arXiv subjects

Gari D. Clifford

Publications and source records attributed to Gari D. Clifford.

At least 19 recordsLinked to original sources

arXiv2026

Circadian Derived Features for Early Discrimination Across Insomnia Severity Levels: At Least 8 Weeks of Monitoring Are Needed for Clinically Meaningful Assessment

Background: Wearable devices provide continuous, objective measures of daily activity and offer promise for assessing sleep disorders. However, the minimum monitoring duration needed to differentiate insomnia severity remains unclear. We investigated when wearable-derived behavioral features become informative for distinguishing Insomnia Severity Index (ISI) categories and examined the contribution of Activity Count (AC) and circadian-derived features. Methods: We analyzed wearable data from 2,305 participants in the Advancing Understanding of Recovery after Trauma (AURORA) study. Separate binary classifiers were developed for four ISI categories across six follow-up periods using AC and circadian-derived feature sets. Performance was evaluated using accuracy, F1-score, precision, recall, and AUROC. Results: Classification improved with longer monitoring. Across ISI categories, approximately eight weeks was the earliest time point at which wearable-derived features consistently achieved informative discrimination, with modest improvements thereafter. Participants without clinically significant insomnia were easiest to identify, reaching an AUROC of 0.693. Circadian-derived features performed comparably to, and in several cases better than, AC features, suggesting that the temporal organization of daily activity provides information beyond overall activity volume. Conclusions: Approximately eight weeks of longitudinal wearable monitoring may represent a practical minimum for differentiating ISI-defined insomnia categories. Longer monitoring provided only incremental improvements. Circadian behavioral features show promise as digital biomarkers for objective insomnia assessment.

q-bio.QM↗

arXiv2026

TAMI: Temporally Aligned, Missingness-Aware, and Interpretable Multimodal Fusion for Mental Health Assessment in Older Adults with Mild Cognitive Impairment

Depression and anxiety in older adults with Mild Cognitive Impairment (MCI) are frequently underdiagnosed due to limited access to care. Multimodal analysis of remote clinical interviews is a scalable screening approach, but existing methods have three limitations. First, they do not correct temporal misalignment across multimodal features extracted at different resolutions, inducing spurious cross-modal associations. Second, remote recordings exhibit uneven modality dropout, but missing values are often zero-filled, making them indistinguishable from valid near-zero measurements. Finally, they do not jointly attribute predictions to modalities, questions, and interview moments, limiting fine-grained clinical interpretation. We propose a Temporally-Aligned, Missingness-Aware, Interpretable (TAMI) multimodal fusion framework. TAMI aligns speech, language, facial, and physiological features within question-answer segments on a shared timeline, encodes modality-level missingness over time, and conditions fusion on question context. In interviews with 49 older adults with MCI, TAMI achieved area under the receiver operating characteristic curve (AUROC) scores of 0.68 (depression) and 0.69 (anxiety). Fine-grained temporal alignment of multimodal features produced the largest performance gain ($Δ{\geq}0.1$). Multi-level interpretability analysis revealed that depression classification relied on eyegaze and open-ended questions, while anxiety classification depended on eyegaze and head pose, with attribution uniformly distributed across questions. Using only responses to the open-ended questions (5.1min), the depression model achieved an AUROC score of 0.67, which was not significantly different from using the full interview (19min) ($p>0.05$). Our findings support designing interview protocols centered on open-ended questions for depression screening in older adults with MCI.

cs.CV↗

arXiv2026

Cross-Modal Generative Framework for Signal Translation from Fetal-Maternal Electrocardiograms to Fetal Doppler Waveforms

Fetal electrocardiogram (fECG) and Doppler ultrasound provide complementary views of fetal cardiovascular function: fECG captures electrical activity while Doppler reflects mechanical hemodynamics shaped by factors such as placental resistance and vascular compliance. Understanding the recoverable and unrecoverable Doppler components through reconstruction from fECG offers insight into the relative contributions of electrical versus mechanical factors in fetal circulation, thereby informing clinical decisions. In addition, clinical evidence of maternal-fetal cardiac coupling suggests that maternal cardiovascular dynamics may also inform fetal hemodynamics. To computationally model these relationships, we propose a cross-modal generative framework combining dilated convolutions with cross-modal attention to selectively incorporate maternal ECG and self-attention to capture long-range temporal dependencies. Trained on 885 synchronized fetal/maternal ECG and Doppler envelope segments from 39 pregnancies, our model synthesizes Doppler envelopes with power spectral density mean squared error (PSD MSE) of 49.9 +/- 15.8 dB^2 (51% lower than two-channel baseline) and heart-rate error of 4.71 +/- 0.77 bpm (1.5% better than baseline; negligible relative to the 110-160 bpm physiological range). Cross-modal attention yields a 39% PSD MSE reduction over naive dual-channel concatenation, quantifying the contribution of maternal-fetal coupling. Our proposed framework advances computational modeling of the maternal-fetal cardiovascular system by enabling the synthesis of Doppler envelopes from dual-lead ECG. By analysis of both recoverable and residual Doppler components, this approach enables quantification of the purely mechanical contributions to Doppler waveforms -- those not recoverable from electrical recordings -- ultimately facilitating a more comprehensive fetal assessment.

cs.LG↗

arXiv2026

Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors

Autism Spectrum Disorder (ASD) is characterized by challenges with social interaction and communication and by restricted or repetitive patterns of thought and behavior, with significant variability in presentation. Approximately a quarter of children with ASD are classified as having profound autism, who often exhibit challenging behaviors, such as self-injurious behavior, aggression, elopement, or pica, that pose serious safety risks and disrupt learning in educational settings. Prior work has applied wearable sensors and machine learning to detect challenging behaviors, but has been largely confined to controlled laboratory environments. This work demonstrates that predicting challenging behavior episodes is feasible in a real-world special education classroom. We collected approximately 110.7 hours of labeled multimodal wearable data comprising accelerometry, electrodermal activity (EDA), and skin temperature from 9 children and young adults aged 10 to 21 years across standard classroom sessions. We fine-tuned state-of-the-art foundation models for multimodal wearable time-series analysis and show that challenging behavior episodes can be predicted up to 10 minutes in advance with an AUC-ROC of 0.78. These results establish a concrete foundation for developing proactive in-class intervention systems that enable teachers to minimize the safety risks of challenging behaviors in special education classrooms

cs.AI↗

arXiv2026

Multi-View Hierarchical Representation Learning of Fetal Hemodynamics for Maternal Hypertension Detection at the Edge

Hypertensive disorders of pregnancy remain a leading cause of maternal and fetal morbidity worldwide, yet diagnosis relies on intermittent cuff-based blood pressure measurements that are prone to bias and fail to capture continuous physiological dynamics. Growing evidence suggests that fetal cardiovascular activity is associated with maternal-placental hemodynamics and may encode markers of maternal hypertension. To analyze this, we collected a large-scale dataset of fetal one-dimensional Doppler ultrasound recordings paired with maternal blood pressure from 3,255 pregnant women across 8,170 antenatal visits in rural Guatemala. We developed AutoHyPE, a hierarchical attention network that models short- and long-term signal structure, incorporating a novel prototype-based contrastive learning and multi-view strategy to enhance representation robustness under long-tailed class distribution and biological variability. AutoHyPE achieved an AUROC of 0.80 for maternal hypertension detection, outperforming baseline approaches while maintaining balanced performance across classes, with no performance degradation in an edge deployment scenario. Our findings demonstrated that fetal cardiac mechanical activity contains hemodynamic features indicative of maternal hypertension status. This supports a promising paradigm shift toward continuous, objective monitoring of maternal health using existing, low-cost ultrasound technology and introduces a complementary approach to traditional methods based on blood pressure measurements, advancing scalable prenatal care.

eess.SP↗

arXiv2026

From PhysioNet to Foundation Models -- A History and Potential Futures

Over the last 35 years, the sharing of medical data and models for research has evolved from sneakernet to the internet - from mailing magnetic tapes and compact discs of a handful of well-curated recordings, to the high-speed download of relatively comprehensive hospital databases. More recently, the fervor around the potential for modern machine learning and 'AI' to catapult us into the next industrial revolution has led to a seemingly insatiable desire to pump almost any source of data into large models. Although this has great potential, it also presents a whole set of new challenges. In this article I examine these trends over the last 30 years, drawing on examples from cardiology, one of the oldest data-intensive fields that is undergoing a renaissance via machine learning. From the early days of computerized cardiology, the Research Resource for Complex Physiologic Signals (PhysioNet) has been at the cutting edge of this field. This article, therefore, includes much of the Resource's history and the contributions drawn from 25 years of firsthand experience of co-developing elements of the Resource with its founders. I identify the most promising future directions for the PhysioNet Resource, and more generally, the growing issues and opportunities around dissemination and use of massive physiological databases, associated open access code, and public competitions, along with potential solutions to the key issues facing our field. Topics range from how we should approach foundation models in the context of the rapidly growing AI carbon footprint, to the potential of Tiny-ML and edge computing. I also cover issues around prizes and incentives, funding models, and scientific repeatability, as well as how we might address these issues by leveraging the PhysioNet Challenges, consistent with the philosophy of open-access from the early days of the PhysioNet Resource.

cs.CY↗

arXiv2026

An Open-Source, Open Data Approach to Activity Classification from Triaxial Accelerometry in an Ambulatory Setting

The accelerometer has become an almost ubiquitous device, providing enormous opportunities in healthcare monitoring beyond step counting or other average energy estimates in 15-60 second epochs. Objective: To develop an open data set with associated open-source code for processing 50 Hz tri-axial accelerometry-based to classify patient activity levels and natural types of movement. Approach: Data were collected from 23 healthy subjects (16 males and seven females) aged between 23 and 62 years using an ambulatory device, which included a triaxial accelerometer and synchronous lead II equivalent ECG for an average of 26 minutes each. Participants followed a standardized activity routine involving five distinct activities: lying, sitting, standing, walking, and jogging. Two classifiers were constructed: a signal processing technique to distinguish between high and low activity levels and a convolutional neural network (CNN)-based approach to classify each of the five activities. Main results: The binary (high/low) activity classifier exhibited an F1 score of 0.79. The multi-class CNN-based classifier provided an F1 score of 0.83. The code for this analysis has been made available under an open-source license together with the data on which the classifiers were trained and tested. Significance: The classification of behavioral activity, as demonstrated in this study, offers valuable context for interpreting traditional health metrics and may provide contextual information to support the future development of clinical decision-making tools for patient monitoring, predictive analytics, and personalized health interventions.

q-bio.QM↗

arXiv2026

Modeling Metabolic State Transitions in Obesity Using a Time-Varying Lambda-Omega Framework

Obesity does not emerge abruptly; rather, it develops gradually over extended periods. The gradual progression often prevents early recognition of physiological changes until excess adiposity is established. A common belief is that weight reduction can be achieved simply by "eating less and moving more". Although reductions in caloric intake and increases in physical activity are fundamental principles of weight management, this perspective oversimplifies a complex and adaptive biological system. Metabolic rate, hormonal regulation, behavioral factors, and compensatory physiological responses all influence the body's resistance to changes in weight. During weight loss, reduced metabolic rate and increased efficiency make maintaining a caloric deficit increasingly difficult. Conversely, during periods of overfeeding, resting metabolic rate, the thermic effect of food, and non-exercise activity thermogenesis increase with rising body weight, partially offsetting the caloric surplus and slowing weight gain. However, these compensatory responses are asymmetrical, with stronger and more persistent adaptations to underfeeding than to overfeeding. This asymmetry helps explain why weight gain often occurs gradually and why sustained weight loss is biologically challenging. In this work, we employ a lambda-omega model from dynamical systems theory to describe metabolic regulation in response to lifestyle perturbations. We introduce time-varying parameters that allow the regulatory coefficients to evolve gradually under sustained environmental and physiological stressors. By allowing lambda(t) and omega(t) to vary over time, the model captures progressive shifts in the metabolic set-point and deformation of the underlying dynamical landscape. This framework enables exploration of transitions between metabolic states and long-term adaptations that shape trajectories of weight gain and loss.

q-bio.QM↗

arXiv2026

Fetal Sleep: A Cross-Species Review of Physiology, Measurement, and Classification

Study Objectives: Fetal sleep is a vital yet underexplored aspect of prenatal neurodevelopment. Its cyclic organization reflects the maturation of central neural circuits, and disturbances in these patterns may offer some of the earliest detectable signs of neurological compromise. This is the first review to integrate more than seven decades of research into a unified, cross-species synthesis of fetal sleep. We examine: (i) Physiology and Ontogeny-comparing human fetuses with animal models; and (ii) Methodological Evolution-transitioning from invasive neurophysiology to non-invasive monitoring and deep learning frameworks. Methods: A structured narrative synthesis was guided by a systematic literature search across four databases (PubMed, Scopus, IEEE Xplore, and Google Scholar). From 2,925 identified records, 171 studies involving fetal sleep-related physiology, sleep-state classification, or signal-based monitoring were included in this review. Results: Across the 171 studies, fetal sleep states become clearly observable as the brain matures. In fetal sheep and baboons, organized cycling between active and quiet sleep emerges at approximately 80%-90% gestation. In humans, this differentiation occurs later, around 95% gestation, with full maturation reached near term. Despite extensive animal research, no unified, clinically validated framework exists for defining fetal sleep states, limiting translation into routine obstetric practice. Conclusions: By integrating evidence across species, methodologies, and clinical contexts, this review provides the scientific foundation for developing objective, multimodal, and non-invasive fetal sleep monitoring technologies-tools that may ultimately support earlier detection of neurological compromise and guide timely prenatal intervention.

q-bio.NC↗

arXiv2025

Feasibility of Detecting Cognitive Impairment and Psychological Well-being among Older Adults Using Facial, Acoustic, Linguistic, and Cardiovascular Patterns Derived from Remote Conversations

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning (ML) models captured facial, acoustic, linguistic, and cardiovascular features from 39 older adults with normal cognition or Mild Cognitive Impairment (MCI), derived from remote video conversations and quantified their cognitive status, social isolation, neuroticism, and psychological well-being. Our model could distinguish Clinical Dementia Rating Scale (CDR) of 0.5 (vs. 0) with 0.77 area under the receiver operating characteristic curve (AUC), social isolation with 0.74 AUC, social satisfaction with 0.75 AUC, psychological well-being with 0.72 AUC, and negative affect with 0.74 AUC. Our feature importance analysis showed that speech and language patterns were useful for quantifying cognitive impairment, whereas facial expressions and cardiovascular patterns were useful for quantifying social and psychological well-being. Our bias analysis showed that the best-performing models for quantifying psychological well-being and cognitive states in older adults exhibited significant biases concerning their age, sex, disease condition, and education levels. Our comprehensive analysis shows the feasibility of monitoring the cognitive and psychological health of older adults, as well as the need for collecting largescale interview datasets of older adults to benefit from the latest advances in deep learning technologies to develop generalizable models across older adults with diverse demographic backgrounds and disease conditions.

cs.HC↗

arXiv2025

Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025

Objective: Chagas disease is a parasitic infection that is endemic to South America, Central America, and, more recently, the U.S., primarily transmitted by insects. Chronic Chagas disease can cause cardiovascular diseases and digestive problems. Serological testing capacities for Chagas disease are limited, but Chagas cardiomyopathy often manifests in ECGs, providing an opportunity to prioritize patients for testing and treatment. Approach: The George B. Moody PhysioNet Challenge 2025 invites teams to develop algorithmic approaches for identifying Chagas disease from electrocardiograms (ECGs). Main results: This Challenge provides multiple innovations. First, we leveraged several datasets with labels from patient reports and serological testing, provided a large dataset with weak labels and smaller datasets with strong labels. Second, we augmented the data to support model robustness and generalizability to unseen data sources. Third, we applied an evaluation metric that captured the local serological testing capacity for Chagas disease to frame the machine learning problem as a triage task. Significance: Over 630 participants from 111 teams submitted over 1300 entries during the Challenge, representing diverse approaches from academia and industry worldwide.

cs.LG↗

arXiv2025

FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classification

Introduction: This study presents FetalSleepNet, the first published deep learning approach to classifying sleep states from the ovine electroencephalogram (EEG). Fetal EEG is complex to acquire and difficult and laborious to interpret consistently. However, accurate sleep stage classification may aid in the early detection of abnormal brain maturation associated with pregnancy complications (e.g. hypoxia or intrauterine growth restriction). Methods: EEG electrodes were secured onto the ovine dura over the parietal cortices of 24 late gestation fetal sheep. A lightweight deep neural network originally developed for adult EEG sleep staging was trained on the ovine EEG using transfer learning from adult EEG. A spectral equalisation-based domain adaptation strategy was used to reduce cross-domain mismatch. Results: We demonstrated that while direct transfer performed poorly, full fine tuning combined with spectral equalisation achieved the best overall performance (accuracy: 86.6 percent, macro F1-score: 62.5), outperforming baseline models. Conclusions: To the best of our knowledge, FetalSleepNet is the first deep learning framework specifically developed for automated sleep staging from the fetal EEG. Beyond the laboratory, the EEG-based sleep stage classifier functions as a label engine, enabling large scale weak/semi supervised labeling and distillation to facilitate training on less invasive signals that can be acquired in the clinic, such as Doppler Ultrasound or electrocardiogram data. FetalSleepNet's lightweight design makes it well suited for deployment in low power, real time, and wearable fetal monitoring systems.

eess.SP↗

arXiv2025

Cross-Learning Between ECG and PCG: Exploring Common and Exclusive Characteristics of Bimodal Electromechanical Cardiac Waveforms

Simultaneous electrocardiography (ECG) and phonocardiogram (PCG) provide a comprehensive, multimodal perspective on cardiac function by capturing the heart's electrical and mechanical activities, respectively. However, the distinct and overlapping information content of these signals, as well as their potential for mutual reconstruction and biomarker extraction, remains incompletely understood, especially under varying physiological conditions and across individuals. In this study, we systematically investigate the common and exclusive characteristics of ECG and PCG using the EPHNOGRAM dataset of simultaneous ECG-PCG recordings during rest and exercise. We employ a suite of linear and nonlinear machine learning models, including non-causal LSTM networks, to reconstruct each modality from the other and analyze the influence of causality, physiological state, and cross-subject variability. Our results demonstrate that nonlinear models, particularly non-causal LSTM, provide superior reconstruction performance, with reconstructing ECG from PCG proving more tractable than the reverse. Exercise and cross-subject scenarios present significant challenges, but envelope-based modeling that utilizes instantaneous amplitude features substantially improves cross-subject generalizability for cross-modal learning. Furthermore, we demonstrate that clinically relevant ECG biomarkers, such as fiducial points and QT intervals, can be estimated from PCG in cross-subject settings. These findings advance our understanding of the relationship between electromechanical cardiac modalities, in terms of both waveform characteristics and the timing of cardiac events, with potential applications in novel multimodal cardiac monitoring technologies.

cs.LG↗

arXiv2025

Auto-FEDUS: Autoregressive Generative Modeling of Doppler Ultrasound Signals from Fetal Electrocardiograms

Fetal health monitoring through one-dimensional Doppler ultrasound (DUS) signals offers a cost-effective and accessible approach that is increasingly gaining interest. Despite its potential, the development of machine learning based techniques to assess the health condition of mothers and fetuses using DUS signals remains limited. This scarcity is primarily due to the lack of extensive DUS datasets with a reliable reference for interpretation and data imbalance across different gestational ages. In response, we introduce a novel autoregressive generative model designed to map fetal electrocardiogram (FECG) signals to corresponding DUS waveforms (Auto-FEDUS). By leveraging a neural temporal network based on dilated causal convolutions that operate directly on the waveform level, the model effectively captures both short and long-range dependencies within the signals, preserving the integrity of generated data. Cross-subject experiments demonstrate that Auto-FEDUS outperforms conventional generative architectures across both time and frequency domain evaluations, producing DUS signals that closely resemble the morphology of their real counterparts. The realism of these synthesized signals was further gauged using a quality assessment model, which classified all as good quality, and a heart rate estimation model, which produced comparable results for generated and real data, with a Bland-Altman limit of 4.5 beats per minute. This advancement offers a promising solution for mitigating limited data availability and enhancing the training of DUS-based fetal models, making them more effective and generalizable.

cs.LG↗

arXiv2025

Edge AI for Real-time Fetal Assessment in Rural Guatemala

Perinatal complications, defined as conditions that arise during pregnancy, childbirth, and the immediate postpartum period, represent a significant burden on maternal and neonatal health worldwide. Factors contributing to these disparities include limited access to quality healthcare, socioeconomic inequalities, and variations in healthcare infrastructure. Addressing these issues is crucial for improving health outcomes for mothers and newborns, particularly in underserved communities. To mitigate these challenges, we have developed an AI-enabled smartphone application designed to provide decision support at the point-of-care. This tool aims to enhance health monitoring during pregnancy by leveraging machine learning (ML) techniques. The intended use of this application is to assist midwives during routine home visits by offering real-time analysis and providing feedback based on collected data. The application integrates TensorFlow Lite (TFLite) and other Python-based algorithms within a Kotlin framework to process data in real-time. It is designed for use in low-resource settings, where traditional healthcare infrastructure may be lacking. The intended patient population includes pregnant women and new mothers in underserved areas and the developed system was piloted in rural Guatemala. This ML-based solution addresses the critical need for accessible and quality perinatal care by empowering healthcare providers with decision support tools to improve maternal and neonatal health outcomes.

cs.LG↗

arXiv2024

Electromechanical Dynamics of the Heart: A Study of Cardiac Hysteresis During Physical Stress Test

Cardiovascular diseases are best diagnosed using multiple modalities that assess both the heart's electrical and mechanical functions. While effective, imaging techniques like echocardiography and nuclear imaging are costly and not widely accessible. More affordable technologies, such as simultaneous electrocardiography (ECG) and phonocardiography (PCG), may provide valuable insights into electromechanical coupling and could be useful for prescreening in low-resource settings. Using physical stress test data from the EPHNOGRAM ECG-PCG dataset, collected from 23 healthy male subjects (age: 25.4+/-1.9 yrs), we investigated electromechanical intervals (RR, QT, systolic, and diastolic) and their interactions during exercise, along with hysteresis between cardiac electrical activity and mechanical responses. Time delay analysis revealed distinct temporal relationships between QT, systolic, and diastolic intervals, with RR as the primary driver. The diastolic interval showed near-synchrony with RR, while QT responded to RR interval changes with an average delay of 10.5s, and the systolic interval responded more slowly, with an average delay of 28.3s. We examined QT-RR, systolic-RR, and diastolic-RR hysteresis, finding narrower loops for diastolic RR and wider loops for systolic RR. Significant correlations (average:0.75) were found between heart rate changes and hysteresis loop areas, suggesting the equivalent circular area diameter as a promising biomarker for cardiac function under exercise stress. Deep learning models, including Long Short-Term Memory and Convolutional Neural Networks, estimated the QT, systolic, and diastolic intervals from RR data, confirming the nonlinear relationship between RR and other intervals. Findings highlight a significant cardiac memory effect, linking ECG and PCG morphology and timing to heart rate history.

physics.med-ph↗

arXiv2024

ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts; A Foundation for Computerized ECG Image Digitization and Analysis

We introduce the ECG-Image-Database, a large and diverse collection of electrocardiogram (ECG) images generated from ECG time-series data, with real-world scanning, imaging, and physical artifacts. We used ECG-Image-Kit, an open-source Python toolkit, to generate realistic images of 12-lead ECG printouts from raw ECG time-series. The images include realistic distortions such as noise, wrinkles, stains, and perspective shifts, generated both digitally and physically. The toolkit was applied to 977 12-lead ECG records from the PTB-XL database and 1,000 from Emory Healthcare to create high-fidelity synthetic ECG images. These unique images were subjected to both programmatic distortions using ECG-Image-Kit and physical effects like soaking, staining, and mold growth, followed by scanning and photography under various lighting conditions to create real-world artifacts. The resulting dataset includes 35,595 software-labeled ECG images with a wide range of imaging artifacts and distortions. The dataset provides ground truth time-series data alongside the images, offering a reference for developing machine and deep learning models for ECG digitization and classification. The images vary in quality, from clear scans of clean papers to noisy photographs of degraded papers, enabling the development of more generalizable digitization algorithms. ECG-Image-Database addresses a critical need for digitizing paper-based and non-digital ECGs for computerized analysis, providing a foundation for developing robust machine and deep learning models capable of converting ECG images into time-series. The dataset aims to serve as a reference for ECG digitization and computerized annotation efforts. ECG-Image-Database was used in the PhysioNet Challenge 2024 on ECG image digitization and classification.

q-bio.QM↗

arXiv2024

Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis

Rapid identification and accurate documentation of interfering and high-risk behaviors in ASD, such as aggression, self-injury, disruption, and restricted repetitive behaviors, are important in daily classroom environments for tracking intervention effectiveness and allocating appropriate resources to manage care needs. However, having a staff dedicated solely to observing is costly and uncommon in most educational settings. Recently, multiple research studies have explored developing automated, continuous, and objective tools using machine learning models to quantify behaviors in ASD. However, the majority of the work was conducted under a controlled environment and has not been validated for real-world conditions. In this work, we demonstrate that the latest advances in video-based group activity recognition techniques can quantify behaviors in ASD in real-world activities in classroom environments while preserving privacy. Our explainable model could detect the episode of problem behaviors with a 77% F1-score and capture distinctive behavior features in different types of behaviors in ASD. To the best of our knowledge, this is the first work that shows the promise of objectively quantifying behaviors in ASD in a real-world environment, which is an important step toward the development of a practical tool that can ease the burden of data collection for classroom staff.

cs.CV↗