J. Low Power Electron. Appl. 2011, 1(1), 150-174; doi:10.3390/jlpea1010150
Review

Data-Driven Approaches for Computation in Intelligent Biomedical Devices: A Case Study of EEG Monitoring for Chronic Seizure Detection

1,* email, 1,* email and 2email
Received: 25 November 2010; in revised form: 13 April 2011 / Accepted: 20 April 2011 / Published: 26 April 2011
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Intelligent biomedical devices implies systems that are able to detect specific physiological processes in patients so that particular responses can be generated. This closed-loop capability can have enormous clinical value when we consider the unprecedented modalities that are beginning to emerge for sensing and stimulating patient physiology. Both delivering therapy (e.g., deep-brain stimulation, vagus nerve stimulation, etc.) and treating impairments (e.g., neural prosthesis) requires computational devices that can make clinically relevant inferences, especially using minimally-intrusive patient signals. The key to such devices is algorithms that are based on data-driven signal modeling as well as hardware structures that are specialized to these. This paper discusses the primary application-domain challenges that must be overcome and analyzes the most promising methods for this that are emerging. We then look at how these methods are being incorporated in ultra-low-energy computational platforms and systems. The case study for this is a seizure-detection SoC that includes instrumentation and computation blocks in support of a system that exploits patient-specific modeling to achieve accurate performance for chronic detection. The SoC samples each EEG channel at a rate of 600 Hz and performs processing to derive signal features on every two second epoch, consuming 9 μJ/epoch/channel. Signal feature extraction reduces the data rate by a factor of over 40×, permitting wireless communication from the patient’s head while reducing the total power on the head by 14×.
Keywords: biomedical; body-area networks; electroencephalography; implantable; low-noise amplifiers; low-power electronics; machine-learning; subthreshold
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MDPI and ACS Style

Verma, N.; Lee, K.H.; Shoeb, A. Data-Driven Approaches for Computation in Intelligent Biomedical Devices: A Case Study of EEG Monitoring for Chronic Seizure Detection. J. Low Power Electron. Appl. 2011, 1, 150-174.

AMA Style

Verma N, Lee KH, Shoeb A. Data-Driven Approaches for Computation in Intelligent Biomedical Devices: A Case Study of EEG Monitoring for Chronic Seizure Detection. Journal of Low Power Electronics and Applications. 2011; 1(1):150-174.

Chicago/Turabian Style

Verma, Naveen; Lee, Kyong Ho; Shoeb, Ali. 2011. "Data-Driven Approaches for Computation in Intelligent Biomedical Devices: A Case Study of EEG Monitoring for Chronic Seizure Detection." J. Low Power Electron. Appl. 1, no. 1: 150-174.

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