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Analysis and Design of an Energy-Eff...

Miaolin, Zhang,

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Analysis and Design of an Energy-Efficient Patient-Kspecific Epilepsy Tracking Soc /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Analysis and Design of an Energy-Efficient Patient-Kspecific Epilepsy Tracking Soc // Zhang Miaolin.
作者:	Miaolin, Zhang,
面頁冊數:	1 electronic resource (120 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
標題:	Electroencephalography. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31777236
ISBN:	9798346773238

Analysis and Design of an Energy-Efficient Patient-Kspecific Epilepsy Tracking Soc /
Miaolin, Zhang,

Analysis and Design of an Energy-Efficient Patient-Kspecific Epilepsy Tracking Soc /Zhang Miaolin. - 1 electronic resource (120 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

Epilepsy is a chronic non-communicable disease that affects over 50 million people's lives around the world. Machine learning algorithm utilizing EEG recording to achieve patient-specific real-time ambulatory epilepsy tracking helps the diagnosis and treatment. However, there are some challenges that state-of-the-art epilepsy tracking systems need to overcome: 1) Limited training dataset (for patient-specific training of the classifier), 2) EEG channel selection, 3) transient and localized noise. In this work, we propose a Time-Channel Averaging Feature Extraction algorithm that averages out transient and localized noise, reducing false negative and false positive by 26% and 65%, respectively. Compared with the prevalent minimum redundancy maximum relevance feature selection algorithm, the proposed Time-Averaging based Channel Selection algorithm selects EEG channels with higher average sensitivity and specificity of 1.6%P and 0.4%P. The SoC is implemented in the TSMC 1P8M 40nm process with an area of 1.6mm x 1.3mm. Verified with the CHB-MIT database with more than 180 seizure events and 990 hours of EEG recording of 24 patients, the implemented Guided Time Channel Averaging Support-Vector-Machine classifier achieves sensitivity and specificity of 97.8% and 99.5% across 24 patients 0.68 µJ/Class. The proposed Approximate Online Tuning algorithm uses 512x fewer feature vectors and 874x fewer training clock cycles per tuning compared with previous algorithms. In addition, the online tuning is verified with a local recruited epilepsy patient and achieves a sensitivity boost of 1.8x after one-shot learning and follow-up online tuning for the first time.

English

ISBN: 9798346773238Subjects--Topical Terms:

543293
Electroencephalography.

Analysis and Design of an Energy-Efficient Patient-Kspecific Epilepsy Tracking Soc /
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