The proposed self-attention autoencoder algorithm with modified Kalman filter pre-processing classified cardiac arrhythmia with 99.71% accuracy, 99.91% precision, and 99.86% recall.
Does a self-attention auto-encoder algorithm with modified Kalman filter pre-processing improve the classification and prediction of cardiac arrhythmia in ECG signals?
A novel self-attention auto-encoder algorithm with modified Kalman filter pre-processing achieved >99.7% accuracy in classifying cardiac arrhythmias from ECG signals, demonstrating potential for automated ECG analysis.
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.
Shah et al. (Tue,) conducted a other in Cardiac arrhythmia (n=47). Self-attention autoencoder (AE) algorithm with modified Kalman filter vs. Existing classification models and filters was evaluated on Classification accuracy for cardiac arrhythmia. The proposed self-attention autoencoder algorithm with modified Kalman filter pre-processing classified cardiac arrhythmia with 99.71% accuracy, 99.91% precision, and 99.86% recall.