DOI: 10.1101/495572Dec 13, 2018Paper

Automated Seizure Detection Tailored Towards Portable Patient Care Using A Novel, Computationally Efficient Method of Analysis

BioRxiv : the Preprint Server for Biology
Zachary Huang, Yiming Ying

Abstract

Epilepsy is a devastating neurological disorder that affects approximately 1-3% population worldwide. Wearables have recently gained more popularity as having a promising future in epilepsy management including seizure alert and close-loop therapy for severe forms of seizures. Due to the random and low frequency of seizures, seizure evaluation requires continuous, long-term EEG monitoring, which produces a large volume of data. The future treatment systems will rely on algorithms that can detect seizures with high precision and low computational cost. Previous implemented algorithms have used various computationally expensive methods of data transformation and feature extraction. In the present study, we developed a new computationally efficient seizure detection algorithm based on analysis of the broad, global shape EEG data of tonic-clonic seizures from a mouse model of temporal lobe epilepsy (TLE). To perform this algorithm, EEG data was normalized and processed through a rolling mean function, producing smoothed, simplified EEG clips that represent the global shape of the clip. These signals were then directly inputted for SVM training and testing. This novel method of seizure analysis only requires a small fraction of EEG ...Continue Reading

Related Concepts

Electroencephalography
Epilepsy
Epilepsy, Temporal Lobe
Patient Care
Heart Rate
Muscle Contraction
Nervous System Disorder
Transformation, Genetic
United States Food and Drug Administration
Clip

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