Heart rate estimation from non-cardiovascular signals using slope sum function and Teager energy

Cardiovascular physiological signals such as electrocardiogram (ECG) and arterial blood pressure (ABP) provide direct measure of heart rate. But ECG & ABP recorded in the intensive care unit (ICU) are often seriously corrupted by noise and missing data, which lead to errors in Heart Rate (HR) estimation and incidences of false alarm from ICU monitors. Cardiac activity, because of its relatively high electrical energy, introduces ECG artifacts in non-cardiovascular physiological signals namely Electroencephalogram (EEG), Electrooculogram (EOG) and Electromyogram (EMG) recordings. This paper presents a HR estimation method from non-cardiovascular signals by detection of R-peaks of ECG artifacts in these signals using slope sum function, energy based function, and a novel Signal Quality Index (SQI) assessment technique. SQIs of non-cardiovascular physiological signals are obtained by correlation of Teager-Kaiser energy (TKE) of these signals with TKE of either ECG or ABP signal. This method was evaluated on PhysioNet database of challenge 2014. The average rMSE of HR estimate for EEG, EOG & EMG signal is 6.59 bpm, 4.20 bpm & 7.37 bpm respectively. The proposed method gives an accurate estimation of HR from non-cardiovascular signals and is quite useful when ECG or ABP signal are either corrupt or missing.