Physiological measurement最新文献

Objective.Arrhythmia classification from electrocardiograms (ECGs) suffers from high false positive rates and limited cross-dataset generalization, particularly for atrial fibrillation (AF) detection where specificity ranges from 0.72 to 0.98 using conventional 30 s analysis windows. While conventional deep learning approaches analyze isolated 30 s ECG windows, many arrhythmias, particularly AF and atrial flutter, exhibit diagnostic features that emerge over extended time scales.Approach.We introduce S4ECG, a deep learning architecture based on structured state-space models (S4), designed to capture long-range temporal dependencies by jointly analyzing multiple consecutive ECG windows spanning up to 2  min. We evaluated S4ECG on four publicly available databases for multi-class arrhythmia classification, including systematic cross-dataset evaluations to assess out-of-distribution robustness.Main results.Multi-window analysis consistently outperformed single-window approaches across all datasets, improving the macro-averaged area under the receiver operating characteristic curve by 1.0-11.6 percentage points. For AF detection specifically, specificity increased from 0.718-0.979 (single-window) to 0.967-0.998 (multi-window) at a fixed sensitivity threshold, representing a 3-10 fold reduction in false positive rates.Significance.Comparative analysis against convolutional neural network baselines demonstrated superior performance of the S4 architecture. Cross-dataset evaluation revealed that multi-window approaches substantially improved generalization performance, with smaller performance degradation when models were tested on held-out datasets from different institutions and acquisition protocols. A systematic investigation revealed optimal diagnostic windows of 10-20 min, beyond which performance plateaus or degrades. These findings demonstrate that structured incorporation of extended temporal context enhances both arrhythmia classification accuracy and cross-dataset robustness. The identified optimal temporal windows provide practical guidance for ECG monitoring system design and may reflect underlying physiological timescales of arrhythmogenic dynamics.

Objective.Infrared thermography is projected as an innovative and very promising tool for the observation of muscle response to fatigue. The aim of this study was to observe the skin temperature (Tsk) by thermography about acute muscle fatigue and delayed soreness (DOMS) following an exercise protocol until exhaustion in the triceps suralis.Approach.An open longitudinal descriptive observational study of the posterior leg region was performed in 73 healthy subjects. Data on age, sex, body mass index and triceps suralis thermography pre, post and 24 h after maximum muscle fatigue physical exercise, as well as pressure-pain threshold (PPT) and pain sensation by analogic visual scale (VAS) were collected.Main Results.Results showed significant difference in skin temperature over time (Tsk B: 30.1 °C (CI 95% (29.7-30.3), Tsk POST: 29.9 °C (CI 95% (29.6-30.2) and Tsk 24 H: 30.6 °C (CI 95% (30.3-30.9),p= <0.001;η2p= 0.272), side (Tsk right: 30.2 °C (CI 95% (29.4-30.3) and Tsk left: 30.1 °C (CI 95% (29.8-30.4),p= 0.021;η2p= 0.072) and a time x side interaction (Right Tsk B: 30.1 °C (CI 95% (29.8-30.4), Tsk POST: 29.9 °C (CI 95% (29.6-30.2), Tsk 24 H: 30.6 °C (CI 95% (30.3-30.9) and Left Tsk B: 30.0 °C (CI 95% (29.6-30.3), Tsk POST: 29.8 °C (CI 95% (29.5-30.1), Tsk 24 H: 30.6 °C (CI 95% (30.3-30.9),p= 0.011;η2p= 0.061). Regarding the PPT, significant changes were observed over time (B: 9.11 Kg (CI 95% (8.3-10.1), POST: 10.5 Kg (CI 95% (9.7-11.6) and 24 H: 7.64 Kg (CI 95% (7.0-8.3),p= <0.001;η²p= 0.328) and in the interaction between time and sex (men B: 11.0 Kg (CI 95% (9.7-12.3), POST: 12.5 Kg (CI 95% (11.1-13.9), 24 H: 8.8 Kg (CI 95% (7.8-9.7) and women B: 7.4 kg (CI 95% (6.1-8.7), POST: 8.8 Kg (CI 95% (7.4-10.1), 24 H = 6.6 Kg (CI 95% (5.7-7.5),p= 0.050;η²p= 0.041). Finally, the VAS scores showed significant changes over time (B: 0.32 cm (CI 95% (0.18-0.43) and 24 H: 4.46 cm (CI 95% (3.85-5.17),p= <0.001;η²p= 0.831).Significance.According to the results obtained, this technique could be a reliable method to evaluate DOMS. Exploring the integration of thermography with other modalities could provide a global understanding of muscle recovery processes.

Objective: To establish population-specific, age- and sex-stratified electrocardiographic (ECG) reference ranges for Chinese children and adolescents using a data-driven approach, addressing the limitations of conventional empirically defined age groupings. Approach. A total of 35,088 ECG recordings from individuals under 18 years of age without structural heart disease or electrocardiographic abnormalities were analyzed. An unsupervised machine-learning clustering algorithm was applied to identify natural developmental trajectories of 149 ECG parameters and derive data-driven age intervals. Sex-specific stratification was performed to account for physiological differences. To assess physiological validity, we evaluated the ability of the newly derived reference ranges to identify ECG deviations in children with echocardiographically confirmed ventricular septal defects (VSD). Main Results. Four distinct age-dependent variation patterns were identified across the 149 ECG parameters, enabling precise determination of age-specific intervals. Sex-related differences were observed for most measurements. When applied to children with VSD, the data-driven reference intervals demonstrated higher sensitivity in detecting ECG deviations compared with previously published standards. Significance. This study introduces a machine-learning-based paradigm for defining pediatric ECG reference values. The resulting age- and sex-specific thresholds more accurately reflect physiological maturation and cardiac loading changes than traditional reference sets, offering improved clinical relevance for pediatric ECG interpretation. .

Excessive daytime sleepiness (EDS) refers to a physiological state where individuals have difficulty remaining alert during the day. Managing EDS is particularly challenging to study and treat due to its multifaceted nature. Assessment methods include both subjective and objective approaches. Subjective evaluation often relies on simple, widely accepted, and widely used questionnaires; however, these tools are inherently limited by self-reporting bias. Objective assessment, on the other hand, primarily involves two well-known and reliable tests, but these are costly, time-consuming, and impractical for use outside of sleep units. Therefore, developing an objective tool that can quickly and accurately detect a decline in alertness, while remaining reliable, easy to use, and affordable, is of critical importance for sleep clinicians, safety organizations, and researchers. According to PRISMA guidelines, we did a systematic analysis of 95 studies that used photoplethysmography (PPG) for assessing EDS, drowsiness, and/or fatigue during the last 15 years (2010-2025). With advances in wearable technology, particularly through PPG and artificial intelligence, achieving this goal may be attainable. The next essential step is rigorous validation against established gold-standard tests to ensure the tool meets scientific and clinical standards for widespread adoption.

Objective.As cardiovascular diseases continue to rise, the accurate and convenient calculation of stroke volume (SV) and cardiac output (CO) has become an important topic. Studies have shown that electrical impedance tomography (EIT) can provide continuous non-invasive SV measurements. Despite its potential, a review of the various calculation methods for EIT-based SV and CO, along with their clinical utility, is lacking.Approach. A literature search was conducted on PubMed and Web of Science Core Collection. Full-text research articles in English were reviewed and discussed.Main results. In recent years, advancements in technology, clinical research, and intelligent algorithms have revealed EIT's substantial potential in SV monitoring.Significance. This article offers a review of the evolution of EIT technology in measuring SV, introducing various calculation methods, their advantages, challenges, and clinical applications.

Numerous studies examine the link between health and sleep-wake patterns to understand etiology, establish preventive algorithms, or develop therapeutics. The use of actigraphy to measure physical activity (PA) and sleep is increasing, partly because of its non-invasive nature and its ability to continuously monitor PA and sleep in free-living settings. There are several actigraphy data cleaning and pre-processing methods, but there is no consensus to define activity values or cleaning guidelines that can be used to facilitate comparison across research studies. This scoping review examined existing literature on cleaning and pre-processing of actigraphy data. The PubMed (US National Library of Medicine), Scopus (Elsevier), and Web of Science:Core Collection (Clarivate Analytics) databases were searched for original studies published in English from 2017-2024. Using Covidence, two reviewers independently screened each article and collected data. A total of 102 studies were included for the final analysis. Our results showed substantial heterogeneity in actigraphy devices, data cleaning and pre-processing methods, with some studies using their own algorithmic approaches to generate PA and sleep variables. While some studies used well-established algorithms like Freedson or Cole-Kripke, a large proportion either developed custom methods or did not report sufficient detail to allow replication. This variability highlights the urgent need for standardized reporting and consensus-based protocols in actigraphy data cleaning and pre-processing to allow replication and comparison of findings across studies.

Objective.Autonomous sensory meridian response (ASMR) is a tingling sensation induced while attending to specific sounds, including whispering, tapping, scratching, or other soft, repetitive noises. While previous studies focused on low arousal-positive emotions such as relaxation and calmness, this study explores a broader range of emotions elicited by ASMR auditory stimuli, including happiness, sadness, and disgust.Approach.The proposed study collects the multi-modal physiological data from electroencephalography, photoplethysmography, and electrodermal activity via wearable bio-sensors from 23 ASMR-experiencing participants while exposed to different ASMR-inducing auditory stimuli. It employs the rmANOVA test on the collected physiological responses and self-reported ratings for quantitative analysis and results in a significant difference between the emotions induced from the four audio stimuli, i.e. Happy from A1, Sad from A2, Calm from A3, Disgust from A4, and the neutral state. The proposed study also applies deep learning classifiers, artificial neural network (ANN), and convolution neural network (CNN) to the collected multi-modal physiological data to classify the four induced emotions from the ASMR auditory stimuli using the dimensions of arousal, valence, and dominance.Main results. The classification accuracy results from ANN, and CNN prove an excellent success rate of 96.12% and 74.25% with multi-modal valence-arousal-dominance for ANN and CNN, respectively, in classifying the four emotions induced by ASMR stimuli. And the statistical rmANOVA test results indicated distinctions among the four emotions, as thep-values exceeded the significance threshold of 0.05.Significance.The results highlight the effectiveness of multi-modal physiological signals and deep learning in reliably classifying ASMR-induced emotions, contributing to advancements in emotion recognition for mental health and therapeutic applications.

Objective.Large vessel occlusion (LVO) stroke presents a major challenge in clinical practice due to the potential for poor outcomes with delayed treatment. Treatment for LVO involves highly specialized care, in particular endovascular thrombectomy, and is available only at certain hospitals. Therefore, prehospital identification of LVO by emergency ambulance services, can be critical for triaging LVO stroke patients directly to a hospital with access to endovascular therapy. Clinical scores exist to help distinguish LVO from less severe strokes, but they are based on a series of examinations that can be time-consuming and may be impractical for patients with dementia or those who cannot follow commands due to their stroke. There is a need for a fast and reliable method to aid in the early identification of LVO. In this study, our objective was to assess the feasibility of using 30 s photoplethysmography (PPG) recording to assist in recognizing LVO stroke.Approach.A total of 88 patients, including 25 with LVO, 27 with stroke mimic (SM), and 36 non-LVO stroke patients (NL), were recorded at the Liverpool Hospital emergency department in Sydney, Australia. Demographics (age, sex), as well as morphological features and beating rate variability measures, were extracted from the PPG. A binary classification approach was employed to differentiate between LVO stroke and NL + SM (NL.SM). A 2:1 train-test split was stratified and repeated randomly across 100 iterations.Main results.The best model achieved a median test set area under the receiver operating characteristic curve of 0.77 (0.71-0.82).Significance.Our study demonstrates the potential of utilizing a 30 s PPG recording for identifying LVO stroke.

Self-supervised learning (SSL) has enabled Vision Transformers (ViTs) to learn robust representations from large-scale natural image datasets, enhancing their generalization across domains. In retinal imaging, foundation models pretrained on either natural or ophthalmic data have shown promise, but the benefits of in-domain pretraining remain uncertain. To investigate this, we benchmark six SSL-pretrained ViTs on seven digital fundus image (DFI) datasets totaling 70,000 expert-annotated images for the task of moderate-to-late age-related macular degeneration (AMD) identification. Our results show that iBOT pretrained on natural images, achieves the highest out-of-distribution generalization, with AUROCs of 0.80-0.97, outperforming domain-specific models, which achieved AUROCs of 0.78-0.96 and a baseline ViT-L with no pretraining, which achieved AUROC of 0.68-0.91. These findings highlight the value of foundation models in improving AMD identification, and challenge the assumption that in-domain pretraining is necessary. Furthermore, we release BRAMD, an open-access dataset (n=587) of DFIs with AMD labels from Brazil.

Objective. Fetal and maternal health during pregnancy can be monitored with sensors such as Doppler or scalp fetal ECG. This study focuses on single-channel dry electrode maternal abdominal ECG (aECG) to extract fetal heart rate (fHR) using a low-complexity algorithm suitable for low-power wearables.Approach. A hybrid model combining machine learning, QRS masking, and data fusion was trained on two PhysioNet databases and synthetically generatedaECG. Model selection employed the Akaike criterion with data balancing and random sampling.Main results. The algorithm was tested on 80 recordings from the Computer in Cardiology Challenge 2013 (CCC) and the abdominal and direct fetal database (ADFD), augmented with 100 syntheticaECG. Performance for fetal QRS detection reachedPrecision=97.2(82.2)%,Specificity=99.8(93.8)%, andSensitivity=97.4(93.9)% on ADFD and CCC, respectively. Clinical validation used the Polar Electro Oy H10 dry-electrode device at the Maternity Hospital of Southwest Finland. Four subjects (gestational age39.8±1.3 weeks) were analyzed, with seven discarded. ForfHR, the mean absolute percentage error was1.9±1.0%, Availability79.6±3.9%, and coverage probabilityCP5=76.2%,CP10=87.5%.Significance. These results demonstrate the feasibility offHRmonitoring from dry-electrodeaECGtailored for low-power wearables. Signal quality in clinical subjects matched the lowest PhysioNet cases, confirming robustness under low signal-to-noise conditions.