A Micro-Power EEG Acquisition SoC With

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Transcript of A Micro-Power EEG Acquisition SoC With


    A Micro-Power EEG Acquisition SoC WithIntegrated Feature Extraction Processor for a Chronic

    Seizure Detection SystemNaveen Verma, Member, IEEE, Ali Shoeb, Jose Bohorquez, Member, IEEE, Joel Dawson, Member, IEEE,

    John Guttag, and Anantha P. Chandrakasan, Fellow, IEEE

    AbstractThis paper presents a low-power SoC that performsEEG acquisition and feature extraction required for contin-uous detection of seizure onset in epilepsy patients. The SoCcorresponds to one EEG channel, and, depending on the pa-tient, up to 18 channels may be worn to detect seizures as partof a chronic treatment system. The SoC integrates an instru-mentation amplifier, ADC, and digital processor that streamsfeatures-vectors to a central device where seizure detection isperformed via a machine-learning classifier. The instrumen-tation-amplifier uses chopper-stabilization in a topology thatachieves high input-impedance and rejects large electrode-off-sets while operating at 1 V; the ADC employs power-gating forlow energy-per-conversion while using static-biasing for com-parator precision; the EEG feature extraction processor employslow-power hardware whose parameters are determined throughvalidation via patient data. The integration of sensing and localprocessing lowers system power by 14x by reducing the rate ofwireless EEG data transmission. Feature vectors are derived at arate of 0.5 Hz, and the complete one-channel SoC operates from a1 V supply, consuming 9 J per feature vector.

    Index Terms1/f noise, algorithm design and analysis, ampli-fiers, biomedical equipment, brain, choppers, digital signal pro-cessing, electroencephalography, low-noise amplifiers, low-powerelectronics.


    R ECENTLY therapeutic and prosthetic devices havebegun emerging that hold great promise for the treatmentof patients with neurological conditions ranging from epilepsy[1], Parkinsons disease [2], narcolepsy [3], depression [4], andmotor impairments [5]. The ability to acquire targeted neuro-logical information from the brain is an essential requirementto the advancement of these systems. This implies the need tosense neural signals but, more critically, to use these in order toestablish correlation with the actual clinical states of interest.

    Manuscript received August 24, 2009; revised January 15, 2010. Current ver-sion published March 24, 2010. This paper was approved by Guest Editor AjithAmerasekera. The work of N. Verma was supported in part by the Intel Foun-dation Ph.D. Fellowship Program and NSERC. IC fabrication was provided byNational Semiconductor Corporation.

    N. Verma is with Princeton University, Princeton, NJ 08544 USA (e-mail:nverma@princeton.edu).

    A. Shoeb, J. Bohorquez, J. Dawson, J. Guttag, and A. P. Chandrakasanare with the Massachusetts Institute of Technology, Cambridge, MA 02139USA (e-mail: ashoeb@mit.edu; joselb@mit.edu; jldawson@mtl.mit.edu;guttag@csail.mit.edu; anantha@mtl.mit.edu).

    Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

    Digital Object Identifier 10.1109/JSSC.2010.2042245

    Brain monitoring thus introduces key challenges for electronicsystems in terms of both instrumentation and informationextraction.

    Seizure detection in epilepsy patients is an important appli-cation that is representative of the challenges. This paper de-scribes the details of an SoC that performs feature extractionfrom an analog EEG channel into the digital domain; the outputis used to detect the onset of seizures by way of a machine-learning classifier that is trained to patient-specific data. EEGsensing is targeted so that the system is noninvasive. This im-plies that microvolt signals must be acquired from electrodeshaving very poor output impedance while in the presence ofnumerous physiological and environmental interferences (e.g.,EMG, hum, etc.). Further, for reliable detection, subtle patient-specific EEG signal correlations must be determined over mul-tiple channels (up to 18). The following sections start by de-scribing the opportunity and algorithm approach for patient-spe-cific seizure detection. Then, the SoC is described from boththe system perspective and the IC implementation perspective.Finally, IC results are described, followed by a system demon-stration and conclusions.


    Epilepsy is a neurological disorder that causes a recurringabnormal firing in groups of neurons. As a result patients ex-perience seizures causing loss of coherence/cognition, loss ofmotor control, involuntary motion (convulsions), and possiblyeven death. Fig. 1 shows PET scan images highlighting a partic-ular firing pattern that is associated with seizures (ictal period)in the considered patient. The EEG during a seizure onset is alsoshown. Although EEG has the benefit that it is noninvasive, itscorrelation with seizures is complicated by the attenuation, 1/ffiltering, and spatial aliasing of the neural field potentials acrossthe skull and skin. Nonetheless, taking the recording in Fig. 1as an example, approximately 7.5 sec before the start of clinicalsymptoms, a subtle but characteristic change in the EEG can beobserved. If this electrical onset can be detected, an advancedsignal can be generated to warn the patient and caregivers, ac-tuate a therapeutic stimulator (e.g., [6], [7]), or trigger EEG datastorage for analysis by a neurologist.

    Although the critical variances in the electrical onset areminute and variable from patient to patient, [8] shows thatseizures are stereotypical for a given patient. Machine learningcan thus be used to train a classifier on a patient-by-patient

    0018-9200/$26.00 2010 IEEE

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    Fig. 1. 18-channel EEG showing onset of patient seizure (ictal); electrical onsetoccurs 7.5 sec before the clinical onset, which is characterized by muscle re-flexes causing the large excursion artifacts.

    Fig. 2. Seizure detection algorithm employing spectral analysis feature extrac-tion and SVM classification.

    basis, thereby simultaneously improving sensitivity and speci-ficity of detection. The following subsection briefly describesthe approach and parameters used in this system.

    A. Seizure Detection AlgorithmFig. 2 illustrates the detection algorithm. First, the EEG chan-

    nels are processed to extract specific bio-markers that are rele-vant for seizure detection. Clinical studies have determined thatseizure onset information is contained in the spectral energy dis-tribution of the patients EEG [9]. Accordingly, in this SoC thespectral energy of each channel is extracted to seven frequencybins over a two second window in order to form a feature vector.Up to 18 channels may be used, resulting in a complete featurevector of up to 126 dimensions.

    In order to distinguish between seizure and non-seizure EEG,machine learning is introduced through the use of a support-vector machine (SVM) classifier. The classifier must first betrained by providing it feature vectors that are labeled as cor-responding to seizure or non-seizure. These are used to estab-lish an optimal decision boundary between the two cases. Ac-cordingly, for real-time seizure detection, incoming test featurevectors are conceptually plotted (as illustrated in Fig. 2) to de-termine where they lie with respect to the decision boundary.The SVM radial-basis kernel is used for the classification com-putation (a description of the kernel can be found in [10]).

    The algorithm was validated through tests on 536 hoursof data over 16 patients. Ref. [8] shows that the approach ofpatient-specific learning simultaneously improves sensitivity,specificity, and latency (the values achieved are 93%, 0.3 0.7false alarms/hour, and 6.7 3 seconds, respectively).


    The physical partitioning and form-factor of the system haveimportant implications to patient usability, power consumption,and robustness. For instance, EEG sensing must be distributedaround the scalp in order to acquire spatial channels, but SVMclassification (over the multidimensional feature vector) mustbe centralized. As a result, the intermediate instrumentation,computation, and communication tradeoffs determine the ap-propriate system topology. Further, a critical application con-sideration is that, for chronic seizure detection, no cables canoriginate from the scalp, since these pose a strangulation hazardin the case where the patient begins convulsing. Accordingly,some form of wireless transmission from the scalp is essential.

    For sensing robustness, the acquisition circuitry (e.g., instru-mentation amplifier and ADC) is kept as close to the electrodesas possible to mitigate EMI and mechanical disturbance onwires carrying the microvolt EEG signals. As a result, the in-strumentation amplifier (and, to a lesser extent, also the ADC)must be distributed along with each electrode.

    The digitized EEG recordings can be robustly transmitted(i.e., wireless EEG) for central processing. However, local pro-cessing is beneficial for minimizing communication cost. InTable I, the system power for wireless EEG (where both fea-ture vector extraction and SVM classification are performed re-motely) is compared to that with local processing (where featurevector extraction is performed locally and only SVM classifi-cation is performed remotely). The power numbers are basedon actual measurements of the hardware prototype assuming18 EEG channels. The radio used is a commercially availablelow-power transmitter, ChipC