TY - JOUR
T1 - ESeiz
T2 - An Edge-Device for Accurate Seizure Detection for Smart Healthcare
AU - Sayeed, Md Abu
AU - Mohanty, Saraju P.
AU - Kougianos, Elias
AU - Zaveri, Hitten P.
N1 - Publisher Copyright:
© 1975-2011 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Epilepsy is one of the most common neurological disorders affecting a significant portion of the world's population and approximately 2.5 million people in the United States. Important biomedical research effort is focused on the development of energy efficient devices for the real-time detection of seizures. In this paper, we propose an Internet of Medical Things (IoMT)-based automated seizure detection system which will detect a seizure from electroencephalography (EEG) signals using a voltage level detector (VLD) and a signal rejection algorithm (SRA). The proposed system analyzes neural signals continuously and extracts the hyper-synchronous pulses for the detection of seizure onset. Within a time frame, if the number of pulses exceeds a predefined threshold value, a seizure is declared. The SRA reduces false detections, which in turn enhances the accuracy of the seizure detector. The design was validated using system-level simulations and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry can lead to reduction of power consumption compared to many contemporary approaches. The proposed approach can be generalized to other sensor modalities and the use of wearable or implantable solutions, or a combination of the two.
AB - Epilepsy is one of the most common neurological disorders affecting a significant portion of the world's population and approximately 2.5 million people in the United States. Important biomedical research effort is focused on the development of energy efficient devices for the real-time detection of seizures. In this paper, we propose an Internet of Medical Things (IoMT)-based automated seizure detection system which will detect a seizure from electroencephalography (EEG) signals using a voltage level detector (VLD) and a signal rejection algorithm (SRA). The proposed system analyzes neural signals continuously and extracts the hyper-synchronous pulses for the detection of seizure onset. Within a time frame, if the number of pulses exceeds a predefined threshold value, a seizure is declared. The SRA reduces false detections, which in turn enhances the accuracy of the seizure detector. The design was validated using system-level simulations and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry can lead to reduction of power consumption compared to many contemporary approaches. The proposed approach can be generalized to other sensor modalities and the use of wearable or implantable solutions, or a combination of the two.
KW - IoMT
KW - Smart healthcare
KW - automated detection
KW - electroencephalography
KW - energy efficient systems
KW - epilepsy
KW - low latency systems
KW - seizure
KW - wearables
UR - http://www.scopus.com/inward/record.url?scp=85067071188&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067071188&partnerID=8YFLogxK
U2 - 10.1109/TCE.2019.2920068
DO - 10.1109/TCE.2019.2920068
M3 - Article
AN - SCOPUS:85067071188
SN - 0098-3063
VL - 65
SP - 379
EP - 387
JO - IEEE Transactions on Consumer Electronics
JF - IEEE Transactions on Consumer Electronics
IS - 3
M1 - 8726143
ER -