Title :
A compressed-domain processor for seizure detection to simultaneously reduce computation and communication energy
Author :
Shoaib, Mohammed ; Jha, Niraj K. ; Verma, Naveen
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., Princeton, NJ, USA
Abstract :
In low-power sensing systems, communication constraints play a critical role; e.g., biomedical devices often acquire physiological signals from distributed sources and/or wireless implants. Compressive sensing enables sub-Nyquist sampling for low-energy data reduction on such nodes. The reconstruction cost, however, is severe, typically pushing signal analysis to a base station. We present a seizure-detection processor that directly analyzes compressively-sensed electroencephalograms (EEGs) on the sensor node. In addition to alleviating communication costs while also circumventing reconstruction costs, it leads to computational energy savings, due to the reduced number of input samples. This provides an effective knob for system power management and enables scaling of energy and application-level performance. For compression factors of 2-24×, the energy to extract signal features (over 18 channels) is 7.13-0.11μJ, and the detector´s performance for sensitivity, latency, and specificity is 96-80%, 4.7-17.8 sec, and 0.15-0.79 false-alarms/hr., respectively (compared to baseline performance of 96%, 4.6 sec, and 0.15 false-alarms/hr.).
Keywords :
biomedical equipment; compressed sensing; diseases; electroencephalography; feature extraction; medical signal processing; prosthetics; sensitivity; signal reconstruction; signal sampling; EEG; application-level performance; base station; biomedical devices; communication constraints; communication energy; compressed-domain processor; compressively-sensed electroencephalogram; computational energy savings; distributed sources; low-energy data reduction; low-power sensing systems; physiological signals; reconstruction cost; seizure-detection processor; sensitivity; signal analysis; signal feature extraction; simultaneously reduce computation; subNyquist sampling; system power management; wireless implants; Compressed sensing; Detectors; Electroencephalography; Feature extraction; Integrated circuits; Random access memory; Support vector machines;
Conference_Titel :
Custom Integrated Circuits Conference (CICC), 2012 IEEE
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4673-1555-5
Electronic_ISBN :
0886-5930
DOI :
10.1109/CICC.2012.6330601