 patients to classify ECG beats into premature ventricular contraction beat, atrial premature contraction beat (APC), left bundle branch block beat, right bundle branch block beat, and normal sinus beat. The results showed that the performance of the proposed HCNN method (with an average score of 86.61% on 12 leads) was 3.31% higher than that of the traditional CNN (83.30%) on the test set. In particular, the APC improved most significantly from 57.67% to 76.92% in terms of sensitivity and from 58.80% to 78.46% in terms of the positive predictive value in lead V1.
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Sudha, Mrs Ch. "Classification of Heartbeats Using Convolutional Neural Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4000–4003. http://dx.doi.org/10.22214/ijraset.2023.54351.
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Abstract: When there is a suspicion of a heart attack, an electrocardiogram (ECG) is a crucial test. It measures the electrical activity of the heart, which is manifested through small electric impulses when the heart beats. The subsequent process of analyzing ECG patterns is time-consuming but vital in determining the likelihood of cardiovascular disease by medical professionals. This project utilizes ECG image data to automate the interpretation of ECG recordings, aiming to assist clinicians in detecting life-threatening Myocardial Infarction. By taking an ECG image as input, the system classifies and attempts to categorize the final result into five classes: Non-Ectopic Beats, Supraventricular Ectopic Beats, Ventricular Ectopic Beats, Fusion Beats, and Unknown Beats.
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YAN, JINGYU, YAN LU, YANGSHENG XU, JIA LIU, and XINYU WU. "INTELLIGENT DIAGNOSIS OF CARDIOVASCULAR DISEASES UTILIZING ECG SIGNALS." International Journal of Information Acquisition 07, no. 02 (June 2010): 81–97. http://dx.doi.org/10.1142/s0219878910002087.
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Early automatic detection of cardiovascular diseases is of great importance to provide timely treatment and reduce fatality rate. Although many efforts have been devoted to detecting various arrhythmias, classification of other common cardiovascular diseases still lacks comprehensive and intensive studies. This work aims at developing an automatic diagnosis system for myocardial infarction, valvular heart disease, cardiomyopathy, hypertrophy, and bundle branch block, based on the clinic recordings provided by PTB Database. The proposed diagnosis system consists of the components as baseline wander reduction, beat segmentation, feature extraction, feature reduction and classification. The selected features are the location, amplitude and width of each wave, exactly the parameters of ECG dynamical model. We also propose a mean shift algorithm based method to extract these features. To demonstrate the availability and efficacy of the proposed system, we use a total of 13,564 beats to conduct a large scale experiment, where only 25% beats are utilized to train the eigenvectors of generalized discriminant analysis in the feature reduction phase and 25% beats are applied to train the support vector machine in the classification phase. The average sensitivity, specificity and positive predicitivity for the test set, containing 75% beats, are respectively 96.06%, 99.32% and 97.29%.
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Wen, Cheng, Chih-Hung Huang, and Ming-Feng Yeh. "GRAY RELATIONAL ALGORITHM FOR ECG PATTERN RECOGNITION." Biomedical Engineering: Applications, Basis and Communications 19, no. 05 (October 2007): 313–21. http://dx.doi.org/10.4015/s1016237207000410.
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The paper proposes a gray relational analysis based learning algorithm, called gray relational algorithm, for the recognition of ECG beats. Without analyzing relations between the input ECG beat and every beat in the database for the recognition, several training beats are chosen for learning from an ECG waveform database with patient diagnosis information, and then the learning result is used to analyze the test ECGs. The resulting similarity measurement is further identified as the diagnosis of the test ECG. This algorithm is capable of reducing the computational procedure of gray relational analysis as it is directly used for the analysis. The experiment shows that the proposed method can achieve a good classification result.
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Kotas, M. "Projective Filtering of Time-Aligned ECG Beats." IEEE Transactions on Biomedical Engineering 51, no. 7 (July 2004): 1129–39. http://dx.doi.org/10.1109/tbme.2004.826592.
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Chikh, Mohammed Amine, Mohammed Ammar, and Radja Marouf. "A Neuro-Fuzzy Identification of ECG Beats." Journal of Medical Systems 36, no. 2 (July 14, 2010): 903–14. http://dx.doi.org/10.1007/s10916-010-9554-4.
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Das, Manab Kumar, and Samit Ari. "ECG Beats Classification Using Mixture of Features." International Scholarly Research Notices 2014 (September 17, 2014): 1–12. http://dx.doi.org/10.1155/2014/178436.
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Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.
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Ramakrishnan, A. G., and S. Saha. "ECG Compression by Multirate Processing of Beats." Computers and Biomedical Research 29, no. 5 (October 1996): 407–17. http://dx.doi.org/10.1006/cbmr.1996.0030.
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Kotas, M. "Projective filtering of time warped ECG beats." Computers in Biology and Medicine 38, no. 1 (January 2008): 127–37. http://dx.doi.org/10.1016/j.compbiomed.2007.08.002.
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Rimbi, Mary, Immaculate Nakitende, Teopista Namujwiga, and John Kellett. "How well are heart rates measured by pulse oximeters and electronic sphygmomanometers? Practice-based evidence from an observational study of acutely ill medical patients during hospital admission." Acute Medicine Journal 18, no. 3 (July 1, 2019): 144–47. http://dx.doi.org/10.52964/amja.0767.
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Background: heart rates generated by pulse oximeters and electronic sphygmomanometers in acutely ill patients may not be the same as those recorded by ECG Methods: heart rates recorded by an oximeter and an electronic sphygmomanometer were compared with electrocardiogram (ECG) heart rates measured on acutely ill medical patients. Results: 1010 ECGs were performed on 217 patients while they were in the hospital. The bias between the oximeter and the ECG measured heart rate was -1.37 beats per minute (limits of agreement -22.6 to 19.9 beats per minute), and the bias between the sphygmomanometer and the ECG measured heart rate was -0.14 beats per minute (limits of agreement -22.2 to 21.9 beats per minute). Both devices failed to identify more than half the ECG recordings that awarded 3 NEWS points for heart rate. Conclusion: Heart rates of acutely ill patients are not reliably measured by pulse oximeter or electronic sphygmomanometers.
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Yao, Guoliang, Xiaobo Mao, Nan Li, Huaxing Xu, Xiangyang Xu, Yi Jiao, and Jinhong Ni. "Interpretation of Electrocardiogram Heartbeat by CNN and GRU." Computational and Mathematical Methods in Medicine 2021 (August 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/6534942.
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The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.
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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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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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Alhelal, Dheyaa, and Miad Faezipour. "Denoising and Beat Detection of ECG Signal by Using FPGA." International Journal of High Speed Electronics and Systems 26, no. 03 (June 27, 2017): 1740016. http://dx.doi.org/10.1142/s012915641740016x.
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This paper introduces an efficient digital system design using hardware concepts to filter the Electrocardiogram (ECG) signal and to detect QRS complex (beats). The system implementation has been done using a Field Programmable Gate Array (FPGA) in two phases. In the first phase, Finite Impule Response (FIR) filters are designed for preprocessing and denoising the ECG signal. The filtered signal is then used as the input of the second phase to detect and classify the ECG beats. The entire system has been implemented on ALTERA DE II FPGA by desinging synthesizable finite state machines. The design has been tested on ECG waves from the MIT-BIH Arrhythmia database by windowing the signal and applying adaptive signal and noise theresholds in each window of processing. The hardware system has achieved an overall accuracy of 98% in the beat detection phase, while providing the detected beats and the classification of irregular heat-beat rates in real-time. The synthesized hardware of the ECG denoising and beat detection system yields reasonable hardware resources, making the system attractive to be eventually fabricated as a stand alone hardware system or integrated/embedded within a portable electronic device for monitoring patients’ conditions on a daily basis conveinently.
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Nejad, Hadi Chahkandi, Omid Khayat, and Javad Razjouyan. "CHAOTIC FEATURE EXTRACTION AND NEURO-FUZZY CLASSIFIER FOR ECG SIGNAL CHARACTERIZATION." Biomedical Engineering: Applications, Basis and Communications 26, no. 03 (March 17, 2014): 1450038. http://dx.doi.org/10.4015/s1016237214500380.
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In this paper, a neuro-fuzzy network is employed to classify the ECG beats based on the extracted chaotic features. Six groups of ECG beats (MIT-BIH Normal Sinus rhythm, BIDMC congestive heart failure, CU ventricular tachyarrhythmia, MIT-BIH atrial fibrillation, MIT-BIH Malignant Ventricular Arrhythmia and MIT-BIH supraventricular arrhythmia) are characterized by the six chaotic parameters including the largest Lyapunov exponent and average of the Lyapunov spectrum (related to the chaoticity of the signal), time lag and embedding dimension (related to the phase space reconstruction) and correlation dimension and approximate entropy of the signal (related to the complexity of the signal). Finally, six structures of the neuro-fuzzy network (in terms of the type of fuzzy set, the number of fuzzy sets per variable and the number of learning epochs) were employed to perform the ECG beats classification based on all extracted features for two lengths of the signals. It was found that all respective chaotic features are discriminative and they improve the classification rate of ECG beats. Also, it is shown that a minimum length of the signal is needed for exhibitive feature extraction and for the higher lengths of the signal (in time) no significant improvement is achieved in feature extraction and calculations. The criteria for the classification task are considered as accuracy, specificity and sensitivity which all together comprehensively demonstrate the capability and performance of the classification. Some conclusions are drawn and they are discussed at the end of the paper.
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Mateo, J., A. M. Torres, A. Aparicio, and J. L. Santos. "An efficient method for ECG beat classification and correction of ectopic beats." Computers & Electrical Engineering 53 (July 2016): 219–29. http://dx.doi.org/10.1016/j.compeleceng.2015.12.015.
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Kotas, M. "Robust projective filtering of time-warped ECG beats." Computer Methods and Programs in Biomedicine 92, no. 2 (November 2008): 161–72. http://dx.doi.org/10.1016/j.cmpb.2008.06.007.
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Kumar, Amit, and Mandeep Singh. "Statistical analysis of ST segments in ECG signals for detection of ischaemic episodes." Transactions of the Institute of Measurement and Control 40, no. 3 (October 7, 2016): 819–30. http://dx.doi.org/10.1177/0142331216667811.
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This paper highlights a new method for the detection of ischaemic episodes using statistical features derived from ST segment deviations in electrocardiogram (ECG) signal. Firstly, ECG records are pre-processed for the removal of artifacts followed by the delineation process. Then region of interest (ROI) is defined for ST segment and isoelectric reference to compute the ST segment deviation. The mean thresholds for ST segment deviations are used to differentiate the ischaemic beats from normal beats in two stages. The window characterization algorithm is developed for filtration of spurious beats in ischaemic episodes. The ischaemic episode detection is made through the coefficient of variation (COV), kurtosis and form factor. A bell-shaped normal distribution graph is generated for normal and ischaemic ST segments. The results show average sensitivity (Se) 97.71% and positive predictivity (+P) 96.89% for 90 records of the annotated European ST-T database (EDB) after validation. These results are significantly better than those of the available methods reported in the literature. The simplicity and automatic discarding of irrelevant beats makes this method feasible for use in clinical systems.
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Niranjana Murthy, H. S. "Comparison Between Non-Linear Autoregressive and Non-Linear Autoregressive with Exogeneous Inputs Models for Predicting Cardiac Ischemic Beats." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 3974–78. http://dx.doi.org/10.1166/jctn.2020.9001.
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The prediction accuracy and generalization ability of neural models for forecasting Myocardial Ischemic Beats depends on type and architecture of employed network model. This paper presents the comparison analysis of recurrent neural network (RNN) architectures with embedded memory, Non-linear Autoregressive (NAR) and Non-linear Autoregressive with Exogeneous inputs (NARX) models for forecasting Ischemic Beats in ECG. Numerous architectures of the NAR and NARX models are verified for prediction and the performances are evaluated in terms of MSE. The performances of NAR and NARX models are validated by using ECG signals acquired from MIT-BIH database. The results have depicted that the NARX architecture with 2 neurons in hidden layer and 1 delay line outperformed with least Mean Square Error (MSE) of 0.0001 for detecting the ischemic beats in ECG.
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Casas, Manuel M., Roberto L. Avitia, Felix F. Gonzalez-Navarro, Jose A. Cardenas-Haro, and Marco A. Reyna. "Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces." Journal of Healthcare Engineering 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/2694768.
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According to the American Heart Association, in its latest commission about Ventricular Arrhythmias and Sudden Death 2006, the epidemiology of the ventricular arrhythmias ranges from a series of risk descriptors and clinical markers that go from ventricular premature complexes and nonsustained ventricular tachycardia to sudden cardiac death due to ventricular tachycardia in patients with or without clinical history. The premature ventricular complexes (PVCs) are known to be associated with malignant ventricular arrhythmias and sudden cardiac death (SCD) cases. Detecting this kind of arrhythmia has been crucial in clinical applications. The electrocardiogram (ECG) is a clinical test used to measure the heart electrical activity for inferences and diagnosis. Analyzing large ECG traces from several thousands of beats has brought the necessity to develop mathematical models that can automatically make assumptions about the heart condition. In this work, 80 different features from 108,653 ECG classified beats of the gold-standard MIT-BIH database were extracted in order to classify the Normal, PVC, and other kind of ECG beats. Three well-known Bayesian classification algorithms were trained and tested using these extracted features. Experimental results show that the F1 scores for each class were above 0.95, giving almost the perfect value for the PVC class. This gave us a promising path in the development of automated mechanisms for the detection of PVC complexes.
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Hwang, Ho Bin, Hyeokchan Kwon, Byungho Chung, Jongshill Lee, and In Young Kim. "ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions." Sensors 21, no. 21 (October 20, 2021): 6966. http://dx.doi.org/10.3390/s21216966.
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The development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes of ECG, which are affected by physical and psychological factors, can make authentication difficult. In this paper, we propose authentication using non-linear normalization of ECG beats that is robust to changes in ECG waveforms according to heart rate fluctuations in various daily activities. We performed a non-linear normalization method through the analysis of ECG alongside heart rate, evaluating similarities and authenticating the performance of our new method compared to existing methods. Compared with beats before normalization, the average similarity of the proposed method increased 23.7% in the resting state and 43% in the non-resting state. After learning in the resting state, authentication performance reached 99.05% accuracy for the resting state and 88.14% for the non-resting state. The proposed method can be applicable to an ECG-based authentication system under various physiological conditions.
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Sharmila, Vallem, and K. Ashoka Reddy. "Identification of Premature Ventricular Cycles of Electrocardiogram Using Discrete Cosine Transform-Teager Energy Operator Model." Journal of Medical Engineering 2015 (March 2, 2015): 1–9. http://dx.doi.org/10.1155/2015/438569.
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An algorithm based on the ability of TEO to track the changes in the envelope of ECG signal is proposed for identifying PVCs in ECG. Teager energy is calculated from DCT coefficients of ECG signal. This method can be considered as computationally efficient algorithm when compared with the well-known DCT cepstrum technique. EPE is derived from the teager energy of DCT coefficients in DCT-TEO method and from the cepstrum of DCT coefficients in the existing method. EPE determines the decay rate of the action potential of ECG beat and provides sufficient information to identify the PVC beats in ECG data. EPEs obtained by DCT-TEO and existing DCT cepstrum models are compared. The proposed algorithm has resulted in performance measures like sensitivity of 98–100%, positive predictivity of 100%, and detection error rate of 0.03%, when tested on MIT-BIH database signals consisting of PVC and normal beats. Result analysis reveals that the DCT-TEO algorithm worked well in clear identification of PVCs from normal beats compared to the existing algorithm, even in the presence of artifacts like baseline wander, PLI, and noise with SNR of up to −5 dB.
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Dumont, Jerome, Alfredo I. Hernandez, and Guy Carrault. "Improving ECG Beats Delineation With an Evolutionary Optimization Process." IEEE Transactions on Biomedical Engineering 57, no. 3 (March 2010): 607–15. http://dx.doi.org/10.1109/tbme.2008.2002157.
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Engin, Mehmet, Musa Fedakar, Erkan Zeki Engin, and Mehmet Korürek. "Feature measurements of ECG beats based on statistical classifiers." Measurement 40, no. 9-10 (November 2007): 904–12. http://dx.doi.org/10.1016/j.measurement.2006.10.012.
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Sharma, L. N. "Coding ECG beats using multiscale compressed sensing based processing." Computers & Electrical Engineering 45 (July 2015): 211–21. http://dx.doi.org/10.1016/j.compeleceng.2014.07.016.
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Tran, Hoai Linh, Van Nam Pham, and Duc Thao Nguyen. "A hardware implementation of intelligent ECG classifier." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 3 (May 5, 2015): 905–19. http://dx.doi.org/10.1108/compel-05-2014-0119.
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Purpose – The purpose of this paper is to design an intelligent ECG classifier using programmable IC technologies to implement many functional blocks of signal acquisition and processing in one compact device. The main microprocessor also simulates the TSK neuro-fuzzy classifier in testing mode to recognize the ECG beats. The design brings various theoretical solutions into practical applications. Design/methodology/approach – The ECG signals are acquired and pre-processed using the Field-Programmable Analog Array (FPAA) IC due to the ability of precise configuration of analog parameters. The R peak of the QRS complexes and a window of 300 ms of ECG signals around the R peak are detected. In this paper we have proposed a method to extract the signal features using the Hermite decomposition algorithm, which requires only a multiplication of two matrices. Based on the features vectors, the ECG beats are classified using a TSK neuro-fuzzy network, whose parameters are trained earlier on PC and downloaded into the device. The device performance was tested with the ECG signals from the MIT-BIH database to prove the correctness of the hardware implementations. Findings – The FPAA and Programmable System on Chip (PSoC) technologies allow us to integrate many signal processing blocks in a compact device. In this paper the device has the same performance in ECG signal processing and classifying as achieved on PC simulators. This confirms the correctness of the implementation. Research limitations/implications – The device was fully tested with the signals from the MIT-BIH databases. For new patients, we have tested the device in collecting the ECG signals and QRS detections. We have not created a new database of ECG signals, in which the beats are examined by doctors and annotated the type of the rhythm (normal or abnormal, which type of arrhythmia, etc.) so we have not tested the classification mode of the device on real ECG signals. Social implications – The compact design of an intelligent ECG classifier offers a portable solution for patients with heart diseases, which can help them to detect the arrhythmia on time when the doctors are not nearby. This type of device not only may help to improve the patients’ safety but also contribute to the smart, inter-networked life style. Originality/value – The device integrate a number of solutions including software, hardware and algorithms into a single, compact device. Thank to the advance of programmable ICs such as FPAA and PSoC, the designed device can acquire one channel of ECG signals, extract the features and classify the arrhythmia type (if detected) using the neuro-fuzzy TSK network in online mode.
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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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Oleiwi, Zahraa Ch, Ebtesam N. AlShemmary, and Salam Al-Augby. "Arrhythmia Detection Based on New Multi-Model Technique for ECG Inter-Patient Classification." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 12 (August 31, 2023): 78–98. http://dx.doi.org/10.3991/ijoe.v19i12.41631.
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This paper presents a novel model for arrhythmia detection based on a cascading technique that utilizes a combination of the One-Sided Selection (OSS) method, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms, this model denoted by (OWSK) model to classify four types of electrocardiogram (ECG) heartbeats following inter-patient scheme. The OWSK model consists of three stages. The first stage involves resampling using the One-Sided Selection (OSS) method to solve the imbalance problem and reduce data by removing noisy, borderline, and redundant samples. The second stage involves using Wavelet Transformation (WT) and Power Spectral Density (PSD) to extract the most relevant frequency domain features. The third stage involves a cascading process by constructing the classifier from SVM trained on the whole dataset to classify normal and abnormal beats. Then, KNN (K-Nearest Neighbors) is trained on only the three irregular minority classes to classify the three types of arrhythmias for the detection of ventricular ectopic beats, supraventricular ectopic beats, and fusion beats (V, S, and F). The performance of the proposed model is evaluated in terms of different metrics, including accuracy, recall, precision, and F1 score. The results show the superiority of the proposed model in medical diagnosis compared to the latest works, where it achieves 90%, 90%, 93%, and 91% for accuracy, recall, precision, and F1 score under the inter-patient paradigm and 98%, 98%, 98%, and 98% under the intra-patient paradigm.
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Mateev, Hristo, Iana Simova, Tzvetana Katova, and Nikolay Dimitrov. "Clinical Evaluation of a Mobile Heart Rhythm Telemonitoring System." ISRN Cardiology 2012 (October 14, 2012): 1–8. http://dx.doi.org/10.5402/2012/192670.
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Purpose. To evaluate the clinical applicability of a telemonitoring system: telemetric system for collection and distant surveillance of medical information (TEMEO). Methods. We evaluated 60 patients, applying simultaneously standard Holter ECG and telemonitoring. Two different comparisons were performed: (1) TEMEO ECG with standard 12-lead ECG; (2) TEMEO Holter with standard ECG Holter. Results. We found a very high coincidence rate (99.3%) between TEMEO derived ECGs and standard ECGs. Intraclass correlation coefficient analysis revealed high and significant correlation coefficients regarding average, maximal, and minimal heart rate, % of time in tachycardia, single supraventricular ectopic beats (SVEB), and single and couplets of ventricular ectopic beats (VEB) between Holter ECG and TEMEO derived parameters. Couplets of SVEB were recorded as different by the two monitoring systems, however, with a borderline statistical significance. Conclusions. TEMEO derived ECGs have a very high coincidence rate with standard ECGs. TEMEO patient monitoring provides results that are similar to those derived from a standard Holter ECG.
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Zhao, Lina, Jianqing Li, Jinle Xiong, Xueyu Liang, and Chengyu Liu. "Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based Sample Entropy." Entropy 22, no. 4 (April 4, 2020): 411. http://dx.doi.org/10.3390/e22040411.
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Sample entropy (SampEn) is widely used for electrocardiogram (ECG) signal analysis to quantify the inherent complexity or regularity of RR interval time series (i.e., heart rate variability (HRV)), with the hypothesis that RR interval time series in pathological conditions output lower SampEn values. However, ectopic beats can significantly influence the entropy values, resulting in difficulty in distinguishing the pathological situation from normal situations. Although a theoretical operation is to exclude the ectopic intervals during HRV analysis, it is not easy to identify all of them in practice, especially for the dynamic ECG signal. Thus, it is important to suppress the influence of ectopic beats on entropy results, i.e., to improve the robustness and stability of entropy measurement for ectopic beats-inserted RR interval time series. In this study, we introduced a physical threshold-based SampEn method, and tested its ability to suppress the influence of ectopic beats for HRV analysis. An experiment on the PhysioNet/MIT RR Interval Databases showed that the SampEn use physical meaning threshold has better performance not only for different data types (normal sinus rhythm (NSR) or congestive heart failure (CHF) recordings), but also for different types of ectopic beat (atrial beats, ventricular beats or both), indicating that using a physical meaning threshold makes SampEn become more consistent and stable.
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Mohebbanaaz, Mohebbanaaz, Y. Padma Sai, and L. V. Rajani Kumari. "Detection of cardiac arrhythmia using deep CNN and optimized SVM." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (October 1, 2021): 217. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp217-225.
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<span>Deep learning (DL) <span>has become a topic of study in various applications, including healthcare. Detection of abnormalities in an electrocardiogram (ECG) plays a significant role in patient monitoring. It is noted that a deep neural network when trained on huge data, can easily detect cardiac arrhythmia. This may help cardiologists to start treatment as early as possible. This paper proposes a new deep learning model adapting the concept of transfer learning to extract deep-CNN features and facilitates automated classification of electrocardiogram (ECG) into sixteen types of ECG beats using an optimized support vector machine (SVM). The proposed strategy begins with gathering ECG datasets, removal of noise from ECG signals, and extracting beats from denoised ECG signals. Feature extraction is done using ResNet18 via concept of transfer learning. These extracted features are classified using optimized SVM. These methods are evaluated and tested on the MIT-BIH arrhythmia database. Our proposed model is effective compared to all State of Art Techniques with an accuracy of 98.70%.</span></span>
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Wang, Zhizhong, Hongyi Li, Chuang Han, Songwei Wang, and Li Shi. "Arrhythmia Classification Based on Multiple Features Fusion and Random Forest Using ECG." Journal of Medical Imaging and Health Informatics 9, no. 8 (October 1, 2019): 1645–54. http://dx.doi.org/10.1166/jmihi.2019.2798.
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Cardiovascular diseases have become more and more prominent in recent years, which have proven to be a major threat to people's health. Accurate detection of arrhythmia in patients has important implications for clinical treatment. The aim of this study was to propose a novel automatic classification method for arrhythmia in order to improve classification accuracy. The electrocardiogram (ECG) signal was subjected preprocessing for denoising purposes using a wavelet transform. Then, the local and global characteristics of the beat, which contained RR interval features according with the clinical diagnosis criterion, morphology features based on wavelet packet decomposition and statistical features along with kurtosis coefficient, skewness coefficient and variance are exploited and fused. Meanwhile, the dimensionality of wavelet packet coefficients were reduced via principal component analysis (PCA). Finally, these features were used as the input of the random forest classifier to train the model and were then compared with the support vector machine (SVM) and back propagation (BP) neural networks. Based on 100,647 beats from the MIT-BIH database, the proposed method achieved an average accuracy, specificity and sensitivity of 99.08%, 99.00% and 89.31%, respectively, using the intra-patient beats, and 92.31%, 89.98% and 37.47%, respectively, using the inter-patient beats. Moreover, two classification schemes, namely, inter-patient and intra-patient scheme, were validated. Compared with the other methods referred to in this paper, the performance of the novel method yielded better results.
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Casas, Manuel M., Roberto L. Avitia, Jose Antonio Cardenas-Haro, Jugal Kalita, Francisco J. Torres-Reyes, Marco A. Reyna, and Miguel E. Bravo-Zanoguera. "A Novel Unsupervised Computational Method for Ventricular and Supraventricular Origin Beats Classification." Applied Sciences 11, no. 15 (July 22, 2021): 6711. http://dx.doi.org/10.3390/app11156711.
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Arrhythmias are the most common events tracked by a physician. The need for continuous monitoring of such events in the ECG has opened the opportunity for automatic detection. Intra- and inter-patient paradigms are the two approaches currently followed by the scientific community. The intra-patient approach seems to resolve the problem with a high classification percentage but requires a physician to label key samples. The inter-patient makes use of historic data of different patients to build a general classifier, but the inherent variability in the ECG’s signal among patients leads to lower classification percentages compared to the intra-patient approach. In this work, we propose a new unsupervised algorithm that adapts to every patient using the heart rate and morphological features of the ECG beats to classify beats between supraventricular origin and ventricular origin. The results of our work in terms of F-score are 0.88, 0.89, and 0.93 for the ventricular origin beats for three popular ECG databases, and around 0.99 for the supraventricular origin for the same databases, comparable to supervised approaches presented in other works. This paper presents a new path to make use of ECG data to classify heartbeats without the assistance of a physician despite the needed improvements.
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Liu, Tong, Yujuan Si, Dunwei Wen, Mujun Zang, and Liuqi Lang. "Dictionary learning for VQ feature extraction in ECG beats classification." Expert Systems with Applications 53 (July 2016): 129–37. http://dx.doi.org/10.1016/j.eswa.2016.01.031.
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OH, SHU LIH, MUHAMMAD ADAM, JEN HONG TAN, YUKI HAGIWARA, VIDYA K. SUDARSHAN, JOEL EN WEI KOH, KUANG CHUA CHUA, KOK POO CHUA, RU SAN TAN, and EDDIE Y. K. NG. "AUTOMATED IDENTIFICATION OF CORONARY ARTERY DISEASE FROM SHORT-TERM 12 LEAD ELECTROCARDIOGRAM SIGNALS BY USING WAVELET PACKET DECOMPOSITION AND COMMON SPATIAL PATTERN TECHNIQUES." Journal of Mechanics in Medicine and Biology 17, no. 07 (November 2017): 1740007. http://dx.doi.org/10.1142/s0219519417400073.
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The occlusion of the coronary arteries commonly known as coronary artery disease (CAD) restricts the normal blood circulation required to the heart muscles, thus results in an irreversible myocardial damage or death (myocardial infarction). Clinically, electrocardiogram (ECG) is performed as a primary diagnostic tool to capture these cardiac activities and detect the presence of CAD. However, the use of computer-aided techniques can reduce the visual burden and manual time required for the analysis of complex ECG signals in order to identify the CAD affected subjects from normal ones. Therefore, in this study, a novel computer-aided technique is proposed using 2[Formula: see text]s of 12 lead ECG signals for the identification of CAD affected patients. Each of the 2[Formula: see text]s 12 lead ECG signal beats (3791 normal and 12308 CAD ECG signal beats) are implemented with four levels of wavelet packet decomposition (WPD) to obtain various coefficients. Using the fourth-level coefficients obtained for each lead ECG signal beat, new 2[Formula: see text]s. ECG signal beats are reconstructed. Later, the reconstructed signals are split into two-fold data sets, in which one set is used for acquiring common spatial pattern (CSP) filter and the other for obtaining features vector (vice versa). The obtained features are one by one fed into k-nearest neighbors (KNN) classifier for automated classification. The proposed system yielded maximum average classification results of 99.65% accuracy, 99.64% sensitivity and 99.7% specificity using 10 features. Our proposed algorithm is highly efficient and can be used by the clinicians as an aiding system in their CAD diagnosis, thus, assisting in faster treatment and avoiding the progression of CAD condition.
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Weiler, Dustin T., Stefanie O. Villajuan, Laura Edkins, Sean Cleary, and Jason J. Saleem. "Wearable Heart Rate Monitor Technology Accuracy in Research: A Comparative Study Between PPG and ECG Technology." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1292–96. http://dx.doi.org/10.1177/1541931213601804.
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Wearable heart rate (HR) monitors typically implement photoplethysmography (PPG) technology and are used in research as an alternative to electrocardiogram (ECG). However, questions surrounding the accuracy of PPG technology exist. To provide an answer regarding the question of accuracy, we conducted a study to compare average HR readings of two different HR technologies (PPG vs. ECG) after an interval style cardio-based workout. A total of 30 trials were conducted and average HR readings from the two HR technologies were compared using an ANOVA. Results revealed no significant difference between the two technologies. However, when HR reached around 155-160 beats per minute, a difference of +/− 5 beats per minute was observed between the two technologies with PPG HR readings being less than ECG. As a result, future research could consider the wearable PPG HR technology as accurate, but with certain experimental design implications.
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Zubair, Muhammad, and Changwoo Yoon. "Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks." Sensors 22, no. 11 (May 27, 2022): 4075. http://dx.doi.org/10.3390/s22114075.
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Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to their vital role in the diagnosis of cardiac abnormalities. Despite this interest, deep feature representation for ECG is still challenging and intriguing due to the inter-patient variability of the ECG’s morphological characteristics. The aim of this study was to learn a balanced deep feature representation that incorporates both the short-term and long-term morphological characteristics of ECG beats. For efficient feature extraction, we designed a temporal transition module that uses convolutional layers with different kernel sizes to capture a wide range of morphological patterns. Imbalanced data are a key issue in developing an efficient and generalized model for arrhythmia detection as they cause over-fitting to minority class samples (abnormal beats) of primary interest. To mitigate the imbalanced data issue, we proposed a novel, cost-sensitive loss function that ensures a balanced deep representation of class samples by assigning effective weights to each class. The cost-sensitive loss function dynamically alters class weights for every batch based on class distribution and model performance. The proposed method acquired an overall accuracy of 99.81% for intra-patient classification and 96.36% for the inter-patient classification of heartbeats. The experimental results reveal that the proposed approach learned a balanced representation of ECG beats by mitigating the issue of imbalanced data and achieved an improved classification performance as compared to other studies.
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Li, Jia, Yujuan Si, Tao Xu, and Saibiao Jiang. "Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques." Mathematical Problems in Engineering 2018 (December 2, 2018): 1–10. http://dx.doi.org/10.1155/2018/7354081.
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Although convolutional neural networks (CNNs) can be used to classify electrocardiogram (ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed as one-dimensional signals while CNNs are better suited to multidimensional pattern or image recognition applications. In this study, the morphology and rhythm of heartbeats are fused into a two-dimensional information vector for subsequent processing by CNNs that include adaptive learning rate and biased dropout methods. The results demonstrate that the proposed CNN model is effective for detecting irregular heartbeats or arrhythmias via automatic feature extraction. When the proposed model was tested on the MIT-BIH arrhythmia database, the model achieved higher performance than other state-of-the-art methods for five and eight heartbeat categories (the average accuracy was 99.1% and 97%). In particular, the proposed system had better performance in terms of the sensitivity and positive predictive rate for V beats by more than 4.3% and 5.4%, respectively, and also for S beats by more than 22.6% and 25.9%, respectively, when compared to existing algorithms. It is anticipated that the proposed method will be suitable for implementation on portable devices for the e-home health monitoring of cardiovascular disease.
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Zhang, Jing, Aiping Liu, Deng Liang, Xun Chen, and Min Gao. "Interpatient ECG Heartbeat Classification with an Adversarial Convolutional Neural Network." Journal of Healthcare Engineering 2021 (May 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/9946596.
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Discovering shared, invariant feature representations across subjects in electrocardiogram (ECG) classification tasks is crucial for improving the generalization of models to unknown patients. Although deep neural networks have recently been emerging in extracting generalizable ECG features, they usually rely on labeled samples from a large number of subjects to guarantee generalization. Extracting invariant representations to intersubject variabilities from a small number of subjects is still a challenge today due to individual physical differences. To address this problem, we propose an adversarial deep neural network framework for interpatient heartbeat classification by integrating adversarial learning into a convolutional neural network to learn subject-invariant, class-discriminative features. The proposed method was evaluated on the MIT-BIH arrhythmia database which is a publicly available ECG dataset collected from 47 patients. Compared with the state-of-the-art methods, the proposed method achieves the highest performance for detecting supraventricular ectopic beats (SVEBs), which are very challenging to identify, and also gains comparable performance on the detection of ventricular ectopic beats (VEBs). The sensitivities of SVEBs and VEBs are 78.8% and 92.5%, respectively. The precisions of SVEBs and VEBs are 90.8% and 94.3%, respectively. With high performance in the detection of pathological classes (i.e., SVEBs and VEBs), this work provides a promising method for ECG classification tasks when the number of patients is limited.
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Cai, Hao, Lingling Xu, Jianlong Xu, Zhi Xiong, and Changsheng Zhu. "Electrocardiogram Signal Classification Based on Mix Time-Series Imaging." Electronics 11, no. 13 (June 24, 2022): 1991. http://dx.doi.org/10.3390/electronics11131991.
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Arrhythmia is a significant cause of death, and it is essential to analyze the electrocardiogram (ECG) signals as this is usually used to diagnose arrhythmia. However, the traditional time series classification methods based on ECG ignore the nonlinearity, temporality, or other characteristics inside these signals. This paper proposes an electrocardiogram classification method that encodes one-dimensional ECG signals into the three-channel images, named ECG classification based on Mix Time-series Imaging (EC-MTSI). Specifically, this hybrid transformation method combines Gramian angular field (GAF), recurrent plot (RP), and tiling, preserving the original ECG time series’ time dependence and correlation. We use a variety of neural networks to extract features and perform feature fusion and classification. This retains sufficient details while emphasizing local information. To demonstrate the effectiveness of the EC-MTSI, we conduct abundant experiments in a commonly-used dataset. In our experiments, the general accuracy reached 93.23%, and the accuracy of identifying high-risk arrhythmias of ventricular beats and supraventricular beats alone are as high as 97.4% and 96.3%, respectively. The results reveal that the proposed method significantly outperforms the existing approaches.
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Sato, Shinichi. "Quantitative evaluation of ontogenetic change in heart rate and its autonomic regulation in newborn mice with the use of a noninvasive piezoelectric sensor." American Journal of Physiology-Heart and Circulatory Physiology 294, no. 4 (April 2008): H1708—H1715. http://dx.doi.org/10.1152/ajpheart.01122.2007.
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A reliable basal heart rate (HR) measurement in freely moving newborn mice was accomplished for the first time by using a novel noninvasive piezoelectric transducer (PZT) sensor. The basal HR was ∼320 beats/min at postnatal day (P)0 and increased with age to ∼690 beats/min at P14. Contribution of autonomic control to HR was then assessed. Sympathetic blockade with metoprolol significantly reduced basal HR at both P6 (−236 ± 23 beats/min; mean ± SE) and P12 (−105 ± 8 beats/min), but atropine was without effect, indicating the predominant tonic adrenergic stimulation and absence of vagal control for basal HR in newborn mice. In contrast to stable basal HR during 5-min recording, HR measured by ECG (ECG-HR) was markedly decreased because of the restraint stress of attaching ECG electrodes, with accompanying freezing behavior. ECG-HR lowered and further decreased gradually during 5 min (slow cardiodeceleration) at P0–P3 and rapidly decreased and gradually recovered within 5 min (transient bradycardia) at P9–P14. The response was not uniform in P4–P8 mice: they showed either of these two patterns or sustained bradycardia (9–29%), and the number of mice that showed transient bradycardia increased with age (30–100%) during the period. Studies with autonomic blockade suggest that the slow cardiodeceleration and transient bradycardia are mediated mainly by withdrawal of adrenergic stimulation and phasic vagal activation, respectively, and the autonomic control of HR response to restraint stress is likely to change from the withdrawal of adrenergic stimulation to the phasic vagal activation at different stages during P4–P8 in individual mice. The PZT sensor may offer excellent opportunities to monitor basal HR of small animals noninvasively.
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Abdelhafid, ERRABIH, EDDER Aymane, NSIRI Benayad, SADIQ Abdelalim, EL YOUSFI ALAOUI My Hachem, OULAD HAJ THAM Rachid, and BENAJI Brahim. "ECG Arrhythmia Classification Using Convolutional Neural Network." International Journal of Emerging Technology and Advanced Engineering 12, no. 7 (July 9, 2022): 186–95. http://dx.doi.org/10.46338/ijetae0722_19.
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This study provides a thorough analysis of earlier DL techniques used to classify the ECG data. The large variability among individual patients and the high expense of labeling clinical ECG records are the main hurdles in automatically detecting arrhythmia by electrocardiogram (ECG). The classification of electrocardiogram (ECG) arrhythmias using a novel and more effective technique is presented in this research. A high-performance electrocardiogram (ECG)-based arrhythmic beats classification system is described in this research to develop a plan with an autonomous feature learning strategy and an effective optimization mechanism, based on the ECG heartbeat classification approach. We propose a method based on efficient 12-layer, the MIT-BIH Arrhythmia dataset's five micro-classes of heartbeat types and using the wavelet denoising technique. Compared to state-of-the-art approaches, the newly presented strategy enables considerable accuracy increase with quicker online retraining and less professional involvement.
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Obeidat, Yusra, and Ali Mohammad Alqudah. "A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification." Traitement du Signal 38, no. 5 (October 31, 2021): 1281–91. http://dx.doi.org/10.18280/ts.380503.
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In this paper we have utilized a hybrid lightweight 1D deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods for accurate, fast, and automated beat-wise ECG classification. The CNN and LSTM models were designed separately to compare with the hybrid CNN-LSTM model in terms of accuracy, number of parameters, and the time required for classification. The hybrid CNN-LSTM system provides an automated deep feature extraction and classification for six ECG beats classes including Normal Sinus Rhythm (NSR), atrial fibrillation (AFIB), atrial flutter (AFL), atrial premature beat (APB), left bundle branch block (LBBB), and right bundle branch block (RBBB). The hybrid model uses the CNN blocks for deep feature extraction and selection from the ECG beat. While the LSTM layer will learn how to extract contextual time information. The results show that the proposed hybrid CNN-LSTM model achieves high accuracy and sensitivity of 98.22% and 98.23% respectively. This model is light and fast in classifying ECG beats and superior to other previously used models which makes it very suitable for embedded systems designs that can be used in clinical applications for monitoring heart diseases in faster and more efficient manner.
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YILDIRIM, ÖZAL. "ECG BEAT DETECTION AND CLASSIFICATION SYSTEM USING WAVELET TRANSFORM AND ONLINE SEQUENTIAL ELM." Journal of Mechanics in Medicine and Biology 19, no. 01 (February 2019): 1940008. http://dx.doi.org/10.1142/s0219519419400086.
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Electrocardiogram (ECG) signals consist of data containing measurements of electrical activity in the heartbeats. These signals include relevant information used to detect abnormalities such as arrhythmia. In this study, a recognition system is proposed for detection and classification of heartbeats in ECG signals. Heartbeats in the ECG data were detected by using the wavelet transform (WT) method and these beats are segmented with determined periods. For obtaining distinctive features from the beats, multi-resolution WT is applied to these segmented signals, and wavelet coefficients are obtained from different frequency levels. Feature vectors are generated on these coefficients by using various statistical methods. The proposed recognition system is trained on feature vectors by using the Online Sequential Extreme Learning Machine (OSELM) classifier during the learning phase to automatically recognize the signals. Five different beat types were obtained from the MIT-BIH arrhythmia dataset. The multi-class dataset that includes five classes and the binary-class dataset that includes two classes were created among these beat types. Performance tests of the proposed wavelet-based-OSELM (W-OSELM) method were realized with these two datasets. The proposed recognition system provided 97.29% correct beat detection rate from raw ECG signals. The classification accuracy is 99.44% for the binary-class dataset and 98.51% for the multi-class dataset. Furthermore, the proposed classifier has shown very fast recognition performance on ECG signals.
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Truedson, Petra, Michael Ott, Krister Lindmark, Malin Ström, Martin Maripuu, Robert Lundqvist, and Ursula Werneke. "Effects of Toxic Lithium Levels on ECG—Findings from the LiSIE Retrospective Cohort Study." Journal of Clinical Medicine 11, no. 19 (October 8, 2022): 5941. http://dx.doi.org/10.3390/jcm11195941.
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(1) Background: Few studies have explored the impact of lithium intoxication on the heart. (2) Methods: We examined electrocardiogram (ECG) changes associated with lithium intoxication in the framework of the LiSIE (Lithium—Study into Effects and Side Effects) retrospective cohort study. We analysed ECGs before, during, and after intoxication. (3) Results: Of the 1136 patients included, 92 patients had experienced 112 episodes of lithium intoxication. For 55 episodes, there was an ECG available at the time; for 48 episodes, there was a reference ECG available before and/or after the lithium intoxication. Lithium intoxication led to a statistically significant decrease in heart rate from a mean 76 beats/min (SD 16.6) before intoxication to 73 beats/min (SD 17.1) during intoxication (p = 0.046). QTc correlated only weakly with lithium concentration (ρ = 0.329, p = 0.014). However, in 24% of lithium intoxication episodes, there were QT prolongations. In 54% of these, QTc exceeded 500 ms; patients with chronic intoxications being more affected. (4) Conclusions: Based on summary statistics, effects of lithium intoxication on HR and QTc seem mostly discrete and not clinically relevant. However, QT prolongation can carry a risk of becoming severe. Therefore, an ECG should always be taken in patients presenting with lithium intoxication.
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MARTIS, ROSHAN JOY, U. RAJENDRA ACHARYA, CHOO MIN LIM, K. M. MANDANA, A. K. RAY, and CHANDAN CHAKRABORTY. "APPLICATION OF HIGHER ORDER CUMULANT FEATURES FOR CARDIAC HEALTH DIAGNOSIS USING ECG SIGNALS." International Journal of Neural Systems 23, no. 04 (June 9, 2013): 1350014. http://dx.doi.org/10.1142/s0129065713500147.
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Electrocardiogram (ECG) is the electrical activity of the heart indicated by P, Q-R-S and T wave. The minute changes in the amplitude and duration of ECG depicts a particular type of cardiac abnormality. It is very difficult to decipher the hidden information present in this nonlinear and nonstationary signal. An automatic diagnostic system that characterizes cardiac activities in ECG signals would provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect cardiac abnormalities in ECG recordings. Application of higher order spectra (HOS) features is a seemingly promising approach because it can capture the nonlinear and dynamic nature of the ECG signals. In this paper, we have automatically classified five types of beats using HOS features (higher order cumulants) using two different approaches. The five types of ECG beats are normal (N), right bundle branch block (RBBB), left bundle branch block (LBBB), atrial premature contraction (APC) and ventricular premature contraction (VPC). In the first approach, cumulant features of segmented ECG signal were used for classification; whereas in the second approach cumulants of discrete wavelet transform (DWT) coefficients were used as features for classifiers. In both approaches, the cumulant features were subjected to data reduction using principal component analysis (PCA) and classified using three layer feed-forward neural network (NN) and least square — support vector machine (LS-SVM) classifiers. In this study, we obtained the highest average accuracy of 94.52%, sensitivity of 98.61% and specificity of 98.41% using first approach with NN classifier. The developed system is ready clinically to run on large datasets.
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CHIKH, M. A., and F. BEREKSI–REGUIG. "CLASSIFICATION OF VENTRICULAR ECTOPIC BEATS (VEB'S) USING NEURAL NETWORKS." Journal of Mechanics in Medicine and Biology 04, no. 03 (September 2004): 333–40. http://dx.doi.org/10.1142/s0219519404001089.
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The most widely used signal in clinical practice is the electrocardiogram (ECG). ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. Thus, the required tasks of ECG processing are the reliable recognition of these waves, and the accurate measurement of clinically important parameters measured from the temporal distribution of the ECG constituent waves. The purpose of this paper is the classification of ventricular ectopic beats (VEB's). This research includes noise handling, feature extraction, and neural classification, all integrated in a three-stage procedure. Thirty features extracted from the morphology of the QRS segment, are reduced to seven coefficients using principal component analysis (PCA) and two coefficients using linear predictive coding (LPC) technique in addition to two other temporal parameters were used separately as the input of two neural network classifiers. The neural classifiers were tested on the MIT-BIH database and high scores were obtained for both sensitivity and specificity (84.88% and 91.92% respectively using ACP technique, and 76.17% and 88.95% using LPC method). This study confirms the power of artificial neural networks in the classification of normal and abnormal VEB beats. Clinical use of this method, however, still requires further investigation.
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Abdelgaber, Khaled M., Mostafa Salah, Osama A. Omer, Ahmed E. A. Farghal, and Ahmed S. Mubarak. "Subject-Independent per Beat PPG to Single-Lead ECG Mapping." Information 14, no. 7 (July 3, 2023): 377. http://dx.doi.org/10.3390/info14070377.
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In this paper, a beat-based autoencoder is proposed for mapping photoplethysmography (PPG) to a single-lead electrocardiogram (single-lead ECG) signal. The main limiting factors represented in uncleaned data, subject dependency, and erroneous beat segmentation are regarded. The dataset is cleaned by a two-stage clustering approach. Rather than complete single–lead ECG signal reconstruction, a beat-based PPG-to-single-lead-ECG (PPG2ECG) conversion is introduced for providing a simple lightweight model that meets the computational capabilities of wearable devices. In addition, peak-to-peak segmentation is employed for alleviating errors in PPG onset detection. Furthermore, subject-dependent training is highlighted as a critical factor in training procedures because most existing work includes different beats/signals from the same subject’s record in both training and testing sets. So, we provide a completely subject-independent model where the testing subjects’ records are hidden in the training stage entirely, i.e., a subject record appears once either in the training or testing set, but testing beats/signals belong to records that never appear in the training set. The proposed deep learning model is designed for providing efficient feature extraction that attains high reconstruction quality over subject-independent scenarios. The achieved performance is about 0.92 for the correlation coefficient and 0.0086 for the mean square error for the dataset extracted/cleaned from the MIMIC II dataset.
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Jadhav, Chetan M., and V. K. Bairagi. "Detection & Classification of Cardiac Arrhythmia." International Journal of Informatics and Communication Technology (IJ-ICT) 6, no. 1 (June 22, 2017): 31. http://dx.doi.org/10.11591/ijict.v6i1.pp31-36.
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<p>The term Arrhythmia refers to any change from the normal sequence in the electrical impulses. It is also treated as abnormal heart rhythms or irregular heartbeats. The rate of growth of Cardiac Arrhythmia disease is very high & its effects can be observed in any age group in society. Arrhythmia detection can be done in many ways but effective & simple method for detection & diagnosis of Cardiac Arrhythmia is by doing analysis of Electrocardiogram signals from ECG sensors. ECG signal can give us the detail information of heart activities, so we can use ECG signals to detect the rhythm & behaviour of heart beats resulting into detection & diagnosis of Cardiac Arrhythmia. In this paper new & improved methodology for early Detection & Classification of Cardiac Arrhythmia has been proposed. In this paper ECG signals are captured using ECG sensors & this ECG signals are used & processed to get the required data regarding heart beats of the human being & then proposed methodology applies for Detection & Classification of Cardiac Arrhythmia. Detection of Cardiac Arrhythmia using ECG signals allows us for easy & reliable way with low cost solution to diagnose Arrhythmia in its prior early stage.</p>