Preictal state detection using prodromal symptoms: A machine learning approach.
Journal:
Epilepsia
PMID:
33465245
Abstract
A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML) approach. Twenty-four patients with drug-resistant epilepsy were admitted for continuous video-electroencephalographic monitoring and filled out a daily four-point questionnaire on prodromal symptoms. Data were then classified into (1) a preictal group for questionnaires completed in a 24-h period prior to at least one seizure (n = 58) and (2) an interictal group for questionnaires completed in a 24-h period without seizures (n = 190). Our prediction model was based on a support vector machine classifier and compared to a Fisher's linear classifier. The combination of all the prodromal symptoms yielded a good prediction performance (area under the curve [AUC] = .72, 95% confidence interval [CI] = .61-.81). This performance was significantly enhanced by selecting a subset of the most relevant symptoms (AUC = .80, 95% CI = .69-.88). In comparison, the linear classifier systematically failed (AUCs < .6). Our findings indicate that the ML analysis of prodromal symptoms is a promising approach to identifying preictal states prior to seizures. This could pave the way for development of clinical strategies in seizure prevention and even a noninvasive alarm system.
Authors
Keywords
Adult
Affect
Area Under Curve
Attention
Comprehension
Drug Resistant Epilepsy
Electroencephalography
Female
Hearing Loss
Humans
Machine Learning
Male
Middle Aged
Noise
Photophobia
Prodromal Symptoms
Reading
Seizures
Speech
Support Vector Machine
Surveys and Questionnaires
Tinnitus
Video Recording
Vision Disorders
Young Adult