Sleep Disorder Detection using Fully Convolutional Neural Networks for Sleep Arrhythmia Analysis

One of the most vital parts of the human body is the heart, which circulates blood throughout the body and transports oxygen, nutrition, and waste products. However, the shift in lifestyle and environmental factors results in an aberrant heart's ability to beat. Cardiovascular diseases (CVDs) are the leading cause of death worldwide and the most prominent health concern today, impacting people of all ages. Heart and blood vascular illnesses are grouped as CVDs. The two primary subtypes of cardiac arrhythmias (CAs), a category of cardiovascular diseases (CVDs), are atrial and ventricular. According to WHO estimates, around 61% of individuals globally have CVD. In percentage terms, the disease affects 15%, 10%, 5%, and 5% of the population. The benefits of a wavelet-based VS method are merged with WF in the hybrid VS/WF technique. EMG interference and power-line interference are two examples of noise sources used to gauge the efficacy of the hybrid VS/WF technique. Numerous quality indicators are also looked at. VS/WF hybrid's performance is compared to well-known thresholding techniques including Visu Shrink, Global SURE Shrink, and hybrid threshold approach. The latter of the three threshold techniques Hybrid, Global SURE Shrink, and Visu Shrink is the best.In order to evaluate how well TNN works when supervised learning techniques are applied, three optimization approaches Gauss-Newton, Newton Raphson, and Leven berg Marquard are used. The de-noised ECG data undergo additional processing in order to extract characteristics. A number of domains, including Time, Frequency, and Time-Frequency (Wavelet) domains, are used to extract the characteristics. Auto-regressive (AR) coefficients are extracted in the time domain. While relative wavelet energy is extracted in the wavelet domain at various decomposition levels, Power Spectral Density (PSD) values are recovered in the frequency domain. These characteristics are used to construct an Artificial Neural Network (ANN) that is fully connected and has an accuracy performance rating of (96.85%) for classifying arrhythmias.A more effective de-noising, feature extraction, and classification model based on Conventional Neural Networks (CNN) are also developed. Compared to ANN, the performance is judged as having (99.2%) accuracy. Therefore, the suggested CNN model is helpful to physicians in reaching the ultimate diagnosis of atrial fibrillation (AFIB), atrial flutter (AFL), and ventricular fibrillation. It incorporates de-noising, feature extraction, and classification VT with Ventricular Fibrillation (VFL).