TY - JOUR
T1 - Early seizure detection by applying frequency-based algorithm derived from the principal component analysis
AU - Lee, Jiseon
AU - Park, Junhee
AU - Yang, Sejung
AU - Kim, Hani
AU - Choi, Yun Seo
AU - Kim, Hyeon Jin
AU - Lee, Hyang Woon
AU - Lee, Byung Uk
N1 - Funding Information:
This work was supported by the Ewha Global Top 5 Grant 2013 (HWL, BUL) and RP-Grant 2014 (JP) of Ewha Womans University (http://www.ewha.ac.kr), and grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) (www.khidi.or.kr), funded by the Ministry of Health and Welfare (http://www. mohw.go.kr) (HI14C1989 to HWL), and the Basic Science Research Program through the National Research Foundation of Korea (NRF) (http://www.nrf.re.kr) funded by the Ministry of Education, Science and Technology (http://www.msip.go.kr) (2012-R1A1A3013525 to SY and 2014-R1A2A1A11052103 to HWL).
Publisher Copyright:
© 2017 Lee, Park, Yang, Kim, Choi, Kim, Lee and Lee.
PY - 2017/8/17
Y1 - 2017/8/17
N2 - The use of automatic electrical stimulation in response to early seizure detection has been introduced as a new treatment for intractable epilepsy. For the effective application of this method as a successful treatment, improving the accuracy of the early seizure detection is crucial. In this paper, we proposed the application of a frequency-based algorithm derived from principal component analysis (PCA), and demonstrated improved efficacy for early seizure detection in a pilocarpine-induced epilepsy rat model. A total of 100 ictal electroencephalographs (EEG) during spontaneous recurrent seizures from 11 epileptic rats were finally included for the analysis. PCA was applied to the covariance matrix of a conventional EEG frequency band signal. Two PCA results were compared: one from the initial segment of seizures (5 sec of seizure onset) and the other from the whole segment of seizures. In order to compare the accuracy, we obtained the specific threshold satisfying the target performance from the training set, and compared the False Positive (FP), False Negative (FN), and Latency (Lat) of the PCA based feature derived from the initial segment of seizures to the other six features in the testing set. The PCA based feature derived from the initial segment of seizures performed significantly better than other features with a 1.40%FP, zero FN, and 0.14 s Lat. These results demonstrated that the proposed frequency-based feature from PCA that captures the characteristics of the initial phase of seizure was effective for early detection of seizures. Experiments with rat ictal EEGs showed an improved early seizure detection rate with PCA applied to the covariance of the initial 5 s segment of visual seizure onset instead of using the whole seizure segment or other conventional frequency bands.
AB - The use of automatic electrical stimulation in response to early seizure detection has been introduced as a new treatment for intractable epilepsy. For the effective application of this method as a successful treatment, improving the accuracy of the early seizure detection is crucial. In this paper, we proposed the application of a frequency-based algorithm derived from principal component analysis (PCA), and demonstrated improved efficacy for early seizure detection in a pilocarpine-induced epilepsy rat model. A total of 100 ictal electroencephalographs (EEG) during spontaneous recurrent seizures from 11 epileptic rats were finally included for the analysis. PCA was applied to the covariance matrix of a conventional EEG frequency band signal. Two PCA results were compared: one from the initial segment of seizures (5 sec of seizure onset) and the other from the whole segment of seizures. In order to compare the accuracy, we obtained the specific threshold satisfying the target performance from the training set, and compared the False Positive (FP), False Negative (FN), and Latency (Lat) of the PCA based feature derived from the initial segment of seizures to the other six features in the testing set. The PCA based feature derived from the initial segment of seizures performed significantly better than other features with a 1.40%FP, zero FN, and 0.14 s Lat. These results demonstrated that the proposed frequency-based feature from PCA that captures the characteristics of the initial phase of seizure was effective for early detection of seizures. Experiments with rat ictal EEGs showed an improved early seizure detection rate with PCA applied to the covariance of the initial 5 s segment of visual seizure onset instead of using the whole seizure segment or other conventional frequency bands.
KW - Early seizure detection
KW - Electroencephalography
KW - Frequency-based feature
KW - Principal component analysis
KW - Seizure onset
UR - http://www.scopus.com/inward/record.url?scp=85028061227&partnerID=8YFLogxK
U2 - 10.3389/fninf.2017.00052
DO - 10.3389/fninf.2017.00052
M3 - Article
AN - SCOPUS:85028061227
SN - 1662-5196
VL - 11
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
M1 - 52
ER -