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

Currently a rarity in high-income countries, tetanus is a diagnosis not to miss. Deaths from tetanus fell by almost 90% between 1990 and 2019, largely reflecting the success of the World Health Organization’s Maternal and Neonatal Tetanus elimination campaign. However, deaths amongst children and adults have plateaued, and tetanus remains an important vaccine-preventable cause of morbidity and mortality, notably in South Asia, Southeast Asia, and Sub-Saharan Africa. Tetanus results from infections with spore-forming Clostridium tetani bacteria, usually acquired via contaminated wounds and burns. C. tetani releases a potent neurotoxin, causing muscle spasms, rigidity, and dysautonomia. Important complications include laryngeal spasm, leading to airway obstruction and respiratory arrest; nosocomial infections; and sequelae of prolonged immobility. Tetanus is a clinical diagnosis, but microbiological tests may serve as useful adjuncts. Treatment is multifaceted, requiring source control, antibiotic therapy, and antitoxin administration. With prolonged, quality intensive care, many patients survive with good functional outcome. However, due to challenges in leveraging routinely-collected healthcare data and performing high-quality trials in resource-constrained settings, several key questions remain unanswered and optimal treatment strategies are contested. In this review, we provide a state-of-the-art summary of global tetanus epidemiology; its clinical features and differential diagnosis; principles of management; and prognosis.

Tetanus: recognition and management

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

Abstract Background Cardiovascular events (CEs) remain the leading cause of death in patients with tetanus. We examined the incidence, patterns, and associated predictors of CEs among patients with tetanus in Vietnam. Methods An ambidirectional cohort study was conducted on hospitalized adult patients with tetanus at the Hospital for Tropical Diseases between 2019 and 2020. Information on demographics, tetanus disease, CEs and outcomes were collected. Results Among all 572 included patients, CEs accounted for 10.8% (95%CI 8.6–13.7%) and included Takotsubo cardiomyopathy (40.3%, 95%CI 29.0–52.8%), arrhythmia (19.4%, 95%CI 11.4–30.9%), sudden cardiac arrest (16.1%, 95%CI 9.0–27.2%), myocardial infarction (11.3%, 95%CI 5.6–21.5%), heart failure (6.5%, 95%CI 2.5–15.4%) and pulmonary embolism (6.5%, 95%CI 2.5–15.4%). CEs occurred from day 5 to 20 of illness. Among 62 CE patients, 21% (95%CI 12.7–32.6%) died and 61.3% (95%CI 48.9–72.4%) developed autonomic nervous system dysfunction (ANSD). Three-fourths (24/32) of patients with Takotsubo cardiomyopathy or myocardial infarction had ANSD. CEs were significantly associated with modified Ablett scores (AOR = 2.42, 95%CI 1.1–5.6, P = .04), underlying diseases (AOR = 2.7, 95%CI 1.1–6.8, P = .04) and overweight (AOR = 0.18, 95%CI .04–.8, P = .02). Conclusions CEs are not rare and associated with high mortality. The most common CE is Takotsubo cardiomyopathy. CEs can occur at any stage of illness, with or without ANSD. To prevent mortality, it is pivotal to screen CEs in patients with tetanus, especially those with underlying diseases, high modified Ablett scores, and a normal or low BMI. More studies are needed to fully elucidate the impact of ANSD on the cardiovascular function and the CE associated mortality in tetanus.

Magnitude, Patterns, and Associated Predictors of Cardiovascular Events in Tetanus: A 2-Year, Single-Center, Ambidirectional Cohort Study Involving 572 Patients

Clinical Benefit of AI–Assisted Lung Ultrasound in a Resource–Limited ICU

Skeletal muscle atrophy is a common occurrence in critically ill patients in the intensive care unit (ICU) who spend long periods in bed. Muscle mass must be recovered through physiotherapy before patient discharge and ultrasound imaging is frequently used to assess the recovery process by measuring the muscle size over time. However, these manual measurements are subject to large variability, particularly since the scans are typically acquired on different days and potentially by different operators. In this paper, we propose a self-supervised contrastive learning approach to automatically retrieve similar ultrasound muscle views at different scan times. Three different models were compared using data from 67 patients acquired in the ICU. Results indicate that our contrastive model outperformed a supervised baseline model in the task of view retrieval with an AUC of 73.52% and when combined with an automatic segmentation model achieved 5.7%+/-0.24% error in cross-sectional area. Furthermore, a user study survey confirmed the efficacy of our model for muscle view retrieval.

https://export.arxiv.org/pdf/2306.04739v1.pdf

Automatic retrieval of corresponding US views in longitudinal examinations

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vital_sqi: A Python package for physiological signal quality control

Magnitude, Patterns, and Associated Predictors of Cardiovascular Events in Tetanus: A 2-Year, Single-Center, Ambidirectional Cohort Study Involving 572 Patients

Clinical Benefit of AI–Assisted Lung Ultrasound in a Resource–Limited ICU

Automatic retrieval of corresponding US views in longitudinal examinations