Photoplethysmography (PPG) is a non-invasive optical technique used to measure cardiovascular activity in both clinical and consumer applications. PPG signals are notoriously susceptible to noise and motion artefacts and in applications where high-frequency PPG signals are required, these issues are even more pronounced. In this work, we propose a two-stage algorithm to process these PPG waveforms without prior knowledge of their noise characteristics. To evaluate its performance, we utilized a set of PPG signal quality indices used to classify waveform quality. We found that using a Chebyshev type-II band-pass filter with cutoff frequencies of 0.35 Hz for high pass and 10 Hz for low pass performs best across six measured Signal Quality Indices (SQIs), achieving a 12% improvement in the dynamic time-warping (DTW) SQI. Furthermore, using a modified Savitzky-Golay smoothing algorithm with a frequency domain-derived window parameter leads to additional improvements in SQI-derived features of 14.09% and 12.22% in DTW and skewness respectively. Our proposed approach provides a universal solution for processing high-frequency raw PPG recordings and prepares them for the extraction of morphological features.

Optimal filtering and smoothing thresholds for high-frequency photoplethysmography signals

Hand foot and mouth disease (HFMD) is caused by a variety of enteroviruses, and occurs in large outbreaks in which a small proportion of children deteriorate rapidly with cardiopulmonary failure. Determining which children are likely to deteriorate is difficult and health systems may become overloaded during outbreaks as many children require hospitalization for monitoring. Heart rate variability (HRV) may help distinguish those with more severe diseases but requires simple scalable methods to collect ECG data.We carried out a prospective observational study to examine the feasibility of using wearable devices to measure HRV in 142 children admitted with HFMD at a children's hospital in Vietnam. ECG data were collected in all children. HRV indices calculated were lower in those with enterovirus A71 associated HFMD compared to those with other viral pathogens.HRV analysis collected from wearable devices is feasible in a low and middle income country (LMIC) and may help classify disease severity in HFMD. 

Feasibility of wearable monitors to detect heart rate variability in children with hand, foot and mouth disease

Abstract Cardiovascular disease (CVD) screening faces significant challenges in resource-limited settings, where infrastructure and computational constraints preclude the use of advanced remote assessment. These constraints are particularly acute for people living with HIV (PLWH), who experience elevated CVD risk yet often receive care in clinics without the capacity for specialist diagnostics. We evaluate pretrained physiological embeddings from foundation models for CVD detection using low-cost wearable photoplethysmography (PPG) signals from 80 PLWH outpatients in Ho Chi Minh City, Vietnam. We compare a strictly zero-shot approach (NormWear applied without any local training) with a more practical pipeline that uses frozen PaPaGei embeddings plus a locally trained classifier. The PaPaGei-embedding approach achieved superior discrimination (AUROC 0.769) compared with zero-shot NormWear (0.610), traditional PCA features (0.651), and established clinical scores, including the Framingham score (0.551) and the D:A:D-modified Framingham score (0.462). Without fine-tuning the foundation model itself, PaPaGei embeddings captured clinically coherent structure: patients on dolutegravir-based regimens clustered in low-risk regions, while those with high cholesterol variability occupied high-risk areas, consistent with cardiometabolic pathophysiology. These results show that pretrained physiological embeddings can enable accurate screening when combined with lightweight local calibration, reducing reliance on extensive feature engineering while preserving pathophysiological plausibility and actionable triage behaviour in the learned embeddings. This provides a practical approach for deploying foundation models in resource-constrained settings where training deep models may be infeasible. 

pdf/foundation-model-embeddings-enable-cardiovascular-screening-02b1cusybxya.pdf

Foundation model embeddings enable cardiovascular screening for people living with HIV in Vietnam using wearable signals

Close vital signs monitoring is crucial for the clinical management of patients with dengue. We investigated performance of a non-invasive wearable utilising photoplethysmography (PPG), to provide real-time risk prediction in hospitalised individuals. We performed a prospective observational clinical study in Vietnam between January 2020 and October 2022: 153 patients were included in analyses, providing 1353 h of PPG data. Using a multi-modal transformer approach, 10-min PPG waveform segments and basic clinical data (age, sex, clinical features on admission) were used as features to continuously forecast clinical state 2 h ahead. Prediction of low-risk states (17,939/80,843; 22.1%), defined by NEWS2 and mSOFA < 6, was associated with an area under the precision-recall curve of 0.67 and an area under the receiver operator curve of 0.83. Implementation of such interventions could provide cost-effective triage and clinical care in dengue, offering opportunities for safe ambulatory patient management. 

Predicting deterioration in dengue using a low cost wearable for continuous clinical monitoring

Electrocardiogram (ECG) and photoplethysmogram (PPG) are commonly used to determine the vital signs of heart rate, respiratory rate, and oxygen saturation in patient monitoring. In addition to simple observation of those summarized indexes, waveform signals can be analyzed to provide deeper insights into disease pathophysiology and support clinical decisions. Such data, generated from continuous patient monitoring from both conventional bedside and low-cost wearable monitors, are increasingly accessible. However, the recorded waveforms suffer from considerable noise and artifacts and, hence, are not necessarily used prior to certain quality control (QC) measures, especially by those with limited programming experience. Various signal quality indices (SQIs) have been proposed to indicate signal quality. To facilitate and harmonize a wider usage of SQIs in practice, we present a Python package, named vital_sqi, which provides a unified interface to the state-of-the-art SQIs for ECG and PPG signals. The vital_sqi package provides with seven different peak detectors and access to more than 70 SQIs by using different settings. The vital_sqi package is designed with pipelines and graphical user interfaces to enable users of various programming fluency to use the package. Multiple SQI extraction pipelines can take the PPG and ECG waveforms and generate a bespoke SQI table. As these SQI scores represent the signal features, they can be input in any quality classifier. The package provides functions to build simple rule-based decision systems for signal segment quality classification using user-defined SQI thresholds. An experiment with a carefully annotated PPG dataset suggests thresholds for relevant PPG SQIs.

vital_sqi: A Python package for physiological signal quality control

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vital_sqi: A Python package for physiological signal quality control