A robust and fast seizure detector for IoT edge

Md Abu Sayeed; Saraju P. Mohanty; Elias Kougianos; Venkata Prasanath Yanambaka; Hitten Zaveri

doi:10.1109/iSES.2018.00042

A robust and fast seizure detector for IoT edge

Md Abu Sayeed
, Saraju P. Mohanty
, Elias Kougianos
, Venkata Prasanath Yanambaka
, Hitten Zaveri

Department of Electrical and Computer Engineering

University of North Texas
Yale University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Scopus citations

Abstract

Epilepsy is a neurological disorder which has negative impact on human life quality. Epilepsy affects almost 1% of the world population necessitating a unified system for fast seizure detection as well as remote health monitoring to enhance the daily lives of the epilepsy patients. We envision a smart seizure detection framework in the edge of the Internet of Things (IoT) which is capable of detecting seizures as well as monitoring the patient's healthcare activity remotely. Detection of seizure is performed using the discrete wavelet transform, statistical feature extraction, and a naive Bayes (NB) classifier. The proposed system was implemented and validated using Simulink, ThingSpeak, and off-the-shelf microcontrollers. Experimental results show that the proposed system reduces latency by 44% compared to a cloud-IoT based system and reports a classification accuracy of 98.65%.

Original language	English
Title of host publication	Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018
Pages	156-160
Number of pages	5
ISBN (Electronic)	9781538691724
DOIs	https://doi.org/10.1109/iSES.2018.00042
State	Published - 2 Jul 2018
Event	4th IEEE International Symposium on Smart Electronic Systems, iSES 2018 - Hyderabad, India Duration: 17 Dec 2018 → 19 Dec 2018

Publication series

Name	Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018

Conference

Conference	4th IEEE International Symposium on Smart Electronic Systems, iSES 2018
Country/Territory	India
City	Hyderabad
Period	17/12/18 → 19/12/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Electroencephalogram (EEG)
Epilepsy
Feature Extraction
IoT
Naive Bayes Classifier
Seizure Detection

Access to Document

10.1109/iSES.2018.00042

Cite this

Sayeed, M. A., Mohanty, S. P., Kougianos, E., Yanambaka, V. P., & Zaveri, H. (2018). A robust and fast seizure detector for IoT edge. In Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018 (pp. 156-160). Article 8719339 (Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018). https://doi.org/10.1109/iSES.2018.00042

@inproceedings{4b6db1f75d634cd6879fe834abf39e71,

title = "A robust and fast seizure detector for IoT edge",

abstract = "Epilepsy is a neurological disorder which has negative impact on human life quality. Epilepsy affects almost 1\% of the world population necessitating a unified system for fast seizure detection as well as remote health monitoring to enhance the daily lives of the epilepsy patients. We envision a smart seizure detection framework in the edge of the Internet of Things (IoT) which is capable of detecting seizures as well as monitoring the patient's healthcare activity remotely. Detection of seizure is performed using the discrete wavelet transform, statistical feature extraction, and a naive Bayes (NB) classifier. The proposed system was implemented and validated using Simulink, ThingSpeak, and off-the-shelf microcontrollers. Experimental results show that the proposed system reduces latency by 44\% compared to a cloud-IoT based system and reports a classification accuracy of 98.65\%.",

keywords = "Electroencephalogram (EEG), Epilepsy, Feature Extraction, IoT, Naive Bayes Classifier, Seizure Detection",

author = "Sayeed, \{Md Abu\} and Mohanty, \{Saraju P.\} and Elias Kougianos and Yanambaka, \{Venkata Prasanath\} and Hitten Zaveri",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th IEEE International Symposium on Smart Electronic Systems, iSES 2018 ; Conference date: 17-12-2018 Through 19-12-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/iSES.2018.00042",

language = "English",

series = "Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018",

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booktitle = "Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018",

}

Sayeed, MA, Mohanty, SP, Kougianos, E, Yanambaka, VP & Zaveri, H 2018, A robust and fast seizure detector for IoT edge. in Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018., 8719339, Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018, pp. 156-160, 4th IEEE International Symposium on Smart Electronic Systems, iSES 2018, Hyderabad, India, 17/12/18. https://doi.org/10.1109/iSES.2018.00042

A robust and fast seizure detector for IoT edge. / Sayeed, Md Abu; Mohanty, Saraju P.; Kougianos, Elias et al.
Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018. 2018. p. 156-160 8719339 (Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A robust and fast seizure detector for IoT edge

AU - Sayeed, Md Abu

AU - Mohanty, Saraju P.

AU - Kougianos, Elias

AU - Yanambaka, Venkata Prasanath

AU - Zaveri, Hitten

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Epilepsy is a neurological disorder which has negative impact on human life quality. Epilepsy affects almost 1% of the world population necessitating a unified system for fast seizure detection as well as remote health monitoring to enhance the daily lives of the epilepsy patients. We envision a smart seizure detection framework in the edge of the Internet of Things (IoT) which is capable of detecting seizures as well as monitoring the patient's healthcare activity remotely. Detection of seizure is performed using the discrete wavelet transform, statistical feature extraction, and a naive Bayes (NB) classifier. The proposed system was implemented and validated using Simulink, ThingSpeak, and off-the-shelf microcontrollers. Experimental results show that the proposed system reduces latency by 44% compared to a cloud-IoT based system and reports a classification accuracy of 98.65%.

AB - Epilepsy is a neurological disorder which has negative impact on human life quality. Epilepsy affects almost 1% of the world population necessitating a unified system for fast seizure detection as well as remote health monitoring to enhance the daily lives of the epilepsy patients. We envision a smart seizure detection framework in the edge of the Internet of Things (IoT) which is capable of detecting seizures as well as monitoring the patient's healthcare activity remotely. Detection of seizure is performed using the discrete wavelet transform, statistical feature extraction, and a naive Bayes (NB) classifier. The proposed system was implemented and validated using Simulink, ThingSpeak, and off-the-shelf microcontrollers. Experimental results show that the proposed system reduces latency by 44% compared to a cloud-IoT based system and reports a classification accuracy of 98.65%.

KW - Electroencephalogram (EEG)

KW - Epilepsy

KW - Feature Extraction

KW - IoT

KW - Naive Bayes Classifier

KW - Seizure Detection

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M3 - Conference contribution

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BT - Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, iSES 2018

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ER -

A robust and fast seizure detector for IoT edge

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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