ECG-Based Semi-Supervised Anomaly Detection for Early Detection and Monitoring of Epileptic Seizures.

ABSTRACT: Epilepsy is one of the most common brain diseases, characterized by frequent recurrent seizures or "ictal" states. A patient experiences uncontrollable muscular contractions, inducing loss of mobility and balance, which may result in injury or even death during these ictal states. Extensive investigation is vital to establish a systematic approach for predicting and informing patients about oncoming seizures ahead of time. Most methodologies developed are focused on the detection of abnormalities using mostly electroencephalogram (EEG) recordings. In this regard, research has indicated that certain pre-ictal alterations in the Autonomic Nervous System (ANS) can be detected in patient electrocardiogram (ECG) signals. The latter could potentially provide the basis for a robust seizure predic

SUBMITTER: Karasmanoglou A 

PROVIDER: S-EPMC10049350 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature