Machine Learning based Cardiac Arrhythmia detection from ECG signal

Subramanian, Kavya; Prakash, N. Krishna

doi:10.1109/icssit48917.2020.9214077

Cited by 27 publications

(6 citation statements)

References 9 publications

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“…Subramanian and Prakash [ 114 ] analyzed heart diseases categorized as arrhythmia based on Electrocardiogram (ECG). ECG records from the MIT-BIH database of different disease conditions were investigated.…”

Section: Resultsmentioning

confidence: 99%

“… Rahman et al [ 113 ] The proposed CNN classification model achieved an overall classification accuracy of 95.2% with an average precision and recall of 95.2% and 95.4% that significantly outperforms the identified in state-of-the-art methods. N/D Subramanian et al [ 114 ] The performance of proposed SVM model was an accuracy of 91% with precision recall and F1 score of about 0.906593. N/D Wang et al [ 115 ] The results demonstrated that the proposed method had high performance for arrhythmia detection.…”

Section: Discussionmentioning

confidence: 99%

“… 2-Lead ECG ML, kNN Patient-independent Arrhythmia Rahman et al [ 113 ] 2020 Nashville (USA) Conference Proceedings 48 records of two channels ECG signals collected from 47 individuals obtained from MIT-BIH Arrhythmia [ 48 ] database DP 1-D CNN classification model was proposed for the automatic ECG heartbeat for five distinct types of cardiac arrhythmia. ECG sensors CNN Patient-specific Arrhythmia Subramanian et al [ 114 ] 2020 Coimbatore (India) Conference Proceedings MIT-BIH database [ 48 ] This work proposed an SVM based solution that classifies ECG data into types of arrhythmias. ECG sensors SVM Patient-specific Arrhythmia Wang et al [ 115 ] 2020 Shanghai (China) Journal article MIT-BIH arrhythmia [ 48 ] database (25 men and 22 women) A dual fully connected neural network model for accurate classification of heartbeats was presented.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Denysyuk¹,

Pinto²,

Silva³

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Denysyuk¹,

Pinto²,

Silva³

et al. 2023

Heliyon

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“…It would aid medical professionals in early disease diagnosis and appropriate treatment administration to lower the risk of problems. Researchers employ a variety of machine learning methods, including Deep Neural Network [21,28], Convolutional Neural Network [34], Decision Tree [20,31], logistic regression [30], SVM [16,26,27,32], K-NN [15], ANN [17] etc. for the detection of cardiac arrhythmia.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Cardiac Arrhythmia Detection: A Systematic Survey

Singh,

Agarwal,

Kaur

et al. 2023

J. Phys.: Conf. Ser.

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A significant global worry is a substantial rise in cardiac arrhythmia cases brought on by improper food and lifestyle choices. Manual analysis of the report of the electrocardiogram to detect the presence of an anomaly is a time-consuming task. Hence, it is necessary to create an automated diagnosis system that can deliver results quickly and accurately. Numerous machine learning-based models were created by researchers working in this field to determine the severity of cardiac arrhythmias. This article provides an organized and thorough assessment of previous research in the field, with a particular emphasis on machine learning methods developed by different authors to detect cardiac arrhythmia. Additionally, covered is the performance analysis of the different algorithms. The difficulties associated with developing a model for cardiac arrhythmia and its potential future impact are finally examined in the conclusion section.

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“…Classification is usually performed using two or five classes of arrhythmias. For classical methods, SVM classifiers [1][2][3] combined with genetic algorithms [4], Wavelet Transform (WT) [5], or Discrete Wavelet Transform [6,7] have been used for most of them. The evaluation metric was most often Accuracy (ACC), for which the results took values of 91-93% up to five classes of arrhythmias and above five classes, with values between 95.92 and 99.66%.…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Using Orthogonal Matching Pursuit and Machine Learning

Śmigiel

2022

Sensors

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Health monitoring and related technologies are a rapidly growing area of research. To date, the electrocardiogram (ECG) remains a popular measurement tool in the evaluation and diagnosis of heart disease. The number of solutions involving ECG signal monitoring systems is growing exponentially in the literature. In this article, underestimated Orthogonal Matching Pursuit (OMP) algorithms are used, demonstrating the significant effect of concise representation parameters on improving the performance of the classification process. Cardiovascular disease classification models based on classical Machine Learning classifiers were defined and investigated. The study was undertaken on the recently published PTB-XL database, whose ECG signals were previously subjected to detailed analysis. The classification was realized for class 2, class 5, and class 15 cardiac diseases. A new method of detecting R-waves and, based on them, determining the location of QRS complexes was presented. Novel aggregation methods of ECG signal fragments containing QRS segments, necessary for tests for classical classifiers, were developed. As a result, it was proved that ECG signal subjected to algorithms of R wave detection, QRS complexes extraction, and resampling performs very well in classification using Decision Trees. The reason can be found in structuring the signal due to the actions mentioned above. The implementation of classification issues achieved the highest Accuracy of 90.4% in recognition of 2 classes, as compared to less than 78% for 5 classes and 71% for 15 classes.

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Machine Learning based Cardiac Arrhythmia detection from ECG signal

Cited by 27 publications

References 9 publications

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

Machine Learning for Cardiac Arrhythmia Detection: A Systematic Survey

ECG Classification Using Orthogonal Matching Pursuit and Machine Learning

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