Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices

• URL: http://arxiv.org/abs/2307.08766v1
• Date: Mon, 17 Jul 2023 18:26:57 GMT
• Title: Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices
• Authors: Felipe M. Dias, Marcelo A. F. Toledo, Diego A. C. Cardenas, Douglas A. Almeida, Filipe A. C. Oliveira, Estela Ribeiro, Jose E. Krieger, Marco A. Gutierrez
• Abstract summary: Photoplethysmography is a non-invasive technology that measures changes in blood volume in the microvascular bed of tissue. Photoplethysmography allows for the assessment of parameters that can indicate conditions such as vasoconstriction or vasodilation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Photoplethysmography (PPG) is a non-invasive technology that measures changes in blood volume in the microvascular bed of tissue. It is commonly used in medical devices such as pulse oximeters and wrist worn heart rate monitors to monitor cardiovascular hemodynamics. PPG allows for the assessment of parameters (e.g., heart rate, pulse waveform, and peripheral perfusion) that can indicate conditions such as vasoconstriction or vasodilation, and provides information about microvascular blood flow, making it a valuable tool for monitoring cardiovascular health. However, PPG is subject to a number of sources of variations that can impact its accuracy and reliability, especially when using a wearable device for continuous monitoring, such as motion artifacts, skin pigmentation, and vasomotion. In this study, we extracted 27 statistical features from the PPG signal for training machine-learning models based on gradient boosting (XGBoost and CatBoost) and Random Forest (RF) algorithms to assess quality of PPG signals that were labeled as good or poor quality. We used the PPG time series from a publicly available dataset and evaluated the algorithm s performance using Sensitivity (Se), Positive Predicted Value (PPV), and F1-score (F1) metrics. Our model achieved Se, PPV, and F1-score of 94.4, 95.6, and 95.0 for XGBoost, 94.7, 95.9, and 95.3 for CatBoost, and 93.7, 91.3 and 92.5 for RF, respectively. Our findings are comparable to state-of-the-art reported in the literature but using a much simpler model, indicating that ML models are promising for developing remote, non-invasive, and continuous measurement devices.

Related papers

• Reliable Physiological Monitoring on the Wrist Using Generative Deep Learning to Address Poor Skin-Sensor Contact [14.10775902843149]
  Photoplethysmography is a widely adopted, non-invasive technique for monitoring cardiovascular health and physiological parameters in both consumer and clinical settings. While motion artifacts in dynamic environments have been extensively studied, suboptimal skin-sensor contact in sedentary conditions can distort waveform morphology, leading to the loss or misalignment of key features and compromising sensing accuracy. In this work, we propose CP-, a novel framework that transforms Contact Pressure-distorted PPG signals into high-fidelity waveforms with ideal morphology.
  arXiv  Detail & Related papers  (2025-04-03T16:22:15Z)
• Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models [45.94962431110573]
  Camera-based monitoring of vital signs, also known as imaging photoplethysmography (i), has seen applications in driver-monitoring, affective computing, and more. We introduce methods that combine signal processing and deep learning methods in an inverse problem.
  arXiv  Detail & Related papers  (2025-03-21T16:11:21Z)
• TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection [1.9518933807303265]
  Photoplethysmography ( PPG) sensors have been widely used in consumer wearable devices to monitor heart rates (HR) and heart rate variability (HRV) Despite the prevalence, PPG signals can be contaminated by motion artifacts induced from daily activities. We propose the Temporal Attentive U-Net, i.e., TAU, to accurately detect peaks from PPG signals.
  arXiv  Detail & Related papers  (2025-03-13T14:45:08Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [53.2981100111204]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring. Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors. In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type. Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers [43.17768785084301]
  We train an amortized neural posterior estimator on a newly built large dataset of cardiac simulations. We incorporate elements modeling effects to better align simulated data with real-world measurements. The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data.
  arXiv  Detail & Related papers  (2024-12-23T13:05:17Z)
• Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study [43.28613210217385]
  We employ and compare three state-of-the-art generative models to generate PCG data. Our results demonstrate that the generated PCG data closely resembles the original datasets. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs.
  arXiv  Detail & Related papers  (2024-12-17T18:07:40Z)
• Adversarial Contrastive Learning Based Physics-Informed Temporal Networks for Cuffless Blood Pressure Estimation [37.94387581519217]
  We introduce a novel physics-informed temporal network(PITN) with adversarial contrastive learning to enable precise BP estimation with very limited data. We then employ adversarial training to generate extra physiological time series data, improving PITN's robustness in the face of sparse subject-specific data.
  arXiv  Detail & Related papers  (2024-08-16T02:17:21Z)
• SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals [37.788535094404644]
  Atrial fibrillation (AF) significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography ( PPG) signals are susceptible to corruption from motion artifacts and other factors often encountered in ambulatory settings. We propose a novel deep learning model, designed to learn how to retain accurate predictions from partially corrupted PPG.
  arXiv  Detail & Related papers  (2024-04-15T01:07:08Z)
• How Suboptimal is Training rPPG Models with Videos and Targets from Different Body Sites? [40.527994999118725]
  Most current models are trained on facial videos using contact PPG measurements from the fingertip as targets/ labels. We show that neural models learn to predict the morphology of the ground truth PPG signal better when trained on the forehead.
  arXiv  Detail & Related papers  (2024-03-15T15:20:21Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling [11.617950008187366]
  Photoplethysmography ( PPG) is a cost-effective and non-invasive technique that utilizes optical methods to measure cardiac physiology. Here, we propose a subject-independent attention-based deep state-space model (ADSSM) to translate PPG signals to corresponding ECG waveforms.
  arXiv  Detail & Related papers  (2023-09-27T03:07:46Z)
• Personalised and Adjustable Interval Type-2 Fuzzy-Based PPG Quality Assessment for the Edge [0.1433758865948252]
  The presented system has the potential to enable ultra-low complexity and real-time PPG quality assessment. The proposed system obtained up to 93.72% for average accuracy during validation.
  arXiv  Detail & Related papers  (2023-09-23T19:35:00Z)
• Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach [39.36827689390718]
  We introduce a novel method, Geodesic-BP, to solve the inverse eikonal problem. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models.
  arXiv  Detail & Related papers  (2023-08-16T14:57:12Z)
• Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG [2.82697733014759]
  r (pg photoplethysmography) is a technology that measures and analyzes BVP (Blood Volume Pulse) by using the light absorption characteristics of hemoglobin captured through a camera. This study is to provide a framework to evaluate various r benchmarking techniques across a wide range of datasets for fair evaluation and comparison.
  arXiv  Detail & Related papers  (2023-07-24T09:35:47Z)
• Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning [16.79885220470521]
  Photoplethysmography (Photoplethysmography) refers to the measurement of variations in blood volume using light. Photoplethysmography signals provide insight into the body's circulatory system. Photoplethysmography signals can be employed to extract various bio-features, such as heart rate and vascular ageing.
  arXiv  Detail & Related papers  (2023-05-23T13:41:52Z)
• Real-time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation [9.883261192383612]
  Remote photo-plethysmography (r) uses a camera to estimate a person's heart rate (HR) HRV is a measure of the fine fluctuations in the intervals between heart beats. We introduce a refined and efficient real-time r pipeline with novel filtering and motion suppression.
  arXiv  Detail & Related papers  (2020-12-31T18:57:05Z)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.