ECG-Derived Respiration Using a Real-Time QRS Detector Based on Empirical Mode Decomposition

Abstract

Respiration Rate (RR) is an important physiological indicator and plays a major role in health deterioration monitoring. Despite that, it has been neglected in hospital wards due to inadequate nursing skills and insufficient equipment. ECG signal, which is always monitored in a clinical setting, is modulated by respiration which renders it a highly enticing mean for the automatic RR estimation. In addition, accurate QRS detection is pivotal to RR estimation from the ECG signal. The investigation of QRS complexes is a continuing concern in ECG analysis because current methods are still inaccurate and miss heart beats. This paper presents a frequency domain RR estimation method which uses a novel real-time QRS detector based on Empirical Mode Decomposition (EMD). Another novelty of the proposed work stems from the RR estimation in the frequency domain as opposed to some of the current methods which rely on a time domain analysis. As will be shown later, the RR extraction in the frequency domain provides more accurate results compared to the time domain methods. Moreover, our novel QRS detector uses an adaptive threshold over a sliding window and differentiates large Q- from R-peaks, facilitating a more accurate RR estimation. The performance of our methods was tested on real data from Capnobase dataset. An average mean absolute error of less than 0.5 breath per minute was achieved using our frequency domain method, compared to 6 breaths per minute of the time domain analysis. Moreover, our modified QRS detector shows comparable results to other published methods, achieving a detection rate over 99.80%.