Sleep Disorder Detection through PSD-Based Short-Time Frequency Analysis of Electrocardiogram Signal

Sleep problems are now the most frequent health issue people experience nowadays. According to studies, about 20% of the global population has some sort of sleep disturbance. As a result, lots of folks are popping Ambien to get some shut-eye. One in five patients has a sleep disturbance, as 20.3% of those who visit the doctor ask for sleep aids. As a result, mortality rates are rising not just in India but everywhere. Therefore, the intelligent computing approach has been developed to help both patients and hospitals deal with sleep disorders and the consequences that come from them. Many people's health can be improved by the development and implementation of an intelligent computing method for the identification of sleep disorders. The proposed approach incorporates widely used feature selection methods such as FFT and PCA, and compares their results to those obtained using TQWT to locate peaks in ECG signals. As measured by the sum of peaks extracted from ECG data, TQWT was shown to be more effective than both FFT and PCA. As a result, preventing sleep disorders from worsening makes diagnosis a top concern.