Onset detection of epileptic seizures from accelerometry signal

Shitanshu Kusmakar, Chandan K. Karmakar, Bernard Yan, Terence J. O'Brien, Ramanathan Muthuganapathy, Marimuthu Palaniswami

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Epileptic seizures are the result of any abnormal asynchronous firing of cortical neurons. Seizures are abrupt and pose a risk of injury and fatal harm to the patient. Epilepsy affects patients quality of life (QOL) and imposes financial, social, and physical burden on the patient. The unpredictability associated with seizures further adds to the reduced QOL and increases dependence on caregivers and family members. A seizure triggered alarm system can reduce the risk of seizure-related injuries and aid in improving patient's QOL. This study presents real-time onset detection of seizures from accelerometry signal. An automated approach based on statistical machine learning is employed to learn the onset of seizures. To search for the optimal parameter that simultaneously maximizes detection sensitivity (sens) while minimizing false alarm rate (FAR) and latency, the epoch length is varied from $t=\{1,~10s\}$. Linear and non-linear time-varying dynamical patterns were extracted from every epoch using Poincaré plot analysis. The correlation patterns were learned using a kernalized support vector data descriptor. The preliminary analysis on accelerometry data collected from 8 epileptic patients with 9 generalized tonicclonic seizures (GTCS) shows promising results. The proposed algorithm detected all GTCS events (sens: 100%, FAR: 1. 09/24h) at 8s from onset. The proposed algorithm can lead to a sensitive, specific, and a relatively short-latency detection system for real-time remote monitoring of epileptic patients.

Original languageEnglish
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
EditorsGregg Suaning, Olaf Dossel
Place of PublicationDanvers MA USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages6104-6107
Number of pages4
ISBN (Electronic)9781538636466 , 9781538636459
ISBN (Print)9781538636473
DOIs
Publication statusPublished - 1 Jul 2018
Externally publishedYes
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society 2018 - Honolulu, United States of America
Duration: 17 Jul 201821 Jul 2018
Conference number: 40th
https://embc.embs.org/2018/

Conference

ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society 2018
Abbreviated titleEMBC 2018
CountryUnited States of America
CityHonolulu
Period17/07/1821/07/18
Internet address

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