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

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dc.authorid0000-0002-4380-9075
dc.authorid0000-0003-1840-9958
dc.authorid0000-0003-0673-4454
dc.contributor.authorKavsaoğlu, Ahmet Reşit
dc.contributor.authorPolat, Kemal
dc.contributor.authorBozkurt, Mehmet Recep
dc.date.accessioned2021-06-23T18:40:16Z
dc.date.available2021-06-23T18:40:16Z
dc.date.issued2016
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe 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, Poincare 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.en_US
dc.identifier.endpage1796en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-84963858668en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1782en_US
dc.identifier.trdizinid245330en_US
dc.identifier.urihttps://app.trdizin.gov.tr/makale/TWpRMU16TXdNQT09
dc.identifier.urihttps://hdl.handle.net/20.500.12491/3036
dc.identifier.volume24en_US
dc.identifier.wosWOS:000374121500076en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPeak Detectionen_US
dc.subjectHeart Rate Variability
dc.subjectPhotoplethysmography Signal
dc.subjectAdaptive Segmentation Method
dc.titleAn innovative peak detection algorithm for photoplethysmography signals: An adaptive segmentation methoden_US
dc.typeArticleen_US

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