ECG-based machine-learning algorithms for heartbeat classification

Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals,...

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Bibliographic Details
Published in:Scientific reports 2021-09, Vol.11 (1), p.18738-18738, Article 18738
Main Authors: Aziz, Saira, Ahmed, Sajid, Alouini, Mohamed-Slim
Format: Article
Language:English
Subjects:
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Algorithms
Cardiovascular diseases
Classification
Coronary artery disease
EKG
Electrocardiography
Electroencephalography - methods
Heart diseases
Heart Rate
Humanities and Social Sciences
Humans
Learning algorithms
Machine Learning
multidisciplinary
Neural Networks, Computer
Patients
Science
Science (multidisciplinary)
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Summary:Electrocardiogram (ECG) signals represent the electrical activity of the human hearts and consist of several waveforms (P, QRS, and T). The duration and shape of each waveform and the distances between different peaks are used to diagnose heart diseases. In this work, to better analyze ECG signals, a new algorithm that exploits two-event related moving-averages (TERMA) and fractional-Fourier-transform (FrFT) algorithms is proposed. The TERMA algorithm specifies certain areas of interest to locate desired peak, while the FrFT rotates ECG signals in the time-frequency plane to manifest the locations of various peaks. The proposed algorithm’s performance outperforms state-of-the-art algorithms. Moreover, to automatically classify heart disease, estimated peaks, durations between different peaks, and other ECG signal features were used to train a machine-learning model. Most of the available studies uses the MIT-BIH database (only 48 patients). However, in this work, the recently reported Shaoxing People’s Hospital (SPH) database, which consists of more than 10,000 patients, was used to train the proposed machine-learning model, which is more realistic for classification. The cross-database training and testing with promising results is the uniqueness of our proposed machine-learning model.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-97118-5