DocumentCode :
1665700
Title :
An 8-channel scalable EEG acquisition SoC with fully integrated patient-specific seizure classification and recording processor
Author :
Yoo, Jerald ; Yan, Long ; El-Damak, Dina ; Altaf, Muhammad Bin ; Shoeb, Ali ; Yoo, Hoi-Jun ; Chandrakasan, Anantha
Author_Institution :
Masdar Inst. of Sci. & Technol., Abu Dhabi, United Arab Emirates
fYear :
2012
Firstpage :
292
Lastpage :
294
Abstract :
Tracking seizure activity to determine proper medication requires a small form factor, ultra-low power sensor with continuous EEG classification. Technical challenges arise from: 1) patient-to-patient variation of seizure pattern on EEG, 2) fully integrating an ultra-low power variable dynamic range instrumentation circuits with seizure detection processor, and 3) reducing communication overhead. Reference [1] extracted EEG features locally on-chip to reduce the data being transmitted, and saved power by 1/14 when compared to raw EEG data transmission. However, it still needs data transmission and off-chip classification to detect and to store seizure activity. This paper presents an ultra-low power scalable EEG acquisition SoC for continuous seizure detection and recording with fully integrated patient-specific Support Vector Machine (SVM)-based classification processor.
Keywords :
electroencephalography; support vector machines; system-on-chip; 8-channel scalable EEG acquisition SoC; EEG data transmission; communication overhead; continuous EEG classification; continuous seizure detection; medication; off-chip classification; patient-specific seizure classification; patient-specific support vector machine; patient-to-patient variation; recording processor; seizure activity tracking; seizure detection processor; seizure pattern; ultralow power sensor; ultralow power variable dynamic range instrumentation circuit; Bandwidth; DSL; Electroencephalography; Instruments; Iron; Noise; System-on-a-chip;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Solid-State Circuits Conference Digest of Technical Papers (ISSCC), 2012 IEEE International
Conference_Location :
San Francisco, CA
ISSN :
0193-6530
Print_ISBN :
978-1-4673-0376-7
Type :
conf
DOI :
10.1109/ISSCC.2012.6177019
Filename :
6177019
Link To Document :
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