Advanced OptiDLCardioNet-Based Cardiac Arrhythmia Detection Model from ECG Signals

Heart disease is regarded as one of the most significant health problems dealing by people today. Approximately, cardiovascular disease (CVD) affects around 50 million people. Electrocardiogram (ECG) signals are more vital for identifying and keeping track of individuals with various CVDs. In order to detect different types of arrhythmias, this papers proposed a novel optimization driven deep learning model for cardiac arrhythmia detection, termed as OptiDLCardioNet. To improve and smoothen the ECG signal, a cascaded wavelet augmented Kalman (CWAK) filtering approach is first applied. Next, an Adaptive Position aware Black-winged SqueezeNet (APBWSqueezeNet) model is used for feature extraction. In order to classify the signals for arrhythmia disease diagnosis, the extracted features are input into an Enhanced Dilated Height-Width Axial Attention Convolutional Network (EDilBW-HWAACNet). Moreover, the hyper-parameters of the EDilBW-HWAACNet are adjusted through the application of the Improved Walrus Optimization Algorithm (IWOA). The MIT-BIH arrhythmia database is used to validate the performance of OptiDLCardioNet model. Rendering to the experimental results, the OptiDLCardioNet model is able to achieve high classification accuracy of 99.82%, which is superior to existing methods with fewer significant features.