Researchers from the Epilepsy Centre at Kuopio University Hospital, University of Eastern Finland and Neuro Event Labs created an algorithm with deep convolutional neural networks to offer fast, reliable and automatic assessment of the severity of myoclonic jerks. The neural network and pre-trained models could identify and track key points in the human body.
Myoclonus is brief, involuntary twitching of muscles. This is the most disabling and progressive drug-resistant symptom in patients with progressive myoclonus epilepsy type 1 (EPM1). Myoclonus is stimulus sensitive and its severity fluctuates throughout the day. Stress, sleep deprivation and anxiety can aggravate the symptoms.
The clinical analysis of myoclonus is challenging and requires extensive expertise from doctors. The medical community has been searching for an automatic tool to improve the consistency and reliability of serial myoclonus evaluations. Currently, the unified myoclonus rating scale (UMRS) is the gold standard for evaluating myoclonus. UMRS is a clinical video recorded test panel.