Recurrent Neural Network Strategies for Classifying Cardiac Ailments from ECG Signal

The advancement of intelligent healthcare systems has brought a lot of improvement on classification of cardiac diseases based on electrocardiogram data. ECG signal categorization is an ideal application of RNNs especially because they outcompete other modes of NNs in handling sequences. Some of the cardiac diseases which can be accurately diagnosed and distinguished from each other using the above mentioned sophisticated RNN models are arrhythmia, atrial fibrillation, and heart failure. These models use deep learning architectures to capture the intricacies of temporal structure in EKG recording without any human imposed hierarchy of features requiring feature extraction from EKG recording data. To accomplish the goals of noise reduction and signal decomposition, the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) and other signal preprocessing techniques are applied. Overall, the current best approach to text-to-spatial data search lives in the Convolutional Neural Networks (CNN) category. Analyzing the results of the experiments, it can be concluded that these proposed RNN-based models showed higher accuracy, sensitivity, and specificity compared to the previous methods. This implies they could be of interest in diagnosing and also in following up certain heart afflictions in real time.