An Efficient and Automatic Systolic Peak Detection Algorithm for Photoplethysmographic Signals

Abstract

Processing of physiological signals often involves detection of peaks and finding intervals between them. Well developed methods are available for Electrocardiogram(ECG) QRS complex detection. However, there are only a few algorithms published for peak detection suitable for pulse wave signals such as arterial pressure wave and photoplethysmographic (PPG) signals. Algorithms for detection of QRS complex in ECG are based on the impulsive character of the signal and are not applicable for pulse wave signals, which are more sinusoidal in nature and the shape varies with age. In this background, a versatile algorithm based on the physiology of the pulse wave is developed to detect the peaks from a pulse wave signal such as PPG. The algorithm combines the technique of moving average of valley-peak differences with an adaptive threshold filtering to detect the systolic peaks. The algorithm is validated against a publicly available validation dataset and achieved a sensitivity of 99.82 and a positive predictivity of 98.88 when compared to expert manual annotations. This algorithm is computationally simple and can be easily implemented in real time processing hardware.

Frequency-Modulated Continuous Wave Radar Respiratory Pattern Detection Technology Based on Multifeature

“…In accordance with recognition of different respiratory patterns, experiments of 5 kinds of respiratory patterns, such as normal respiration, Biot's respiration, tachypnea, bradypnea and Cheyne–Stokes respiration, were designed and conducted separately [ 29 ]. The preprocessing and feature extraction were carried out afterwards: the valley to peak difference (VPD) peak-finding was utilized to extract the peak value, the valley value, and the difference between them [ 30 ]. After calculating the average and standard deviation of the normalized short-term energy [ 31 ], Hilbert–Huang transform (HHT) was taken advantage of to extract the average, standard deviation, and minimum of the instantaneous frequency [ 32 ].…”

“…e preprocessing and feature extraction were carried out afterwards: the valley to peak difference (VPD) peak-finding was utilized to extract the peak value, the valley value, and the difference between them [30]. After calculating the average and standard deviation of the normalized short-term energy [31], Hilbert-Huang transform (HHT) was taken advantage of to extract the average, standard deviation, and minimum of the instantaneous frequency [32].…”

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