Advanced Algorithm for Cardiac Arrhythmia Classification and Efficient Detection of Abnormalities in ECG Signals

High-Performance Algorithm for Cardiac Arrhythmias Classification and ECG Abnormality Detection Aspecific heart rhythms that are not found upon initial examinations, often lead to heart failure and stroke. Arrhythmias have to be diagnosed early through precise classification of ECG signals. Conventional methods employ ineffective and tedious manual intervention in interpreting the time series and the filtering of noise. To boot, we apply intricate deep learning and the Superlet Transform (SLT) for detection’s automation and enhancement. A superior time-frequency analysis tool, the SLT, transforms 1D ECG signals into 2D spectrograms to identify transient oscillation phenomena such as arrhythmias with higher resolution than STFT and CWT. SLT spectrograms are classified by deep Convolutional Neural Networks. This duty is accomplished by some advanced CNN models such as AlexNet, GoogLeNet, DenseNet-201.Due to the flexibility of information flow and gradient propagation in DenseNet-201, it achieves a high accuracy of classification. There are three classes of ECG signals, namely, healthy heart, atrial fibrillation, and ventricular fibrillation. The model is tested and demonstrated in various ECG database such as MIT-BIH arrhythmia and AF termination challenge. This method simplifies the hardware by the removal of various noise reduction processes and enables real-time monitoring of heart rates. The utilization of SLT and deep CNN models for early detection of cardiac arrhythmia is current trends that enhance clinical diagnosis and accurate results.