Gaussian mixture model for the identification of psychogenic non-epileptic seizures using a wearable accelerometer sensor

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

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

7 Citations (Scopus)

Abstract

Any abnormal hypersynchronus activity of neurons can be characterized as an epileptic seizure (ES). A broad class of non-epileptic seizures is comprised of Psychogenic non-epileptic seizures (PNES). PNES are paroxysmal events, which mimics epileptic seizures and pose a diagnostic challenge with epileptic seizures due to their clinical similarities. The diagnosis of PNES is done using video-electroencephalography (VEM) monitoring. VEM being a resource intensive process calls for alternative methods for detection of PNES. There is now an emerging interest in the use of accelerometer based devices for the detection of seizures. In this work, we present an algorithm based on Gaussian mixture model (GMM's) for the identification of PNES, ES and normal movements using a wrist-worn accelerometer device. Features in time, frequency and wavelet domain are extracted from the norm of accelerometry signal. All events are then classified into three classes i.e normal, PNES and ES using a parametric estimate of the multivariate normal probability density function. An algorithm based on GMM's allows us to accurately model the non-epileptic and epileptic movements, thus enhancing the overall predictive accuracy of the system. The new algorithm was tested on data collected from 16 patients and showed an overall detection accuracy of 91% with 25 false alarms.

Original languageEnglish
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016)
EditorsJose Principe, Jose Carmena, Justin Sanchez
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1006-1009
Number of pages4
ISBN (Electronic)9781457702204
ISBN (Print)9781457702198
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society 2016: Empowering Individual Healthcare Decisions through Technology - Walt Disney World Resort, Orlando, United States of America
Duration: 16 Aug 201620 Aug 2016
Conference number: 38th
https://embc.embs.org/2016/

Conference

ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society 2016
Abbreviated titleEMBC 2016
CountryUnited States of America
CityOrlando
Period16/08/1620/08/16
Internet address

Cite this

Kusmakar, S., Muthuganapathy, R., Yan, B., O'Brien, T. J., & Palaniswami, M. (2016). Gaussian mixture model for the identification of psychogenic non-epileptic seizures using a wearable accelerometer sensor. In J. Principe, J. Carmena, & J. Sanchez (Eds.), 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) (pp. 1006-1009). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2016.7590872