TY - GEN
T1 - RSeiz
T2 - 5th IEEE International Symposium on Smart Electronic Systems, iSES 2019
AU - Sayeed, Md Abu
AU - Mohanty, Saraju
AU - Kougianos, Elias
AU - Rachakonda, Laavanya
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Epilepsy affects 1% of the world population, which necessitates a fast seizure detection system for practical epilepsy solutions. The reduction of seizure detection delay is a critical problem which needs to be addressed as rapid detection provides effective treatment. In this paper an electroencephalogram (EEG) based, patient-specific seizure detection system is proposed in the Internet of Medical Things (IoMT) framework which can detect seizures at a minimum delay. The proposed system uses neighborhood component analysis (NCA) for channel selection, statistical features for optimal feature extraction, and a ReliefFbased optimization (RBO) in conjunction with a k-nearest neighbor classifier for feature classification. A publicly available database (CHB-MIT EEG) has been used for evaluation of the proposed algorithm. The simulation results show that the proposed algorithm provides a sensitivity of 100% while maintaining a low average latency of 1.49 sec, which may be useful for practical epilepsy treatment and biomedical applications.
AB - Epilepsy affects 1% of the world population, which necessitates a fast seizure detection system for practical epilepsy solutions. The reduction of seizure detection delay is a critical problem which needs to be addressed as rapid detection provides effective treatment. In this paper an electroencephalogram (EEG) based, patient-specific seizure detection system is proposed in the Internet of Medical Things (IoMT) framework which can detect seizures at a minimum delay. The proposed system uses neighborhood component analysis (NCA) for channel selection, statistical features for optimal feature extraction, and a ReliefFbased optimization (RBO) in conjunction with a k-nearest neighbor classifier for feature classification. A publicly available database (CHB-MIT EEG) has been used for evaluation of the proposed algorithm. The simulation results show that the proposed algorithm provides a sensitivity of 100% while maintaining a low average latency of 1.49 sec, which may be useful for practical epilepsy treatment and biomedical applications.
KW - Electroencephalogram (EEG)
KW - Epilepsy
KW - Internet of Medical Things (IoMT)
KW - Seizure Detection
KW - Seizure Early Detection
KW - Smart Healthcare
UR - http://www.scopus.com/inward/record.url?scp=85081198751&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081198751&partnerID=8YFLogxK
U2 - 10.1109/iSES47678.2019.00033
DO - 10.1109/iSES47678.2019.00033
M3 - Conference contribution
AN - SCOPUS:85081198751
T3 - Proceedings - 2019 IEEE International Symposium on Smart Electronic Systems, iSES 2019
SP - 105
EP - 110
BT - Proceedings - 2019 IEEE International Symposium on Smart Electronic Systems, iSES 2019
Y2 - 16 December 2019 through 18 December 2019
ER -