Advanced Deep Learning Models For Automated Detection Of Cardiac Arrhythmia And Atrial Fibrillation

Biomedical signals provide valuable data on the activities of many bodily systems. Biomedical signals are often non-stationary and exhibit non-linear behaviour. Hence, it is rather challenging to get useful data from these signals alone by observation in the temporal domain. Because of this, signal processing methods are used to extract crucial aspects from these signals in order to diagnose various illnesses. Automated methods that do not need human intervention and have a high rate of anomaly detection may help doctors with the diagnosis and treatment of a variety of ailments. Back in the day, machine learning was the go-to approach for bio signal analysis. Deep learning algorithms are currently being developed. Electrocardiogram (ECG) signals indicate that the ANS, which regulates the heart's regular rhythm, is in a state of active regulation. This ECG signal is non-stationary and non-linear, much like any other bio signal. An electrocardiogram (ECG) may reveal symptoms of a number of illnesses. There were significant variations in the examined models' accuracy, recall, precision, and F1 score, and CNN 1-RNN, CNN 1-LSTM, and CNN 1-GRU all performed differently. While CNN 2-RNN and CNN2-LSTM models showed clear advantages and disadvantages, CNN2-GRU showed a more even distribution of the two. The CNN7 Layer model nevertheless managed to show impressive results, even with these modifications. On the other hand, CNN 7-LSTM comes out as the most effective approach, with impressive results: 93.5% accuracy.