Signal processing methods for robust heart rate estimation from multimodal Physiological signals

Abstract

quotCardiovascular diseases are the major cause of world wide mortality. Heart rate (HR) and heart rate variability (HRV) are important health parameters to monitor functioning of heart of cardiac patients. Multimodal physiological signals namely; Electrocardiogram, Arterial Blood newlinePressure, Photoplethysmogram, Electroencephalogram, Electrooculogram, Electromyogram etc. are recorded in ICU for close monitoring of vital health parameters of critically ill patients. newline newlineHowever, Electrocardiogram (ECG), that provides direct measure of heart rate, is often corrupted by noise or is missing and the heart rate estimated from such signals would be erroneous. Thus, there is a need for development of methods for robust heart rate estimation especially when ECG is either noisy or missing. This thesis investigates the development newlineof appropriate signal processing techniques for robust heart rate estimation from fusion of cardiovascular signals with non-cardiovascular (NC) signals that are not related to cardiac activities newlinebut contain some markers of heart beats. The signals used in proposed study are ECG, Arterial Blood Pressure (ABP), Electroencephalogram (EEG), Electro-oculogram (EOG) and Electromyogram (EMG). newline newlineA novel slope sum function and Teager-Kaiser Energy (SSF-TKE) method is developed for ECG artifacts detection in NC signals. It requires neither additional ECG channel nor a priori user input. Results from evaluation on standard databases have shown that SSF-TKE method is a highly effective technique for R-peak artifacts detection in non-cardiovascular signals contaminated with ECG artifacts. newline newlineThe use of SSF-TKE method is then explored in R-peak detection in ECG signal. SSFTKE is a simple method for R-peak detection that does not consider detail morphology of ECG, except steep slopes, amplitude and periodicity of QRS complex. This method has achieved excellent R-peak detection performance across a number of standard databases with newlinevariety of signal morphology.