Beat detection algorithm for ECG and arterial blood pressure waveforms using empirical mode decomposition: a unified approach

Abstract

Beat detection algorithms for electrocardiogram (ECG) and pressure signals are extremely important for clinical applications. The accurate detection of R–peak locations in ECG signal is extremely important for its further processing with regard to cardiac health monitoring and for Heart Rate Variability (HRV) studies. Beat detection algorithms for pressure signals have many clinical applications including pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring. This paper presents a reliable unified algorithm for R–peak detection in ECG and systolic peak detection in Arterial Blood Pressure (ABP) signal using empirical mode decomposition. The proposed beat detection algorithm was tested on different data records of MIT–BIH arrhythmia database, Fantasia database, self–recorded signals and MIMIC database. The proposed algorithm achieved an overall sensitivity Se = 99.98% and a positive predictivity +P = 99.89% for ECG signals and an overall sensitivity Se = 99.58% and a positive predictivity +P = 99.85% for ABP signals.