TY - GEN
T1 - A Fast and Accurate Approach for Real-Time Seizure Detection in the IoMT
AU - Sayeed, Md Abu
AU - Mohanty, Saraju P.
AU - Kougianos, Elias
AU - Zaveri, Hitten
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - We propose an EEG-based seizure detection method which uses the discrete wavelet transform (DWT), Hjorth parameters and a k-NN classifier. Seizure detection is performed in three stages. In the first stage, EEG signals are decomposed by the DWT into sub-bands and Hjorth parameters are extracted from each of these sub-bands. In the second stage, a k-NN classifier is used to classify the EEG data. The results demonstrate a significant difference in Hjorth parameters between interictal and ictal EEG with ictal EEG being less complex than interictal EEG. We report an accuracy of 100% for a classification of normal vs. ictal EEG and 97.9% for normal and interictal vs. ictal EEG. We propose an Internet of Medical Things (IoMT) platform for performing seizure detection. The proposed framework accommodates the proposed scheme for seizure detection and allows communication of detection results. The IoMT framework also allows the adjustment of seizure detection parameters in response to updated performance evaluations, and possible changes in seizure and signal characteristics as well as the incorporation of other sensor signals to provide an adaptive, multi-modal framework for detecting seizures.
AB - We propose an EEG-based seizure detection method which uses the discrete wavelet transform (DWT), Hjorth parameters and a k-NN classifier. Seizure detection is performed in three stages. In the first stage, EEG signals are decomposed by the DWT into sub-bands and Hjorth parameters are extracted from each of these sub-bands. In the second stage, a k-NN classifier is used to classify the EEG data. The results demonstrate a significant difference in Hjorth parameters between interictal and ictal EEG with ictal EEG being less complex than interictal EEG. We report an accuracy of 100% for a classification of normal vs. ictal EEG and 97.9% for normal and interictal vs. ictal EEG. We propose an Internet of Medical Things (IoMT) platform for performing seizure detection. The proposed framework accommodates the proposed scheme for seizure detection and allows communication of detection results. The IoMT framework also allows the adjustment of seizure detection parameters in response to updated performance evaluations, and possible changes in seizure and signal characteristics as well as the incorporation of other sensor signals to provide an adaptive, multi-modal framework for detecting seizures.
KW - Electroencephalogram (EEG)
KW - Epilepsy
KW - Feature Extraction
KW - Hjorth Parameters
KW - IoT
KW - Seizure Detection
UR - http://www.scopus.com/inward/record.url?scp=85063532721&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063532721&partnerID=8YFLogxK
U2 - 10.1109/ISC2.2018.8656713
DO - 10.1109/ISC2.2018.8656713
M3 - Conference contribution
AN - SCOPUS:85063532721
T3 - 2018 IEEE International Smart Cities Conference, ISC2 2018
BT - 2018 IEEE International Smart Cities Conference, ISC2 2018
T2 - 2018 IEEE International Smart Cities Conference, ISC2 2018
Y2 - 16 September 2018 through 19 September 2018
ER -