Sleep Apnea Diagnosis by DWT-Based Kurtosis, Radar and Histogram Analysis of Electrocardiogram

Abstract

ABSTRACT Sleep Apnea is a serious breathing disorder that occurs when a person's breathing is interrupted during sleeping. People with sleep Apnea stop breathing repeatedly during their sleep, which means that the brain and rest of the body may not get enough oxygen. If left untreated, sleep Apnea can result in a number of health problems, including high blood pressure, stroke, heart failure, irregular heartbeats, heart attacks, and depression. This paper deals with sleep Apnea assessment by discrete wavelet-transformation-(DWT)-based kurtosis, radar and histogram analysis of electrocardiogram (ECG) signals. ECG signals are de-noised by passing them through well-known Savitzky–Golay FIR filter and then are decomposed at different DWT levels, and kurtosis of approximate and detailed coefficients at different DWT levels is measured. Kurtosis at different levels is compared for a healthy person and Apnea patients. Then, radars are formed by kurtosis and compared. Histogram analysis is done on both ECG signals and obtained kurtosis. The comparative study shows that up to DWT level-4, kurtosis of approximate coefficients of Apnea patients is lower than that of a healthy person. However, kurtosis of the approximate coefficient for Apnea patients is greater than that of a normal person at DWT level-7. Up to level 6, Kurtosis of detailed coefficients for Apnea patients is less than that of a normal person. Radar shapes and histogram peaks of ECG signal and kurtosis are also different between a normal person and Apnea patients. Probability in terms of a “p” value for Kurtosis at optimized DWT levels for Apnea patients has shown satisfactory outcome.