Dr. Emmanuel During presents a talk on the validation of a novel actigraphy-based classifier developed to detect iRBD using wearable devices. Originally trained on data from a high-resolution actigraph, the classifier was successfully tested on a large external dataset from Hong Kong using a different, lower resolution actigraph.
Mount Sinai-Led Team Enhances Automated Method to Detect Common Sleep Disorder Affecting Millions
AI-powered algorithm can analyze video recordings of clinical sleep tests and more accurately diagnose REM sleep behavior disorder.
Sleep-Monitoring Devices Can Help Detect Neurological Issues
A Mount Sinai researcher has demonstrated that commonly used wrist actigraphy devices can accurately detect REM sleep behavior disorder (RBD). Because at least half of patients with RBD go on to develop a neurological disease such as Parkinson’s or Lewy body dementia, this finding holds the potential for early intervention to reduce the risk or slow the progression of these disabling disorders.
