Photoplethysmogram signal analysis for detecting vital physiological parameters: An evaluating study

Reguig, F. Bereksi

doi:10.1109/isivc.2016.7893981

Cited by 9 publications

(7 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PPG signal presents a quasi-periodic stereotyped waveform, commonly called PPG pulse, which occurs with each heartbeat [ 8 ]. Each PPG pulse can be divided into two phases: the anacrotic phase, which relates to the systolic heart contraction, and the catacrotic phase, which depends both on the diastolic heart phase and on the pulse wave reflected from the peripheral artery [ 9 ]. Within each PPG pulse, in ideal conditions, four fiducial points can be identified, as highlighted in Figure 1 : Systolic foot: the beginning of the systolic phase and the minimum of the pulse; Systolic peak: the most prominent maximum; Dicrotic notch: most visible in healthy young subjects, it is supposed to represent the closure of the aortic valve [ 10 ]; Diastolic peak: the second prominent maximum of the pulse.…”

Section: Introductionmentioning

confidence: 99%

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

Moscato

Giudice

Massaro

et al. 2022

Sensors

View full text Add to dashboard Cite

Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.

show abstract

Section: Introductionmentioning

confidence: 99%

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

Moscato

Giudice

Massaro

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Next, the decreased normalized radial pulse area1, radial pulse area2, and PPG pulse area2 have been observed in this study at the third trimester in comparison with the first trimester, expect the PPG pulse area1. It has been accepted that the pulse waveform area before the dicrotic notch point (pulse area1) mainly reflects the systolic characteristics, which is influenced by cardiac function, and the pulse waveform after the dicrotic notch point (pulse area2) mainly reflects the diastolic characteristics, which is affected by peripheral resistance and vessels compliance [12]. The increased ejection function and decreased peripheral resistance have been reported during pregnancy, resulting in the decreased pulse area1 and pulse area2 as observed in this study.…”

Section: Discussionmentioning

confidence: 99%

Arterial Pulse Waveform Characteristics Difference between the Three Trimesters of Healthy Pregnant Women

Zhang

Chi

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

During pregnancy, the pregnant mother undergoes significant physiological changes in order to accommodate the developing fetus. In recent years, arterial pulse wave has been widely used to reflect these physiological changes. The aim of this study was to investigate the changes of radial pulse and photoplethysmography (PPG) pulse waveform characteristic with gestational age in normal pregnant women. 40 pregnant women volunteers were recruited from February 2016 to September 2016 from the Haidian Maternal & Child Health Hospital in Beijing. Both radial pulses and PPG pulses were recorded simultaneously using a PowerLab data collection system at a sampling rate of 1000Hz for offline analysis. Their pulses were measured from each pregnant woman at three trimesters (first trimester between week 11-13; second trimester between week 20-22 and the third trimester between week 37-39). Three waveform characteristics (total pulse area; pulse area1: the area before the notch position; pulse area2: the area after the notch position) were derived. The results showed that the total pulse area and pulse area2 from both radial and PPG pulses decreased significantly between two paired consecutive trimesters (all P<0.01, except the comparisons between the second and third trimesters for PPG pulses). In summary, this study has quantified the pulse waveform characteristic differences in terms of pulse areas between the three trimesters, providing useful scientific evidence to better understand the cardiovascular physiological changes during normal pregnancy.

show abstract

“…The results are shown in the PC monitor with related medical indicators graphics such as the augmentation index (for artery stiffness), the BPM, etc. [ 18 , 19 ]. Figure 20 reports representative results for the robustness of the proposed pattern recognition algorithm as well as the developed MATLAB ® GUI.…”

Section: Testing and Validation Of The Proposed Methodsmentioning

confidence: 99%

“…Figure 5 reports the block-scheme of the processing pipeline applied to collected PPG signals. It is composed by four blocks: (a) PPG raw signal filter ( Figure 5 a), (b) the PPG pattern recognition system ( Figure 5 b), (c) medical indicators detection system ( Figure 5 c) and (d) indicators extraction stage (such as pulse wave velocity (PWV), pulse transit time (PTT), cardiovascular assessment stage (A1), artery stiffness, blood pressure measurement/monitoring, beats per minute (BPM) and so on) ( Figure 5 d) [ 18 , 19 ].…”

Section: The Ppg Detection Systemmentioning

confidence: 99%

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment

Rundo

Conoci

Ortis

et al. 2018

Sensors

122

115

View full text Add to dashboard Cite

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG “combo” pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting “clean” PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.

show abstract

Photoplethysmogram signal analysis for detecting vital physiological parameters: An evaluating study

Cited by 9 publications

References 29 publications

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

Arterial Pulse Waveform Characteristics Difference between the Three Trimesters of Healthy Pregnant Women

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment

Contact Info

Product

Resources

About