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Journal articles on the topic 'ECG segmentation'

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Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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1

Gacek, A., and W. Pedrycz. "A genetic segmentation of ECG signals." IEEE Transactions on Biomedical Engineering 50, no. 10 (October 2003): 1203–8. http://dx.doi.org/10.1109/tbme.2003.816074.

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2

Kornev, V. P., and V. A. Tatsenko. "Multiscale wavelet analysis in ECG segmentation problem." Electronics and Communications 18, no. 3 (July 7, 2013): 38–42. http://dx.doi.org/10.20535/2312-1807.2013.18.3.158453.

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3

Malali, Aman, Srinidhi Hiriyannaiah, Siddesh G.M., Srinivasa K.G., and Sanjay N.T. "Supervised ECG wave segmentation using convolutional LSTM." ICT Express 6, no. 3 (September 2020): 166–69. http://dx.doi.org/10.1016/j.icte.2020.04.004.

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4

Beraza, Idoia, and Iñaki Romero. "Comparative study of algorithms for ECG segmentation." Biomedical Signal Processing and Control 34 (April 2017): 166–73. http://dx.doi.org/10.1016/j.bspc.2017.01.013.

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5

Preethi, S., and M. Jayasheela M.Jayasheela. "VLSI Implementation of Segmentation of Single Channel ECG." International Journal of Computer Applications 90, no. 13 (March 26, 2014): 27–30. http://dx.doi.org/10.5120/15781-4515.

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6

Yamaguchi, H., O. Uenaka, T. Nakamura, and K. Fujikawa. "Evaluation of Pulmonary MRA Using ECG Triggered k-space Segmentation." Japanese Journal of Radiological Technology 51, no. 8 (1995): 957. http://dx.doi.org/10.6009/jjrt.kj00001352532.

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7

Sayadi, O., and M. B. Shamsollahi. "A model-based Bayesian framework for ECG beat segmentation." Physiological Measurement 30, no. 3 (February 25, 2009): 335–52. http://dx.doi.org/10.1088/0967-3334/30/3/008.

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8

Teplitzky, Benjamin Adam, Mike McRoberts, and Peter J. Schwartz. "B-PO02-185 DEEP LEARNING FOR ECG WAVEFORM SEGMENTATION." Heart Rhythm 18, no. 8 (August 2021): S173—S174. http://dx.doi.org/10.1016/j.hrthm.2021.06.438.

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9

Aspuru, Javier, Alberto Ochoa-Brust, Ramón Félix, Walter Mata-López, Luis Mena, Rodolfo Ostos, and Rafael Martínez-Peláez. "Segmentation of the ECG Signal by Means of a Linear Regression Algorithm." Sensors 19, no. 4 (February 14, 2019): 775. http://dx.doi.org/10.3390/s19040775.

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The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.
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Bailey, Ben, and Saeed Babaeizadeh. "Record segmentation to speed up long-term ECG analysis algorithms." Journal of Electrocardiology 69 (November 2021): 86. http://dx.doi.org/10.1016/j.jelectrocard.2021.11.016.

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11

Юрова, А. С. "An algorithmic chain for the forward personalized ECG simulation and the evaluation of its working time." Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie), no. 1(55) (March 13, 2018): 72–84. http://dx.doi.org/10.26089/nummet.v19r107.

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Предложена последовательность алгоритмов, позволяющая реализовать прямое моделирование электрокардиографии с использованием данных об индивидуальных анатомических особенностях пациентов. Приведенная цепочка включает в себя алгоритмы сегментации медицинских изображений, построения расчетной сетки и решения прямой задачи электрокардиографии. Реализовано ускорение алгоритмов сегментации и решения прямой задачи электрокардиографии. Дана оценка времени работы предложенной последовательности алгоритмов. An algorithmic chain for the forward ECG simulation using personalized anatomical patient models is proposed. The proposed algorithmic chain contains algorithms for segmentation of medical images, mesh generation and solving the forward ECG problem. The algorithms for segmentation and solving the forward ECG problem are accelerated. The working time of the algorithmic chain is evaluated.
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12

Yoshizawa, Satoshi, Toshio Tsuchihashi, Toshio Maki, Shuugo Yanagawa, Koujyu Mizutani, and Takeshi Suzuki. "Study of ECG Triggered K-Space Segmentation(fastcard)2D-TOF MR Angiography." Japanese Journal of Radiological Technology 54, no. 1 (1998): 147. http://dx.doi.org/10.6009/jjrt.kj00001351817.

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13

Kolokolnikov, George, Anna Borde, Victor Skuratov, Roman Gaponov, and Anastasiya Rumyantseva. "Comparative Study of Neural Network-Based Approaches for QRS Segmentation." International Journal of Embedded and Real-Time Communication Systems 11, no. 4 (October 2020): 80–103. http://dx.doi.org/10.4018/ijertcs.2020100105.

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The paper is devoted to the development of QRS segmentation system based on deep learning approach. The considered segmentation problem plays an important role in the automatic analysis of heart rhythms, which makes it possible to identify life-threatening pathologies. The main goal of the research is to choose the best segmentation pipeline in terms of accuracy and time-efficiency. Process of ECG-signal analysis is described, and the problem of QRS segmentation is discussed. State-of-the-art algorithms are analyzed in literature review section and the most prominent are chosen for further research. In the course of the research, four hypotheses about appropriate deep learning model are checked: LSTM-based model, 2-input 1-dimensional CNN model, “signal-to-picture” approach based on 2-dimensional CNN, and the simplest 1-dimensional CNN model. All the architectures are tested, and their advantages and disadvantages are discussed. The proposed ECG segmentation pipeline is developed for Holter monitor software.
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14

Akhbari, Mahsa, Mohammad B. Shamsollahi, Omid Sayadi, Antonis A. Armoundas, and Christian Jutten. "ECG segmentation and fiducial point extraction using multi hidden Markov model." Computers in Biology and Medicine 79 (December 2016): 21–29. http://dx.doi.org/10.1016/j.compbiomed.2016.09.004.

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15

Shahbakhti, Mohammad, Elnaz Heydari, and Gia Thien Luu. "Segmentation of ECG from Surface EMG Using DWT and EMD: A Comparison Study." Fluctuation and Noise Letters 13, no. 04 (October 20, 2014): 1450030. http://dx.doi.org/10.1142/s0219477514500308.

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The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.
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Lynn, Htet Myet, Pankoo Kim, and Sung Bum Pan. "Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication." Applied Sciences 11, no. 3 (January 26, 2021): 1125. http://dx.doi.org/10.3390/app11031125.

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In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.
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17

SUN, Huan, Yuchun GUO, Yishuai CHEN, and Bin CHEN. "ECG Classification with Multi-Scale Deep Features Based on Adaptive Beat-Segmentation." IEICE Transactions on Communications E103.B, no. 12 (December 1, 2020): 1403–10. http://dx.doi.org/10.1587/transcom.2020sep0002.

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18

Sepehri, Amir A., Arash Gharehbaghi, Thierry Dutoit, Armen Kocharian, and A. Kiani. "A novel method for pediatric heart sound segmentation without using the ECG." Computer Methods and Programs in Biomedicine 99, no. 1 (July 2010): 43–48. http://dx.doi.org/10.1016/j.cmpb.2009.10.006.

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19

Lourenço, André, Hugo Silva, and Ana Fred. "Unveiling the Biometric Potential of Finger-Based ECG Signals." Computational Intelligence and Neuroscience 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/720971.

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The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.
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20

Duraj, Konrad, Natalia Piaseczna, Paweł Kostka, and Ewaryst Tkacz. "Semantic Segmentation of 12-Lead ECG Using 1D Residual U-Net with Squeeze-Excitation Blocks." Applied Sciences 12, no. 7 (March 25, 2022): 3332. http://dx.doi.org/10.3390/app12073332.

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Analyzing biomedical data is a complex task that requires specialized knowledge. The development of knowledge and technology in the field of deep machine learning creates an opportunity to try and transfer human knowledge to the computer. In turn, this fact influences the development of systems for the automatic evaluation of the patient’s health based on data acquired from sensors. Electrocardiography (ECG) is a technique that enables visualizing the electrical activity of the heart in a noninvasive way, using electrodes placed on the surface of the skin. This signal carries a lot of information about the condition of heart muscle. The aim of this work is to create a system for semantic segmentation of the ECG signal. For this purpose, we used a database from Lobachevsky University available on Physionet, containing 200, 10-second, and 12-lead ECG signals with annotations, and applied one-dimensional U-Net with the addition of squeeze-excitation blocks. The created model achieved a set of parameters indicating high performance (for the test set: accuracy—0.95, AUC—0.99, specificity—0.95, sensitivity—0.99) in extracting characteristic parts of ECG signal such as P and T-waves and QRS complex, regardless of the lead.
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21

Grande-Fidalgo, Alejandro, Javier Calpe, Mónica Redón, Carlos Millán-Navarro, and Emilio Soria-Olivas. "Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition." Sensors 21, no. 16 (August 18, 2021): 5542. http://dx.doi.org/10.3390/s21165542.

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One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patient’s daily life. In this paper, we propose using an ambulatory ECG (aECG) recording device with a low number of leads and then estimating the views that would have been obtained with a standard ECG location, reconstructing the complete Standard 12-Lead System, the most widely used system for diagnosis by cardiologists. Four approaches have been explored, including Linear Regression with ECG segmentation and Artificial Neural Networks (ANN). The best reconstruction algorithm is based on ANN, which reconstructs the actual ECG signal with high precision, as the results bring a high accuracy (RMS Error < 13 μV and CC > 99.7%) for the set of patients analyzed in this paper. This study supports the hypothesis that it is possible to reconstruct the Standard 12-Lead System using an aECG recording device with less leads.
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22

Liu, Lingfeng, Baodan Bai, Xinrong Chen, and Qin Xia. "Semantic Segmentation of QRS Complex in Single Channel ECG with Bidirectional LSTM Networks." Journal of Medical Imaging and Health Informatics 10, no. 3 (March 1, 2020): 758–62. http://dx.doi.org/10.1166/jmihi.2020.2929.

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In this paper, bidirectional Long Short-Term Memory (BiLSTM) networks are designed to realize the semantic segmentation of QRS complex in single channel electrocardiogram (ECG) for the tasks of R peak detection and heart rate estimation. Three types of seq2seq BiLSTM networks are introduced, including the densely connected BiLSTM with attention model, the BiLSTM U-Net, and the BiLSTM U-Net++. To alleviate the sparse problem of the QRS labels, symmetric label expansion is applied by extending the single R peak into a time interval of fixed length. Linear ensemble method is introduced that averages the outputs of different BiLSTM networks. The cross-validation results show significant increase of the accuracy and decrease of discontinuous gaps in the QRS interval prediction by the ensembling over singular neural networks.
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23

Vullings, R., C. H. L. Peters, R. J. Sluijter, M. Mischi, S. G. Oei, and J. W. M. Bergmans. "Dynamic segmentation and linear prediction for maternal ECG removal in antenatal abdominal recordings." Physiological Measurement 30, no. 3 (February 17, 2009): 291–307. http://dx.doi.org/10.1088/0967-3334/30/3/005.

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24

Londhe, Aboli N., and Mithilesh Atulkar. "Semantic segmentation of ECG waves using hybrid channel-mix convolutional and bidirectional LSTM." Biomedical Signal Processing and Control 63 (January 2021): 102162. http://dx.doi.org/10.1016/j.bspc.2020.102162.

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25

Xu, Xiaowen, Ying Liang, Pei He, and Junliang Yang. "Adaptive Motion Artifact Reduction Based on Empirical Wavelet Transform and Wavelet Thresholding for the Non-Contact ECG Monitoring Systems." Sensors 19, no. 13 (July 1, 2019): 2916. http://dx.doi.org/10.3390/s19132916.

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Electrocardiogram (ECG) signals are crucial for determining the health status of the human heart. A clean ECG signal is critical in analysis and diagnosis of heart diseases. However, ECG signals are often contaminated by motion artifact noise in the non-contact ECG monitoring systems. In this paper, an ECG motion artifact removal approach based on empirical wavelet transform (EWT) and wavelet thresholding (WT) is proposed. This method consists of five steps, namely, spectrum preprocessing, spectrum segmentation, EWT decomposition, wavelet threshold denoising, and EWT reconstruction. The proposed approach was used to process real ECG signals collected by the non-contact ECG monitoring equipment. The results of quantitative study and analysis indicate that this approach produces a better performance in terms of restorage of QRS complexes of the original ECG with reduced distortion, retaining useful information in ECG signals, and improvement of the signal to noise ratio (SNR) value of the signal. The output results of the practical ECG signal test show that motion artifact in the real recorded ECG is effectively filtered out. The proposed method is feasible for reducing motion artifacts from ECG signals, whether from simulation ECG signals or practical non-contact ECG monitoring systems.
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26

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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27

Seger, M., C. Hintermüller, G. Fischer, H. Mühlthaler, R. Modre-Osprian, B. Tilg, and B. Pfeifer. "AAM-based Segmentation for Imaging Cardiac Electrophysiology." Methods of Information in Medicine 46, no. 01 (2007): 36–42. http://dx.doi.org/10.1055/s-0038-1627829.

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Summary Objectives: Activation time (AT) imaging from electrocardiographic (ECG) mapping data has been developing for several years. By coupling 4-dimensional volume data (3D + time) the electrical sequence can be computed non-invasively. In this paper an approach for extracting the ventricular and atrial blood masses for structurally normal hearts by using cine-gated shortaxis data obtained via magnetic resonance imaging (MRI) is introduced. Methods: The blood masses are extracted by employing Active Appearance Models (AAMs). The ventricular blood masses are segmented, applying the AAMs after providing apex cordis and base of the heart in the volume data, whereas the more complex geometry of the atria requires a more specific attempt. On account of this the atrium was divided into three divisions of appearance, where the images of the volume data in the related divisions have a maximum affinity. The first division reaches from the base of the heart to initial visibility of the upper and left lower pulmonary vein. The second division up from there to the last occurrence and the third division from there to the end of the visibility of the right upper and lower pulmonary vein. After extracting the cardiac blood masses the result gets triangulated and remeshed for activation time imaging. Results: With this method the cardiac models of eight patients were extracted and the AT imaging approach was applied to single-beat ECG data of atrial and ventricular depolarization. Conclusion: The advantage of the proposed AAM approach is that only a few initial parameters have to be set. Therefore, the approach can be integrated into a processing pipeline that works semi-automatically. The extracted models can be used for further investigations.
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28

Bsoul, Abed Al Raoof K. "A novel ECG segmentation for compression using Fourier series approximation in e-health devices." International Journal of Electronic Healthcare 8, no. 2/3/4 (2015): 163. http://dx.doi.org/10.1504/ijeh.2015.075345.

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29

Khalaidzhi, Aleksei K. "RESEARCH OF THE CETLIN METHOD OF AUTOMATIC ARRHYTHMIA DETECTION BY ECG SIGNALS FROM MIT-BIH." АВТОМАТИЗАЦИЯ ПРОЦЕССОВ УПРАВЛЕНИЯ 63, no. 1 (2021): 98–109. http://dx.doi.org/10.35752/1991-2927-2021-1-63-98-109.

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This article presents and solves the problem of evaluating the quality of the Cetlin method, which classifies the sequence of RR-intervals by the recordings of ECG signals from MIT-BIH, which have labels on R-peaks. To solve this problem, author proposes new quality metrics and describes developed algorithms for calculating them in real time with taking into account segmentation errors. Author analyzes the influence of the accuracy of the segmentation procedure for extracting the positions of R-peaks from ECG signal on the proposed quality metrics. Paper compares the quality of the Cetlin method and other existing algorithms for arrhythmia detection that analyze the duration of RR-intervals in accordance with a set of rules in real time. Article reveals advantages and limitations of the method. Paper shows that the method successfully detects SVEB and VEB arrhythmias. but has inertia, that leads to false positives, and is immune to morphological abnormalities that do not change the duration of RR-intervals. Author analyzes the influence of parameters of the Cetlin method on its quality according to the proposed metrics. Paper describes the procedure for searching the best parameters configuration. In conclusion, author reveals that there is no the only configuration, that achieves the best quality for each signal from MIT-BIH.
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30

Li, Tianyu, Fangyan Dong, and Kaoru Hirota. "Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (March 20, 2015): 217–24. http://dx.doi.org/10.20965/jaciii.2015.p0217.

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A fuzzy association rule mining based method is proposed for myocardial ischemia diagnosis on ECG signals. The proposal provides interpretable and understandable information to doctors as an assistant reference, while rule mining on fuzzy itemsets guarantees that the feature segmentation before rule extraction is feasible and effective. A set of fuzzy association rules is mined through experiments on data from the European ST-T Database, and classification results of myocardial ischemia and normal heartbeats on the test dataset using the extracted rules obtained values of 83.4%, 80.7%, and 81.4% for sensitivity, specificity, and accuracy, respectively. The proposed method aims to become a helpful tool to accelerate the diagnosis of myocardial ischemia on ECG signal, and to be expanded to other heart disease diagnosis areas such as hypertensive heart disease and arrhythmia.
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31

Cuadros, Jhosmary, Nelson Dugarte, Sara Wong, Pablo Vanegas, Villie Morocho, and Rubén Medina. "ECG Multilead QT Interval Estimation Using Support Vector Machines." Journal of Healthcare Engineering 2019 (April 15, 2019): 1–14. http://dx.doi.org/10.1155/2019/6371871.

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This work reports a multilead QT interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including QRS detection as well as an accurate multilead QT interval detection algorithm using support vector machines (SVMs). Two fiducial points (Qini and Tend) are estimated using the SVM algorithm on each incoming beat. This enables segmentation of the current beat for obtaining the P, QRS, and T waves. The QT interval is estimated by updating the QT interval on each lead, considering shifting techniques with respect to a valid beat template. The validation of the QT interval measurement algorithm is attained using the Physionet PTB diagnostic ECG database showing a percent error of 2.60±2.25 msec with respect to the database annotations. The usefulness of this software tool is also tested by considering the analysis of the ECG signals for a group of 60 patients acquired using our digital electrocardiograph. In this case, the validation is performed by comparing the estimated QT interval with respect to the estimation obtained using the Cardiosoft software providing a percent error of 2.49±1.99 msec.
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32

Cai, Jing, Ge Zhou, Mengkun Dong, Xinlei Hu, Guangda Liu, and Weiguang Ni. "Real-Time Arrhythmia Classification Algorithm Using Time-Domain ECG Feature Based on FFNN and CNN." Mathematical Problems in Engineering 2021 (May 17, 2021): 1–17. http://dx.doi.org/10.1155/2021/6648432.

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To solve the problem of real-time arrhythmia classification, this paper proposes a real-time arrhythmia classification algorithm using deep learning with low latency, high practicality, and high reliability, which can be easily applied to a real-time arrhythmia classification system. In the algorithm, a classifier detects the QRS complex position in real time for heartbeat segmentation. Then, the ECG_RRR feature is constructed according to the heartbeat segmentation result. Finally, another classifier classifies the arrhythmia in real time using the ECG_RRR feature. This article uses the MIT-BIH arrhythmia database and divides the 44 qualified records into two groups (DS1 and DS2) for training and evaluation, respectively. The result shows that the recall rate, precision rate, and overall accuracy of the algorithm’s interpatient QRS complex position prediction are 98.0%, 99.5%, and 97.6%, respectively. The overall accuracy for 5-class and 13-class interpatient arrhythmia classification is 91.5% and 75.6%, respectively. Furthermore, the real-time arrhythmia classification algorithm proposed in this paper has the advantages of practicability and low latency. It is easy to deploy the algorithm since the input is the original ECG signal with no feature processing required. And, the latency of the arrhythmia classification is only the duration of one heartbeat cycle.
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33

Bazi, Yakoub, Mohamad M. Al Rahhal, Haikel AlHichri, Nassim Ammour, Naif Alajlan, and Mansour Zuair. "Real-Time Mobile-Based Electrocardiogram System for Remote Monitoring of Patients with Cardiac Arrhythmias." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 10 (December 31, 2019): 2058013. http://dx.doi.org/10.1142/s0218001420580136.

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In this study, we propose an electrocardiogram (ECG) system for the simultaneous and remote monitoring of multiple heart patients. It consists of three main components: patient, sever, and monitoring units. The patient unit uses a wearable miniature sensor that continuously measures ECG signals and sends them to a smart mobile phone via a Bluetooth connection. In the mobile device, the ECG signals can be stored, displayed on screen, and automatically transmitted to a distant server unit over the internet; the server stores ECG data from several patients. Health care stakeholders use a monitoring unit to retrieve the ECG signals of multiple patients at any time from the server for display and real-time automatic analysis. The analysis includes segmentation of the ECG signal into separate heartbeats followed by arrhythmia detection and classification. When compared to existing real-time ECG systems, where the detection of abnormalities is usually performed using simple rules, the proposed system implements a real-time classification module that is based on a support vector machine (SVM) classifier. Extensive experimental results on ECG data obtained from a TechPatientTM simulator, a real person, and 20 records from the MIT arrhythmia database are reported and discussed.
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Radia, Fandi, and Hadj Slimane Zine Eddine. "A new heart sounds segmentation approach based on the correlation between ECG and PCG signals." International Journal of Biomedical Engineering and Technology 29, no. 2 (2019): 174. http://dx.doi.org/10.1504/ijbet.2019.097304.

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Radia, Fandi, and Hadj Slimane Zine Eddine. "A new heart sounds segmentation approach based on the correlation between ECG and PCG signals." International Journal of Biomedical Engineering and Technology 29, no. 2 (2019): 174. http://dx.doi.org/10.1504/ijbet.2019.10018404.

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Micó, Pau, Margarita Mora, David Cuesta-Frau, and Mateo Aboy. "Automatic segmentation of long-term ECG signals corrupted with broadband noise based on sample entropy." Computer Methods and Programs in Biomedicine 98, no. 2 (May 2010): 118–29. http://dx.doi.org/10.1016/j.cmpb.2009.08.010.

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Sraitih, Mohamed, Younes Jabrane, and Amir Hajjam El Hassani. "An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques." Journal of Clinical Medicine 10, no. 22 (November 22, 2021): 5450. http://dx.doi.org/10.3390/jcm10225450.

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The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients.
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Al-dabag, Mohand Lokman Ahmad, Haider Th Salim ALRikabi, and Raid Rafi Omar Al-Nima. "Anticipating Atrial Fibrillation Signal Using Efficient Algorithm." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 02 (February 12, 2021): 106. http://dx.doi.org/10.3991/ijoe.v17i02.19183.

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One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients. Correct diagnosing of heart problem through examining the Electrocardiogram (ECG) signal will lead to prescribe the right treatment for a patient. This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat. The algorithm utilizes low computational cost techniques to segment the ECG signal. Then, useful pre-processing and feature extraction methods are suggested. Two classifiers, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), are separately used to evaluate the two proposed algorithms. The performance of the last proposed method with the two classifiers (SVM and MLP) show an improvement of about (19% and 17%, respectively) after using the proposed segmentation method so it became 96.2% and 97.5%, respectively.
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ARI, SAMIT, and GOUTAM SAHA. "ON A ROBUST ALGORITHM FOR HEART SOUND SEGMENTATION." Journal of Mechanics in Medicine and Biology 07, no. 02 (June 2007): 129–50. http://dx.doi.org/10.1142/s0219519407002200.

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The detection of heart diseases from heart sound signals needs an efficient segmentation algorithm to properly identify the location of the first and second heart sounds. This in turn helps in characterizing murmurs present in the cardiac cycles and the pathological condition by providing an appropriate time reference. The work presented here needs only the average heart rate as discrete auxiliary information that can be easily provided, unlike most of the methods which require the electrocardiography (ECG) signal as a continuous auxiliary signal in a complex setup. The algorithm was tested on 34 pathological cases and normal heart sound for a variety of sampling frequencies, recording environments, and age groups of subjects. It was found to give an overall accuracy of 95.51%. The robustness of the algorithm against additive white Gaussian noise contamination is also presented, and the noise immunity of various diseases for correct segmentation is established through this study.
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Yadav, Om Prakash, and Shashwati Ray. "Smoothening and Segmentation of ECG Signals Using Total Variation Denoising –Minimization-Majorization and Bottom-Up Approach." Procedia Computer Science 85 (2016): 483–89. http://dx.doi.org/10.1016/j.procs.2016.05.195.

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Bauer, R., B. Kraus, D. Bernhardt, J. Kerl, T. Lehnert, H. Ackermann, F. Vega-Higuera, and T. Vogl. "Computer-Based Automated Left Atrium Segmentation and Volumetry from ECG-Gated Coronary CT Angiography Data: Comparison with Manual Slice Segmentation and Ultrasound Planimetric Methods." RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 182, no. 12 (October 11, 2010): 1110–17. http://dx.doi.org/10.1055/s-0029-1245729.

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Golpaygani, Ali Tavakoli, Nahid Abolpour, Kamran Hassani, Kourosh Bajelani, and D. John Doyle. "Detection and identification of S1 and S2 heart sounds using wavelet decomposition method." International Journal of Biomathematics 08, no. 06 (October 15, 2015): 1550078. http://dx.doi.org/10.1142/s1793524515500783.

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Phonocardiogram (PCG), the digital recording of heart sounds is becoming increasingly popular as a primary detection system for diagnosing heart disorders and it is relatively inexpensive. Electrocardiogram (ECG) is used during the PCG in order to identify the systolic and diastolic parts manually. In this study a heart sound segmentation algorithm has been developed which separates the heart sound signal into these parts automatically. This study was carried out on 100 patients with normal and abnormal heart sounds. The algorithm uses discrete wavelet decomposition and reconstruction to produce PCG intensity envelopes and separates that into four parts: the first heart sound, the systolic period, the second heart sound and the diastolic period. The performance of the algorithm has been evaluated using 14,000 cardiac periods from 100 digital PCG recordings, including normal and abnormal heart sounds. In tests, the algorithm was over 93% correct in detecting the first and second heart sounds. The presented automatic segmentation algorithm using wavelet decomposition and reconstruction to select suitable frequency band for envelope calculations has been found to be effective to segment PCG signals into four parts without using an ECG.
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Abda, Sofiah Ishlakhul, Auli Damayanti, and Edi Winarko. "Detection of Heart Abnormalities Based On ECG Signal Characteristics using Multilayer Perceptron with Firefly Algorithm-Simulated Annealing." Contemporary Mathematics and Applications (ConMathA) 3, no. 1 (May 20, 2021): 45. http://dx.doi.org/10.20473/conmatha.v3i1.26941.

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Heart disease is one of the causes of death worldwide. Therefore, detecting heart disease is very important to reduce the increased mortality rate. One of the methods used to detect the abnormalities or disorders of the heart is to use computer assistance to determine the characteristics of an electrocardiogram. Electrocardiogram (ECG) is a test that detects and records the activity of the heart through small metal electrodes attached to the skin of one's chest, arms and legs. This test shows how fast the heart beats and whether the rhythm is stable or not. The purpose of this thesis is to apply a multi-layer perceptron model with firefly algorithm and simulated annealing in detecting cardiac abnormalities based on the ECG signal characteristics. The initial step of this research is image processing. The stages of ECG image processing are grayscale, thresholding, edge detection, segmentation and normalization processes. The results of this image processing are used as input matrices in the perceptron multilayer network training using firefly algorithm and simulated annealing. In the training process, we will get optimal weights and biases for validation tests on test data. The training data in this thesis uses 20 ECG images and in the validation test process uses 10 ECG images. The validation results in the validation test show that the accuracy in detecting heart abnormalities based on the characteristics of ECG signals using multi- layer perceptron with firefly algorithm and simulated annealing is 100%.
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Homaeinezhad, M. R., A. Ghaffari, H. Najjaran Toosi, R. Rahmani, M. Tahmasebi, and M. M. Daevaeiha. "Ambulatory Holter ECG individual events delineation via segmentation of a wavelet-based information-optimized 1-D feature." Scientia Iranica 18, no. 1 (February 2011): 86–104. http://dx.doi.org/10.1016/j.scient.2011.03.011.

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Zhu, Huaiyu, Yun Pan, Kwang-Ting Cheng, and Ruohong Huan. "A lightweight piecewise linear synthesis method for standard 12-lead ECG signals based on adaptive region segmentation." PLOS ONE 13, no. 10 (October 19, 2018): e0206170. http://dx.doi.org/10.1371/journal.pone.0206170.

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Hesar, Hamed Danandeh, and Maryam Mohebbi. "A Multi Rate Marginalized Particle Extended Kalman Filter for P and T Wave Segmentation in ECG Signals." IEEE Journal of Biomedical and Health Informatics 23, no. 1 (January 2019): 112–22. http://dx.doi.org/10.1109/jbhi.2018.2794362.

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Choi, Samjin, Mourad Adnane, Gi-Ja Lee, Hoyoung Jang, Zhongwei Jiang, and Hun-Kuk Park. "Development of ECG beat segmentation method by combining lowpass filter and irregular R–R interval checkup strategy." Expert Systems with Applications 37, no. 7 (July 2010): 5208–18. http://dx.doi.org/10.1016/j.eswa.2009.12.069.

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Ghaffari, A., M. R. Homaeinezhad, M. Khazraee, and M. M. Daevaeiha. "Segmentation of Holter ECG Waves Via Analysis of a Discrete Wavelet-Derived Multiple Skewness–Kurtosis Based Metric." Annals of Biomedical Engineering 38, no. 4 (January 20, 2010): 1497–510. http://dx.doi.org/10.1007/s10439-010-9919-3.

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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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Nasim, Amnah, Agnese Sbrollini, Micaela Morettini, and Laura Burattini. "Extended Segmented Beat Modulation Method for Cardiac Beat Classification and Electrocardiogram Denoising." Electronics 9, no. 7 (July 20, 2020): 1178. http://dx.doi.org/10.3390/electronics9071178.

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Beat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments; (2) wavelet-based time-frequency feature extraction; (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats; and (4) a template-based denoising procedure. ESBMM was tested using the MIT–BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p < 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings.
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