[HTML][HTML] Self-supervised contrastive learning for medical time series: A systematic review
Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
Intelligent wearable systems: Opportunities and challenges in health and sports
Wearable devices, or wearables, designed to be attached to the human body, can gather
personalized real-time data and continuously monitor an individual's health status and …
personalized real-time data and continuously monitor an individual's health status and …
Self-supervised anomaly detection: A survey and outlook
Over the past few years, anomaly detection, a subfield of machine learning that is mainly
concerned with the detection of rare events, witnessed an immense improvement following …
concerned with the detection of rare events, witnessed an immense improvement following …
Self-supervised anomaly detection in computer vision and beyond: A survey and outlook
Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal …
finance, and healthcare, by identifying patterns or events that deviate from normal …
Automatic severity classification of dysarthric speech by using self-supervised model with multi-task learning
Automatic assessment of dysarthric speech is essential for sustained treatments and
rehabilitation. However, obtaining atypical speech is challenging, often leading to data …
rehabilitation. However, obtaining atypical speech is challenging, often leading to data …
Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach
Objective Passive monitoring of touchscreen interactions generates keystroke dynamic
signals that can be used to detect and track neurological conditions such as Parkinson's …
signals that can be used to detect and track neurological conditions such as Parkinson's …
[HTML][HTML] Machine learning-based analysis of digital movement assessment and ExerGame scores for Parkinson's disease severity estimation
Neurodegenerative Parkinson's Disease (PD) is one of the common incurable diseases
among the elderly. Clinical assessments are characterized as standardized means for PD …
among the elderly. Clinical assessments are characterized as standardized means for PD …
SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection
Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate
and timely diagnostic methods to facilitate effective treatment and intervention. In recent …
and timely diagnostic methods to facilitate effective treatment and intervention. In recent …