Adaptive threshold method for the peak detection of photoplethysmographic waveform

Shin, Hyunki; Lee, Chung-Keun; Lee, Myoungho

doi:10.1016/j.compbiomed.2009.10.006

Cited by 191 publications

(113 citation statements)

References 28 publications

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“…It can be efficiently implemented in a real time processing hardware for short term data, as the VPD threshold is adaptive and calculated locally using only three beats. Compared to the beat detection algorithm by M. Aboy et al [4], the algorithm presented in the paper exhibits better performance using the same validation database of M. Aboy et al, at all acceptance intervals, for both Se and +P values. There is a chance of increasing false positives when threshold coefficient is decreased, but it can be overcome by using inter-beat interval logic.…”

Section: Resultsmentioning

confidence: 95%

“…Fortunately, the authors Mateo Aboy et.al [4] have provided their validation dataset at http://bsp.pdx.edu to facilitate other developers to validate their beat detection algorithms. The database contains two PPG signals each of 60 minutes duration with expert annotated detections generated by two different experts (DT and JM).…”

Section: Validation Of Algorithmmentioning

confidence: 99%

“…Hangsik Shin et al proposed an adaptive threshold method for peak detection of PPG signals, which is based on processing of minimum and maximum amplitudes of signal along with time intervals. This method has a limitation that it cannot classify incident wave peak and reflected wave peak of PPG signal, when the reflected wave peak is large and close to the incident wave peak [4]. Bistra Nenova proposed an automatic algorithm for detecting pulse wave which is based on a seven-rule decision logic.…”

Section: Introductionmentioning

confidence: 99%

“…Fortunately, the peak detection algorithms for Electrocardiogram (ECG) signals are well developed and widely available [1]. Whereas, there are only a few publications that describe algorithms to detect features in pulse wave signals such as arterial pressure wave and photoplethysmographic (PPG) signals [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient and Automatic Systolic Peak Detection Algorithm for Photoplethysmographic Signals

Kuntamalla¹,

Reddy²

2014

IJCA

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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.

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Section: Resultsmentioning

confidence: 95%

Section: Validation Of Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient and Automatic Systolic Peak Detection Algorithm for Photoplethysmographic Signals

Kuntamalla¹,

Reddy²

2014

IJCA

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“…In their experiments, they obtained a sensitivity of 99.36% and a positive detection rate of 98.43%. Shin et al [15] mentioned that PPG-based temporal analyses were widely used as an analytical method for the diagnosis of physiological and cardiovascular diseases. They emphasized that many of the temporal approaches of PPG were based on peak points.…”

Section: Introductionmentioning

confidence: 99%

An innovative peak detection algorithm for photoplethysmography signals: an adaptive segmentation method

Kavsaoğlu¹,

Polat²,

Bozkurt³

2016

Turk J Elec Eng & Comp Sci

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Abstract:The purpose of this paper is twofold. The first purpose is to detect M-peaks from raw photoplethysmography (PPG) signals with no preprocessing method applied to the signals. The second purpose is to estimate heart rate variability (HRV) by finding the peaks in the PPG signal. HRV is a measure of the fluctuation of the time interval between heartbeats and is calculated based on time series between strokes derived from electrocardiogram (ECG), arterial pressure (AP), or PPG signals, separately. PPG is a method widely used to measure blood volume of tissue on the basis of blood volume change in every heartbeat. In the estimation of the HRV signal from the PPG signal, HRV is calculated by measuring the time intervals between the peak values in the PPG signal. In the present paper, a novel peak detection algorithm was developed for PPG signals. Finding peak values correctly from PPG signals, the HRV signal can be estimated. This peak detection algorithm has been called an adaptive segmentation method (ASM).In this method, the PPG signals are first separated into segments with sample sizes and then the peak points in these signals are detected by comparing with maximum points in these segments. To evaluate the estimated pulse rate and HRV signals from PPG, Poincaré plots and time domain features including minimum, maximum, mean, mode, standard deviation, variance, skewness, and kurtosis values were used. Our experimental results demonstrated that ASM could be even used both in the estimation of HRV signals and to detect the peaks from raw and noisy PPG signals without a pre-processing method.

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Non-contact Pulse Rate Measurement of Hand and Wrist Using RGB Camera

Low

Tuan

2018

Lecture Notes on Data Engineering and Communications Technologies

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Adaptive threshold method for the peak detection of photoplethysmographic waveform

Cited by 191 publications

References 28 publications

An Efficient and Automatic Systolic Peak Detection Algorithm for Photoplethysmographic Signals

An Efficient and Automatic Systolic Peak Detection Algorithm for Photoplethysmographic Signals

An innovative peak detection algorithm for photoplethysmography signals: an adaptive segmentation method

Non-contact Pulse Rate Measurement of Hand and Wrist Using RGB Camera

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