Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder

Sudden cardiac arrest among young people is a recent worldwide risk, and it is noticed that people with cardiac arrhythmia are more susceptible to various heart diseases. Manual classification can be error-prone, and certainly, there is a need for automation to classify ECG signals to predict cardia...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 9230 - 23
Main Authors: Shah, Ameet, Singh, Dhanpratap, Mohamed, Heba G., Bharany, Salil, Rehman, Ateeq Ur, Hussen, Seada
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 18.03.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/166/985
692/4019/592
Algorithms
Arrhythmia
Arrhythmias, Cardiac - classification
Arrhythmias, Cardiac - diagnosis
Arrhythmias, Cardiac - physiopathology
Artificial Intelligence
Atrial fibrillation
Automation
Cardiac arrhythmia
Cardiovascular diseases
Classification
Deep learning classification
EKG
Electrocardiography
Electrocardiography - methods
Heart diseases
Humanities and Social Sciences
Humans
multidisciplinary
Neural networks
Neural Networks, Computer
Prediction
Science
Science (multidisciplinary)
Self-attention mechanism
Signal Processing, Computer-Assisted
Cardiac arrhythmia
Self-attention mechanism
Dense neural network
Atrial fibrillation
Kalman filter
Prediction
Deep learning classification
Artificial intelligence
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Sudden cardiac arrest among young people is a recent worldwide risk, and it is noticed that people with cardiac arrhythmia are more susceptible to various heart diseases. Manual classification can be error-prone, and certainly, there is a need for automation to classify ECG signals to predict cardiac arrhythmia accurately. The proposed self-attention artificial intelligence auto-encoder algorithm proved an effective cardiac arrhythmia classification strategy with a novel modified Kalman filter pre-processing. We achieved 24.00 SNRimp, 0.055 RMSE, 22.1 PRD% for -5db, 20.4 SNRimp, 0.0245 RMSE, 12 PRD% whereas 14.05 SNRimp, 0.010 RMSE, and 7.25 PRD%, which reduces the ECG signal noise during the pre-processing and improves the visibility of the QRS complex and R-R peaks of ECG waveform. The extracted features were used in network of neurons to execute the classification for MIT-BIH arrhythmia databases using the newly developed self-attention autoencoder (AE) algorithm. The results are compared with existing models, revealing that the proposed system outperforms the classification and prediction of cardiac arrhythmia with a precision of 99.91%, recall of 99.86%, and accuracy of 99.71%. It is confirmed that self-attention-AE training results are promising, and it benefits the diagnosis of ECGs for complex cardiac conditions to solve real-world heart problems.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-93906-5