A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods

Desquins, Théo; Bousefsaf, Frédéric; Pruski, Alain; Maaoui, Choubeila

doi:10.3390/app12199582

Cited by 15 publications

(6 citation statements)

References 110 publications

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“…Machine learning-and deep learning-based methods for PPG-signal-quality assessment have recently attracted wider attention among researchers [22,23,[57][58][59]. These developments have been captured in a recent survey [60] that reviews signal-quality assessment methods for contact-based as well as imaging-based PPG. While the majority of works focus on developing a classifier model [58,[61][62][63] with binary inference for quality of the length of a PPG signal, a few recent works have proposed models that offer hightemporal-resolution-signal-quality assessment [20,22,23,64].…”

Section: Pre-processing and Signal Quality Assessmentmentioning

confidence: 99%

iBVP Dataset: RGB-Thermal rPPG Dataset with High Resolution Signal Quality Labels

Joshi,

Cho

2024

Electronics

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Remote photo-plethysmography (rPPG) has emerged as a non-intrusive and promising physiological sensing capability in human–computer interface (HCI) research, gradually extending its applications in health-monitoring and clinical care contexts. With advanced machine learning models, recent datasets collected in real-world conditions have gradually enhanced the performance of rPPG methods in recovering heart-rate and heart-rate-variability metrics. However, the signal quality of reference ground-truth PPG data in existing datasets is by and large neglected, while poor-quality references negatively influence models. Here, this work introduces a new imaging blood volume pulse (iBVP) dataset of synchronized RGB and thermal infrared videos with ground-truth PPG signals from ear with their high-resolution-signal-quality labels, for the first time. Participants perform rhythmic breathing, head-movement, and stress-inducing tasks, which help reflect real-world variations in psycho-physiological states. This work conducts dense (per sample) signal-quality assessment to discard noisy segments of ground-truth and corresponding video frames. We further present a novel end-to-end machine learning framework, iBVPNet, that features an efficient and effective spatio-temporal feature aggregation for the reliable estimation of BVP signals. Finally, this work examines the feasibility of extracting BVP signals from thermal video frames, which is under-explored. The iBVP dataset and source codes are publicly available for research use.

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Section: Pre-processing and Signal Quality Assessmentmentioning

confidence: 99%

iBVP Dataset: RGB-Thermal rPPG Dataset with High Resolution Signal Quality Labels

Joshi,

Cho

2024

Electronics

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“…For ensuring well-trained deep learning models, the learning dataset should be strictly cleaned by dropping outliers and noisy/deformed signals. Different quality assessment strategies are applied along with proper thresholds for excluding unaccepted signals/beats [104][105][106][107][108][109][110]. However, strict cleaning reduces the training data severely.…”

Section: Challenges and Limitationsmentioning

confidence: 99%

Video-based beat-by-beat blood pressure monitoring via transfer deep-learning

Omer,

Salah,

Hassan

et al. 2024

Appl Intell

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Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, continuously revealing new aspects characterizes that field as a rich research topic. It includes a diverse number of critical points represented in signal denoising, data cleaning, employed features, feature format, feature selection, feature domain, model structure, problem formulation (regression or classification), and model combinations. It is worth noting that extensive research efforts are devoted to utilizing different variants of machine learning and deep learning models while transfer learning is not fully explored yet. So, in this paper, we are introducing a per-beat rPPG-to-BP mapping scheme based on transfer learning. An interesting representation of a 1-D PPG signal as a 2-D image is proposed for enabling powerful off-the-shelf image-based models through transfer learning. It resolves limitations about training data size due to strict data cleaning. Also, it enhances model generalization by exploiting underlying excellent feature extraction. Moreover, non-uniform data distribution (data skewness) is partially resolved by introducing logarithmic transformation. Furthermore, double cleaning is applied for training contact PPG data and testing rPPG beats as well. The quality of the segmented beats is tested by checking some of the related quality metrics. Hence, the prediction reliability is enhanced by excluding deformed beats. Varying rPPG quality is relaxed by selecting beats during intervals of the highest signal strength. Based on the experimental results, the proposed system outperforms the state-of-the-art systems in the sense of mean absolute error (MAE) and standard deviation (STD). STD for the test data is decreased to 5.4782 and 3.8539 for SBP and DBP, respectively. Also, MAE decreased to 2.3453 and 1.6854 for SBP and DBP, respectively. Moreover, the results for BP estimation from real video reveal that the STD reaches 8.027882 and 6.013052 for SBP and DBP, respectively. Also, MAE for the estimated BP from real videos reaches 7.052803 and 5.616028 for SBP and DBP, respectively. Graphical abstract Proposed camera-based blood pressure monitoring system

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“…Factors leading to artifacts cannot be controlled, though it is often possible to perform signal quality assessment and eliminate the noisy segments from the analysis. While signal quality assessment for physiological signals is an active research field [22,[57][58][59][60][61], existing physiological sensing solutions do not offer provision of assessing signal quality, which can immensely increase validity of the analysis. Widely used methods for signal quality assessment include signal-to-noise ratio (SNR), template matching, and relative power signal quality index (pSQI) [61], along with recent machine learning-based approaches based on SVM classifier [60], LSTM [59], and 1D-CNN [57].…”

Section: Real-time Signal Quality Assessmentmentioning

confidence: 99%

PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies

Joshi,

Wang,

Cho

2023

Sensors

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The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit provides a one-stop pipeline consisting of (i) a sensing and data acquisition layer that can be configured in a modular manner per research needs, and (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study with 16 participants, PhysioKit shows strong agreement with research-grade sensors on measuring heart rate and heart rate variability metrics data. Furthermore, we report usability survey results from 10 small-project teams (44 individual members in total) who used PhysioKit for 4–6 weeks, providing insights into its use cases and research benefits. Lastly, we discuss the extensibility and potential impact of the toolkit on the research community.

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A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods

Cited by 15 publications

References 110 publications

iBVP Dataset: RGB-Thermal rPPG Dataset with High Resolution Signal Quality Labels

iBVP Dataset: RGB-Thermal rPPG Dataset with High Resolution Signal Quality Labels

Video-based beat-by-beat blood pressure monitoring via transfer deep-learning

PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies

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