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J. Low Power Electron. Appl. 2011, 1(1), 150-174; doi:10.3390/jlpea1010150
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Data-Driven Approaches for Computation in Intelligent Biomedical Devices: A Case Study of EEG Monitoring for Chronic Seizure Detection

1,* , 1,*  and 2
1 Princeton University, Engineering Quadrangle B-226, Olden Street, Princeton, NJ 08544, USA 2 Massachusetts General Hospital & MIT , 77 Massachusetts Avenue, Cambridge, MA 02139, USA
* Authors to whom correspondence should be addressed.
Received: 25 November 2010 / Revised: 13 April 2011 / Accepted: 20 April 2011 / Published: 26 April 2011
(This article belongs to the Special Issue Selected Topics in Low Power Design - From Circuits to Applications)
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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 biomedical; body-area networks; electroencephalography; implantable; low-noise amplifiers; low-power electronics; machine-learning; subthreshold
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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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.

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