An energy efficient real-time seizure detection method in rats with spontaneous temporal lobe epilepsy

Yu Lin Wang, Sheng-Fu Liang, Fu-Zen Shaw, Yu Shin Huang, Yin Lin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Hie presence of an on-nne seizure uetccnon system could drive an antiepileptic stimulator in real time to suppress seizure generation and to enhance the patients' safety and quality of life. In this paper, the continuous long-term EEGs of three Wistar rats with spontaneous temporal lobe seizure were analyzed. We proposed the development of an energy efficient real-time seizure detection method that employs a hierarchical architecture. The first stage was used to fast detect the seizure-like EEG segment, and a classifier was utilized in the second stage for final confirmation. Only when a suspected seizure segment is found, the second stage is activated. With 2-staged architecture, it saved about 99.4% computation energy in the experiment. Therefore, it is useful to improve the longevity of the closed-loop seizure control system. Three classifiers, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM), were applied for comparison. From the experimental results, three classifiers yielded the comparable performances. However, considering of the trade-off between detection performances and power consumption, LDA which yielded the 100% detection rate, 0.22 FP/hr, and 1.69 s detection latency is suggested for a portable closed-loop seizure controller.

Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
PublisherIEEE Computer Society
Pages29-35
Number of pages7
ISBN (Print)9781467358712
DOIs
Publication statusPublished - 2013 Jan 1
Event2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, Singapore
Duration: 2013 Apr 162013 Apr 19

Publication series

NameProceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

Other

Other2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
CountrySingapore
CitySingapore
Period13-04-1613-04-19

Fingerprint

Epilepsy
Discriminant analysis
Energy Efficient
Rats
Classifiers
Discriminant Analysis
Electroencephalography
Real-time
Classifier
Support vector machines
Quality of Life
Closed-loop Control
Electric power utilization
Closed-loop
Closed-loop System
Power Consumption
Control systems
Latency
Support Vector Machine
Controllers

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Modelling and Simulation

Cite this

Wang, Y. L., Liang, S-F., Shaw, F-Z., Huang, Y. S., & Chen, Y. L. (2013). An energy efficient real-time seizure detection method in rats with spontaneous temporal lobe epilepsy. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 (pp. 29-35). [6609162] (Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013). IEEE Computer Society. https://doi.org/10.1109/CCMB.2013.6609162
Wang, Yu Lin ; Liang, Sheng-Fu ; Shaw, Fu-Zen ; Huang, Yu Shin ; Chen, Yin Lin. / An energy efficient real-time seizure detection method in rats with spontaneous temporal lobe epilepsy. Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. IEEE Computer Society, 2013. pp. 29-35 (Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013).
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title = "An energy efficient real-time seizure detection method in rats with spontaneous temporal lobe epilepsy",
abstract = "Hie presence of an on-nne seizure uetccnon system could drive an antiepileptic stimulator in real time to suppress seizure generation and to enhance the patients' safety and quality of life. In this paper, the continuous long-term EEGs of three Wistar rats with spontaneous temporal lobe seizure were analyzed. We proposed the development of an energy efficient real-time seizure detection method that employs a hierarchical architecture. The first stage was used to fast detect the seizure-like EEG segment, and a classifier was utilized in the second stage for final confirmation. Only when a suspected seizure segment is found, the second stage is activated. With 2-staged architecture, it saved about 99.4{\%} computation energy in the experiment. Therefore, it is useful to improve the longevity of the closed-loop seizure control system. Three classifiers, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM), were applied for comparison. From the experimental results, three classifiers yielded the comparable performances. However, considering of the trade-off between detection performances and power consumption, LDA which yielded the 100{\%} detection rate, 0.22 FP/hr, and 1.69 s detection latency is suggested for a portable closed-loop seizure controller.",
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Wang, YL, Liang, S-F, Shaw, F-Z, Huang, YS & Chen, YL 2013, An energy efficient real-time seizure detection method in rats with spontaneous temporal lobe epilepsy. in Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013., 6609162, Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, IEEE Computer Society, pp. 29-35, 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, Singapore, Singapore, 13-04-16. https://doi.org/10.1109/CCMB.2013.6609162

An energy efficient real-time seizure detection method in rats with spontaneous temporal lobe epilepsy. / Wang, Yu Lin; Liang, Sheng-Fu; Shaw, Fu-Zen; Huang, Yu Shin; Chen, Yin Lin.

Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. IEEE Computer Society, 2013. p. 29-35 6609162 (Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Wang, Yu Lin

AU - Liang, Sheng-Fu

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N2 - Hie presence of an on-nne seizure uetccnon system could drive an antiepileptic stimulator in real time to suppress seizure generation and to enhance the patients' safety and quality of life. In this paper, the continuous long-term EEGs of three Wistar rats with spontaneous temporal lobe seizure were analyzed. We proposed the development of an energy efficient real-time seizure detection method that employs a hierarchical architecture. The first stage was used to fast detect the seizure-like EEG segment, and a classifier was utilized in the second stage for final confirmation. Only when a suspected seizure segment is found, the second stage is activated. With 2-staged architecture, it saved about 99.4% computation energy in the experiment. Therefore, it is useful to improve the longevity of the closed-loop seizure control system. Three classifiers, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM), were applied for comparison. From the experimental results, three classifiers yielded the comparable performances. However, considering of the trade-off between detection performances and power consumption, LDA which yielded the 100% detection rate, 0.22 FP/hr, and 1.69 s detection latency is suggested for a portable closed-loop seizure controller.

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Wang YL, Liang S-F, Shaw F-Z, Huang YS, Chen YL. An energy efficient real-time seizure detection method in rats with spontaneous temporal lobe epilepsy. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. IEEE Computer Society. 2013. p. 29-35. 6609162. (Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013). https://doi.org/10.1109/CCMB.2013.6609162