Digital processing of ECG and PPG signals for study of arterial parameters for cardiovascular risk assessment

Dutt, D. Narayana; Shruthi, S.

doi:10.1109/iccsp.2015.7322766

Cited by 27 publications

(18 citation statements)

References 3 publications

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“…As future work, we would like to investigate other anthropometric features, such as dyslipidemia and smoking habits, integrating PPG data with data coming from structured electronic-health-record datasets. Furthermore, we will investigate other features extracted from the PPG signal, such as those proposed in [30] to compute the diastolic and systolic pressure, and in [31,32] to analyze the ECG waveform. Our expectation is that research on this new ML approach based on PPG features will be empowered by the proposed work.…”

Section: Discussionmentioning

confidence: 99%

Learning-Based Screening of Endothelial Dysfunction From Photoplethysmographic Signals

et al. 2019

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Endothelial-Dysfunction (ED) screening is of primary importance to early diagnosis cardiovascular diseases. Recently, approaches to ED screening are focusing more and more on photoplethysmography (PPG)-signal analysis, which is performed in a threshold-sensitive way and may not be suitable for tackling the high variability of PPG signals. The goal of this work was to present an innovative machine-learning (ML) approach to ED screening that could tackle such variability. Two research hypotheses guided this work: (H1) ML can support ED screening by classifying PPG features; and (H2) classification performance can be improved when including also anthropometric features. To investigate H1 and H2, a new dataset was built from 59 subject. The dataset is balanced in terms of subjects with and without ED. Support vector machine (SVM), random forest (RF) and k-nearest neighbors (KNN) classifiers were investigated for feature classification. With the leave-one-out evaluation protocol, the best classification results for H1 were obtained with SVM (accuracy = 71%, recall = 59%). When testing H2, the recall was further improved to 67%. Such results are a promising step for developing a novel and intelligent PPG device to assist clinicians in performing large scale and low cost ED screening.

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Section: Discussionmentioning

confidence: 99%

Learning-Based Screening of Endothelial Dysfunction From Photoplethysmographic Signals

et al. 2019

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“…In particular, ECG and PPG signals are widely used for the evaluation of cardiovascular function. Characteristics of PPG waveforms such as amplitude, peak-peak time, and dicrotic notch were used for blood pressure estimation [23][24][25][26][27], hypertension assessment [28], and cardiovascular risk evaluation [29]. Since noise-free signals are very important when using the feature points of a biosignal, silicon photomultipliers (SiPMs) were adopted instead of the conventional photodiode (PD) [30] to obtain clear PPG waveforms.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Brachial–Ankle Pulse Wave Velocity and Heart–Finger Pulse Wave Velocity in Korean Adults

Cho

Baek

2020

Sensors

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Arterial stiffness is considered an index of vascular aging. The brachial–ankle pulse wave velocity (baPWV) method is widely used because of its proven effectiveness; and the pulse wave velocity measurement method using both electrocardiogram (ECG) and photoplethysmogram (PPG) is actively being studied due to the convenience of measurement and the possibility of miniaturization. The aim of this study was to evaluate and compare the effects of age and gender in Korean adults using both the baPWV method and the PWV method with ECG and finger PPG (heart–finger PWV). The measurements have been carried out for 185 healthy subjects of Korean adults, and the results showed that the baPWV was highly correlated with age in both genders (r = 0.94 for both males and females). However, the correlation values in heart–finger PWV measurement were significantly lower than those of baPWV (r = 0.37 for males and r = 0.71 for females). Although the heart–finger PWV method is suitable for mobile applications because it can be easily miniaturized while maintaining its signal quality, these results show that the heart–finger PWV method is not as effective as baPWV at evaluating the arterial stiffness.

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“…In [ 9 , 10 ] the authors have proposed a reconstruction pipeline for PPG signals based on usage of the discrete wavelet transform (DWT) in combination of EMD methodology. In [ 11 , 12 ] a pipeline based on usage of step-size and adaptive Least Mean Squares (LMS) filters for removing motion artifacts in PPG signals is described. In [ 10 ] the authors provided a full PPG signal analysis pipeline for Pulse Wave Velocity (PWV) measurements as medical indicators for cardiovascular risk assessment.…”

Section: Ppg Physiological Analysis and Recognition: Description Amentioning

confidence: 99%

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

Rundo

Conoci

Ortis

et al. 2018

Sensors

124

115

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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.

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Digital processing of ECG and PPG signals for study of arterial parameters for cardiovascular risk assessment

Cited by 27 publications

References 3 publications

Learning-Based Screening of Endothelial Dysfunction From Photoplethysmographic Signals

Learning-Based Screening of Endothelial Dysfunction From Photoplethysmographic Signals

A Comparative Study of Brachial–Ankle Pulse Wave Velocity and Heart–Finger Pulse Wave Velocity in Korean Adults

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

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