Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder
Ameet Shah, Dhanpratap Singh, Heba G. Mohamed, Salil Bharany, Ateeq Ur Rehman, Seada Hussen
Abstract
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.