A Novel Low-Power-Implantable Epileptic Seizure-Onset Detector

568 IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, VOL. 5, NO. 6, DECEMBER 2011

A Novel Low-Power-ImplantableEpileptic Seizure-Onset Detector

Muhammad Tariqus Salam, Mohamad Sawan, Fellow, IEEE, and Dang Khoa Nguyen

Abstract—A novel implantable low-power integrated circuit isproposed for real-time epileptic seizure detection. The presentedchip is part of an epilepsy prosthesis device that triggers focaltreatment to disrupt seizure progression. The proposed chipintegrates a front-end preamplifier, voltage-level detectors, digitaldemodulators, and a high-frequency detector. The preamplifieruses a new chopper stabilizer topology that reduces instru-mentation low-frequency and ripple noises by modulating thesignal in the analog domain and demodulating it in the digitaldomain. Moreover, each voltage-level detector consists of anultra-low-power comparator with an adjustable threshold voltage.The digitally integrated high-frequency detector is tunable torecognize the high-frequency activities for the unique detection ofseizure patterns specific to each patient. The digitally controlledcircuits perform accurate seizure detection. A mathematicalmodel of the proposed seizure detection algorithm was validatedin Matlab and circuits were implemented in a 2 mm� chip usingthe CMOS 0.18- m process. The proposed detector was tested byusing intracerebral electroencephalography (icEEG) recordingsfrom seven patients with drug-resistant epilepsy. The seizuresignals were assessed by the proposed detector and the averageseizure detection delay was 13.5 s, well before the onset of clinicalmanifestations. The measured total power consumption of thedetector is 51 W.

Index Terms—Algorithm, epilepsy, low noise, low power, micro-electronics, seizure detector.

I. INTRODUCTION

E PILEPSY is a common medical condition characterizedby a predisposition to unprovoked recurrent seizures. A

seizure is the manifestation of an abnormal, hypersynchronousdischarge of a population of cortical neurons [1]. Approximately30% of patients, the majority of which suffer from partial (focal)seizures with or without secondary generalization, are refractoryto anticonvulsants. Not all refractory patients are good epilepsysurgery candidates due to an extensive area of epileptogenic-zone (EZ), multifocal, inability to localize the EZ, and an EZoverlying eloquent areas (language, primary motor, or visualareas) that cannot be resected without permanent sequelae [1].

Manuscript received October 04, 2010; revised January 19, 2011; acceptedMay 13, 2011. Date of publication June 23, 2011; date of current version De-cember 29, 2011. This paper was recommended by Associate Editor R. Genov.

M. T. Salam and M. Sawan are with the Polystim Neurotechnologies Labo-ratory, École Polytechnique de Montréal, Montréal, QC H3T 1J4, Canada.

D. K. Nguyen is with the Neurology Service, Department of Medicine, Notre-Dame Hospital (Centre Hospitalier de l’Université de Montréal), Montréal, QCH2L 4M1 Canada (e-mail: [email protected]).

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

Digital Object Identifier 10.1109/TBCAS.2011.2157153

As a result, the uncontrolled seizures bring a devastating impacton their quality of life.

Proof-of-concept experiments conducted in animals andhumans with epilepsy have demonstrated that focal electrical,thermal, or pharmacological manipulations of the EZ cansuppress seizure activity [2]–[5]. Over the last few years, therehas been growing interest in the development of implantabledevices as an adjunctive treatment for patients with refractorypartial epilepsy. So far, the vagus nerve stimulator (VNS)is the only Food and Drug Administration (FDA)-approvedmedical device for the treatment of epilepsy. This scheduled(open-loop) stimulation device provides a reduction in seizurefrequency; however, the overall effectiveness is modest [2],[3]. A cranially implanted responsive neurostimulator thattriggers stimulation only upon detection of a seizure holds thepromise of better seizure control, lower systemic, peripheraland central nervous system side effects, as well as lower batteryconsumption [2], [3], [5]–[7]. Preliminary results on a newresponsive device for the treatment of epilepsy (RNS system,Neuropace Inc.), Mountain View, CA, have been promising [2].Several issues remain to be addressed, such as the necessityof a reliable seizure detection system that is sensitive enoughto detect seizures early on but also specific enough to preventunwarranted triggering of focal intervention.

The initial steps required for the development of any re-sponsive focal therapy device for epilepsy are the recordingof intracerebral electroencephalography (icEEG) followed bythe automated detection of seizures. IcEEG recordings aregenerally performed using subdural strip and/or depth electrodecontacts. The recorded icEEG represents synchronous firing ofmany neurons throughout a region across the diameter of anelectrode contact. It is generally characterized by a low-am-plitude signal (microvolts) and low-frequency bandwidth.Due to the microvolt-level range, the neural signal must beamplified very carefully before further analysis (e.g., detectionand digitization). CMOS technology has relatively poor noiseperformance and the low-amplitude amplification requiresa CMOS amplifier with low input-referred noise [8]–[15].However, the restrictions on the power consumption and sizeof an implantable device limit increasing the biasing current.Therefore, design tradeoffs between the biasing current andnoise are required to optimize the performance of a device.

The challenges of seizure detection are variability in epilepticseizure onset pattern, signal amplitude, and spectral content.Over the past few decades, many seizure detection and pre-diction algorithms have been proposed [16]–[20]. However,these algorithms are carried out offline using high-performancecomputers. These types of algorithms cannot be employed

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SALAM et al.: A NOVEL LOW-POWER-IMPLANTABLE EPILEPTIC SEIZURE-ONSET DETECTOR 569

Fig. 1. IEEG recordings of two patients with refractory focal epilepsy and signal analyses. (a) Start of seizure activity characterized by low-amplitude fast activity.(b) Frequency analysis �� of (a). (c) Mean absolute amplitude �� analysis of (a). (d) Seizure activity of the second patient with an initial brief electricalseizures (BES) followed by an electroclinical seizure. (e) � of (d), and �� of (d).

in a low-power implantable microchip. More recently, a fewimplantable integrated seizure detectors have been proposed[8]–[10] and [21]–[26]. The earlier design of our seizure detec-tors [8]–[10] is based on several detection criteria in differentamplitude levels. The details will be explained in Sections IIand IV. The detection algorithm presented in [21] is based onclassifying icEEG data into events, and the events are relatedto a threshold voltage in the icEEG during high-frequencydischarges at seizure state. Since the detector [21] relies onlyon two threshold voltages (positive and negative), there isa high risk for false positive detections. The support vectorseizure detection machine [22] needs a high number of supportvectors in order to define the complex decision boundarybetween a patient’s seizure and nonseizure activity, explainingits high power consumption and cost. Similarly, the detectorbased on the linear-discriminant analysis classifier requireshigher complexity in digital signal processor (DSP) and ap-plication-specific integrated-circuit (ASIC) implementation toimprove sensitivity and specificity [23].

In this paper, we present a low-power-implantable CMOS in-tegrated seizure onset detector (SOD) for patients with medi-cally intractable epilepsy. The detector is part of an epilepsyprosthesis that triggers focal treatment to disrupt seizure pro-gression. This SOD includes implanted electrodes, a data-ac-quisition system, as well as analog and digital signal processorsin order to acquire and process real-time icEEG. The proposedSOD chip uses the specific seizure onset features of a patient inorder to detect their progressive increase of low-voltage fast-ac-tivity ictal pattern. The system is designed to have tunable pa-rameters, which would allow for the tradeoff between sensitivity(SXT), false detection (FD), and detection delay (DTD). The

tunability of the SOD provides higher accuracy on seizure de-tection. The adjustable gain of an amplifier can emphasize theamplitude level of interest, and variable threshold voltages of thevoltage level detectors (VLD) delimit the detected signal loca-tions and extract the information of frequency as well as a pro-gressive increase in amplitude. The SOD chip was tested offlineon seven patients with refractory epilepsy. The measured resultshave shown that the SOD maximizes the SXT and minimizesthe FD, which would tradeoff for the longer DTD, but prior tothe first clinical manifestations of the patients. The detection isexpected to be reliable in an implantable device without riskingfalse detections of physiological rhythms (e.g., sleep spindles).

The epileptic seizure detection algorithm is described in Sec-tion II and the global system in Section III. The proposed circuitsand their implementations are the subject of Section IV. Exper-imental results are presented in Section V, and conclusions aresummarized in the last section of this paper.

II. EPILEPTIC SEIZURE DETECTION ALGORITHM

Partial seizures originate primarily within discretely local-ized or more widely distributed networks limited to one cerebralhemisphere. They may subsequently generalize as the epilepticdischarge spreads contralaterally. Seizure onsets may vary frompatient to patient in terms of onset morphology, discharge fre-quency, focality, and spread pattern. Electrographically, severalpatterns can be seen at seizure onset, such as low-voltage andhigh-voltage fast activities or rhythmic spiking [1]. Fig. 1(a)shows the sudden appearance of the typical low-voltage fastactivity recorded from two intracerebral contacts positionedover the EZ, increasing in frequency [Fig. 1(b)], and am-plitude [Fig. 1(c)]. The icEEG is analyzed over the seizure


Fig. 2. Seizure detection algorithm. (a) Input signal � . (b) Modulated signalof � . (c) Output of VLDs � . (d) Digital demodulation � .

onset [Fig. 1(a)–(c)] and the SOD detects the high-frequencydischarge in icEEG, which may suggest an upcoming electro-clinical seizure. However, as seen in many icEEG recordings,some of these high-frequency discharges [e.g., Fig. 1(d)] can bevery brief (few seconds), remain very focal (without spread), donot evolve in time or frequency, and are clinically silent (elec-trical seizures). For most patients, it is probably not necessaryto target them as “seizures” warranting focal treatment, they cansimply be ignored. For this reason, the seizure onset detectioncriteria are preferably set as a high-frequency activity [Fig. 1(b)and (e)] showing a progressive increase in amplitude [Fig. 1(c)and (f)]. This should avoid false detections of interictal spikesand polyspikes, movement artifacts, physiological rhythms(e.g., sleep spindles), and brief asymptomatic high-frequencyvoltage activities or very brief electrical seizures which woulderroneously trigger unwarranted focal treatment.

The proposed seizure detector is specialized to detect veryspecific types of seizures characterized by their progressive in-crease of low-voltage fast activity. In this algorithm, the inputsignal [ in Fig. 2(a)] is modulated into high frequency

so that the instrumentation’s low-frequency noise doesnot affect the signal. Moreover, this modulation (1) convertsnegative signal amplitudes to positive amplitudes [Fig. 2(b)].Thus, positive hyper-excited threshold voltages ofa VLD are sufficient to detect the high frequency of . Thediscrete modulated signal confined to a time framepasses through number of VLDs to detect the specific fea-tures (2) characterized by a progressive increase in amplitudeand high-frequency variation. Fig. 2(c) shows the output of aVLD when it detects fast activities following (2):

(1)

where ,

'` for'` for otherwise

(2)

where . , and are tuned tothe specific seizure onset frequency of a patient so that

no false alarms occur during seizure detection. Due to the mod-ulation, VLDs detect a burst of pulses and unwanted high-fre-quency samples [Fig. 2(c)]. The following equation shows theelimination of false positive detections for the unwanted high-frequency samples:

'` for'` for otherwise

(3)

where is the pulsewidth of . The detected burstpulses are converted to a single pulse by

'` for and'` for otherwise.

(4)

The signal frequency is defined by the total numberof identified pulses confined to as follows:

(5)

Thus, seizure onset will be declared based on the following con-ditions (6):

'` Seizure,'` No Seizure otherwise.

(6)

The SXT of the algorithm is enhanced, and several decisionboundaries are introduced to reduce the number of FDs forthe patient’s specific seizure onset pattern. The signal analysisof this algorithm demonstrates that the early modulation andproper rectification of icEEG can identify the seizure onsetefficiently.

III. PROPOSED SYSTEM

The proposed implantable SOD provides continuous long-term monitoring of icEEG from the EZ. Fig. 3(a) illustrates theimplant configuration of the SOD, and the functional block dia-gram of Fig. 3(b) presents its architecture. The device will be im-planted within the skull and interface directly with the recordingsite using standard subdural/depth electrodes (diameter/size: 5mm and interelectrode spacing: 10 mm). This SOD consists ofa preamplifier, voltage-level detectors (VLD), digital demodu-lators (DD), and a high-frequency detector (HFD). In this SOD,several variable parameters ( , , and ) are in-troduced to facilitate higher accuracy in real-time seizure onsetdetection. controls the amplification of neural signals,

are used to adjust the threshold voltages of VLDs,and in HFD sets the tunability of the frequency detection.Fig. 3(b) shows that most of the signal processing in the SODis accomplished in the digital domain because of the relativelypoor noise performance of CMOS technology. The preampli-fier initially modulates the neural signal in and amplifies theinput amplitude level of interest. Subsequently, the VLDs con-vert the amplified signal to a digital signal . Oncethe signal is digitalized, there is little further possibility to add


Fig. 3. Proposed integrated SOD. (a) Implant configuration which shows the devices and two sets of electrodes—the sensing subdural electrodes and depth elec-trodes. (b) Block diagram of the proposed SOD chip.

Fig. 4. Dedicated chopper stabilizer circuit and corresponding frequency analysis of signals in different nodes.

noises in this signal. Then, the is demodulated to the orig-inal digital signal . Finally, the HFD determines the seizureonset frequency from processed signals and declares a seizuredetection without false alarm.

IV. CIRCUIT IMPLEMENTATION

As illustrated in Fig. 3, the SOD consists of four main func-tional blocks. The details are given below.

A. Preamplification

A dedicated chopper preamplification method was intro-duced in our previous work [10]. Fig. 4 shows the block diagramof the preamplifier and the frequency analysis of signals indifferent nodes. This figure demonstrates that the preamplifierinput signal is modulated by a signal with frequency , and theflicker noise and dc-offset voltage noise of the amplifierare attenuated by the high-pass filter, while the finite bandwidthof the amplifier and buffer band limit the thermal noise . Theproposed preamplifier is advantageous over the conventionalchopper preamplifier for the detection of epileptic seizures.

The comparison of the preamplifiers is shown in Table I.Fig. 5(a) illustrates the preamplifier construction, which consistsof an operational transconductance amplifier (OTA) [Fig. 5(b)],high-pass filter, and a buffer. These circuits provide a band-pass frequency response, which is produced by the preamplifier[Fig. 5(a)] and the bandpass filter that has a maximum of 80-dBmidband gain and 17 kHz (2 kHz to 19 kHz) bandwidth with6 input-referred noise. Moreover, the OTA has variable

TABLE ICOMPARISON OF THE CONVENTIONAL AND THE PROPOSED

CHOPPER PREAMPLIFIER

gain that can emphasize a specific amplitude range of the neuralsignal.

B. Voltage-Level Detector

A voltage level detector (VLD) consists of comparators, logicgates, DFF, and a buffer [Fig. 6(a) and (b)]. A low-power com-parator has been reported in [27] that includes two cascadedCMOS inverters, with the threshold voltage set by the aspect ra-tios of the transistors. The main disadvantage of this comparatoris the fixed threshold voltage in an integrated device. However,


Fig. 5. Preamplification front end. (a) Bandpass filter comprising an OTA, ahigh-pass filter, and a buffer. (b) Circuit of the OTA used in the preamplifier.

Fig. 6. Construction of VLDs: (a) Block diagram of VLDs. (b) Schematic of aVLD. (c) Circuit of a comparator.

a modified version of the comparator [Fig. 6(c)] provides vari-able threshold voltages

(7)

where and are the threshold voltage of the NMOS andPMOS devices, respectively; is the source to gate voltageof the Mcp1 transistor; and and

. Equation (7) shows that is the only vari-able parameter that can adjust the value of in an inte-grated circuit (IC). The variation of is proportional tothe bias voltage . The other advantages of the mod-ified comparator are: 1) negligible static power consumption,2) no hysteresis effect, and 3) relatively small transistor area.

In order to construct a VLD, two modified comparatorsare used. Fig. 6(a) shows several VLDs. The bias voltages

and set the variable lower andupper threshold voltages, respectively. The DFFcircuit removes unnecessary high-frequency samples.

C. Digital Demodulator

A digital demodulator (DD) includes an RC circuit and aVLD [Fig. 7(a)] that converts a burst of pulses to a single pulse.During a seizure, the VLD (Fig. 6) detects the abnormalities in

Fig. 7. Digital demodulator (DD). (a) Circuit. (b) Burst of pulses detected byVLD. (c) Voltage � across the RC circuit. (d) Output � .

Fig. 8. Microphotograph of the fabricated SOD chip.

signals and generates several bursts of pulses due to mod-ulation in the preamplification stage. In the DD, each input pulse

[Fig. 7(b)] charges the capacitor (Ceb) quickly, but the dis-charging time of Ceb is longer than the duration be-tween two consequent pulses of clock [Fig. 7(c)]. Thus, the Cebcannot be discharged completely during a burst of pulses. How-ever, a VLD connected to an RC circuit detects the end of a burst,where the Ceb discharges completely through a diode connec-tion of the Meb1 transistor and generates a pulse [Fig. 7(d)].

D. High-Frequency Detector

The high-frequency detector (HFD) [Fig. 7(b)] has two mainbuilding blocks: 1) a time frame selector (TFS) and 2) threefrequency detectors (FD). The TFS is based on a 14-b counterthat generates two different time frames and

in 13th and 14th b, respectively. The FD countsthe number of pulses received from the DD and resets allFDs at the end of every . Finally, the logic gates analyze theoutputs of FD and declare an upcoming seizure .

V. EXPERIMENTAL RESULTS

The SOD was fabricated in a CMOS 0.18- m process andoccupies 2 mm 1 mm of silicon area. A photograph of thefabricated chip is shown in Fig. 8.

A. IC Measured Performance

The test bench measurements were performed on five sam-ples of the fabricated chip and were presented consistently in


TABLE IIMEASURED FEATURES FOR THE FABRICATED SOD

Fig. 9. Measured results: (a) Variable gain of the preamplification front endwith changing � . (b) Gain response of the front-end preamplifier. (c) Com-parator threshold levels. (d) Time frame �� generation.

the results. The test bench supply voltage was set to 1.8 V, andthe measurements shown in Table II are based on averages overthe set of test chips. The observed measured variation over thetest chips was within 5%.

The measured input-output characteristics [Fig. 9(a)] of thefront-end preamplifier show that the variable gain of the pream-plifier can emphasize a specific amplitude range of the neuralsignal. The preamplifier gain frequency response is shown inFig. 9(b). The maximum achieved measured gain of 66 dB wasobtained over a 3-kHz to 5-kHz frequency, and the cutoff fre-quencies were measured at 100 Hz and 6.5 kHz. The outputvoltage range is 50 to 450 mV while the VLD detectsthe desired amplitude of with 30-mV incremental/decre-mental steps very precisely. Fig. 9(c) shows the dc sweepingof the modified comparator with different threshold voltages

. Table II shows the lowest and highest threshold volt-ages and 495 mV, respectively.Fig. 9(d) shows the variable time frames and gener-ated using different clock frequencies. The generated variedfrom 1.3 to 8 s.

B. Patient Selection Methodology

This study was conducted at Notre-Dame Hospital, CentreHospitalier de l’Université de Montréal (CHUM). The pro-posed detector was validated using intracerebral recordingsfrom seven patients with refractory epilepsy who underwentan intracranial study to better delineate the epileptogenic zone.Previously, these patients had undergone a comprehensivepresurgical evaluation, such as video-scalp EEG, a brain mag-netic resonance study (MRI), ictal single-photon emissioncomputed tomography (SPECT), positron emission tomog-raphy (PET), magnetoencephalo-graphic (MEG) study, and anEEG-functional MRI (EEG-fMRI). These complementary non-invasive studies failed to adequately localize the epileptogeniczone, and invasive intracranial electrode studies were requiredto delineate with more precision the EZ. In these studies,intracranial electrodes were implanted through a craniotomyor burr holes under general anaesthesia. Later, patients weretransferred to the epilepsy monitoring unit for continuousvideo-EEG telemetry to record seizures. The patients, whohad seizure onsets characterized by a progressive increaseof low-voltage fast activity in icEEG recordings, were goodcandidates for the proposed detection validation.

C. Method of Case Studies

Seven patients (age: 15 to 49) with intractable nonlesionalpartial epilepsy, who were candidates for epilepsy surgery,underwent an intracranial study to better delineate the EZs(Table III). A combination of depth and subdural (strip and/orgrid) electrodes were implanted over suspected areas of epilep-togenicity (e.g., hippocampus, insula, medial frontal gyrus,orbital frontal cortex, etc.) through a craniotomy window or burrholes. Following the implantation of intracranial electrodes,the patient underwent a long-term video-EEG recording in theepilepsy monitoring unit (Notre-Dame Hospital, Montréal).The use of intracerebral recordings from epileptic patientsundergoing an invasive study for the validation of our systemwas approved by the Notre-Dame Hospital ethics committee.Recorded seizures were carefully analyzed to identify the EZ.The EZ, seizure characteristics, and seizure detection resultsof the seven patients in this study are listed in Table III. Com-mercially available equipment was used to record the icEEGsignal during a seizure from two contacts located in the EZ.The seizure signals of cases 1, 2, 3, 5, and 6 were recordedusing depth electrodes and the signals of cases 4 and 7 wererecorded using subdural strip electrodes. These signals werefed into the proposed seizure detection algorithm (Matlabsoftware) and SOD chips. The proposed detector can handleup to two contacts subdural electrodes or depth electrodes.Seizure detections were tested on various seizures from sevenpatients (average of five seizures for each patient) due to theheterogeneity in signal amplitude and frequencies observedat ictal onset. Parameters of the detector were tuned for eachpatient based on time frequency and time amplitude analysisof a seizure signal and three or four brief electrical seizures.The detection performances in terms of DTD of bothMatlab analysis and the fabricated chip results are presented inSection VI.


TABLE IIICASE STUDIES OF SEVEN PATIENTS WITH PARTIAL EPILEPSY AND MATLAB ANALYSIS

Fig. 10. Seizure onset detections where icEEGs were recorded from different locations in patients, the zoom inset shows signal analysis and detection: � isicEEG of the seizure recorded using two contacts from the EZ, � is the frequency analysis of � , � is the mean absolute amplitude analysis of � ,� –� are high-frequency detections, and � is seizure onset detection: (a) Case 1. (b) Case 2. (c) Case 3.

D. Validation of the Seizure Detection Algorithm

The proposed seizure detection algorithm is evaluated byapplying the recorded icEEG from seven patients. The per-formance of the algorithm is shown in Table III. Case 1 isa 24-year-old male with drug-resistant partial epilepsy sinceage 18. During the intracranial study, several seizures wererecorded, all originating from the right medial temporal lobe(hippocampus) and spreading to the lateral temporal neocortexand the insula [Fig. 10(a)]. The seizure signal is analyzed intime and frequency domains [inset of Fig. 10(a)] in order toset the , , and . The frequency domaindemonstrates that the seizures were electrically characterizedby an initial low-voltage tonic alpha activity (12 Hz) evolvinginto rhythmic spiking while amplitude in the time domainincreased progressively.Table III shows that the seizure wasdetected 12 s after ictal onset. In case 2, seizuressimilarly started from the left hippocampus with an initiallow-voltage fast activity pattern, before spreading to the left

occipital region. The signal analysis [Fig. 10(b)] shows that theincrease of signal frequency and progressive amplitude werefound at 7 s. Case 3 shows a higher signal frequency(20 Hz) at seizure onset [Fig. 10(c)] that started to decreasewith the progressive increase of its amplitude. The seizure ofcase 3 was detected at 10 s.

Case 4 had seizures which electrically started as a diffuseslow wave followed by desynchronization and regional low-voltage high-frequency activity over several temporal neocor-tical contacts [Fig. 11(a)]. The algorithm ignored the brief elec-trical seizure (as specified by the clinician) and detected theelectroclinical seizure after 24 s. The seizure onset of case 5was initially characterized by fast activity without increasingthe amplitude [Fig. 11(b)]. The signal frequency then suddenlydrops for 2 s, followed by a higher frequency signal and pro-gressive increase in amplitude which are detected 12s). Fig. 11(c) shows icEEG recordings from case 6 of two briefelectrical seizures (ES) followed by an electroclinical seizurethat started with low-voltage fast activity that quickly increased


Fig. 11. IcEEG analysis and seizure onset detection using Matlab. The icEEG �� of a seizure recorded using two contacts from the EZ, frequency analysis � ,mean absolute amplitude analysis � , the high-frequency detections � –� , and � is seizure onset detection: (a) Case 4.(b) Case 5. (c) Case 6. (d) Case 7.

TABLE IVTUNEABLE PARAMETERS’ VALUES OF THE SOD CHIPS AND AVERAGE

DETECTION DELAYS

in amplitude and decreased in frequency. Finally, the seizureonset of case 7 was characterized by a rapid increase of fre-quency and progressive amplitude increase of the signal [Fig.11(d)]. Overall, the proposed algorithm maximizes the sensi-tivity and specificity of the detection, with a slightly longer de-tection delay as a tradeoff. In these experiments, the seizures ofseven patients were detected on an average of 13.8 s (min: 7 sand max: 25 s) prior to first clinical manifestations.

E. Validation of the SoD Chip

Following the validation of the seizure detection algorithm inMatlab, the IC of the SOD was tested by using the same seizurerecordings from the seven patients mentioned before. TheicEEG recordings were modulated, amplified, and analyzed inthe frequency and time domain in order to set the thresholdvoltages of two VLDs (Table IV). The seizure detection onicEEG recordings of 3 patients is shown in Fig. 12.Outputs of the VLDs ( and ) were fed into an HFD to

extract the frequency information (the zoom inset of Fig. 12).The HFD had three 3-b counters and a variable generatorthat detected the seizure onset at an early stage of a seizure. TheSOD ignored all of the preictal activities (as set by the clinician)and detected the electroclinical seizures of the seven patients

13.5 s after onset, well before onset of clinical manifestations( 12 s prior). Table IV shows the tunable parameters valuesand the average seizure detection delays of the SOD chips forall cases.

The proposed system is compared with recently publishedseizure detectors based on events (ESD) [21], nonlinear energy(NLESD) [23], and spectral energy (SESD) [22] in Table V.The detectors presented in [21] and [23] do not have a neuralsignal amplifier, and corresponding results are based on cir-cuits simulation. The seizure detector from [22] was fully in-tegrated in CMOS 0.18- m technology, but the seizure detec-tion results shown are based on a software simulation platformusing scalp EEG and no experimental results on seizure detec-tion were reported. The proposed detector in this paper is a fullyintegrated device and the experimental results were based onicEEG recordings from different locations in the human brain.Furthermore, the power consumption of the proposed detectoris 7 times lower than the one presented in [21]. The DTD variesdepending on the patient’s specific ictal onset pattern. Althoughthe average DTD of the proposed SOD is 5 s higher than the de-tector described in [21], the SXT and specificity of the proposedSOD are maximized 100% to prevent unwarranted stimulation;however, SXT of ESD [21], SESD [22], and NLESD [23] are95.3%, 94.35%, and 93%, respectively. The DTD and SXT ofother seizure detectors [24]–[26] are unknown.


Fig. 12. Measured seizure onset detection by the SOD chip, where � is the icEEG of seizure from EZ, � –� are high-frequency detections in the icEEG,and � is the seizure onset detection. (a) Case 1. (b) Case 2. (c) Case 3.

TABLE VCOMPARISON WITH THE LATEST COMPETITIVE RESULTS

Fig. 13. Comparative results of analyzing the same icEEG recordings with sev-eral detection methods.

Further, a comparative analysis on seizure detection perfor-mance is demonstrated by using the same icEEG recordings

of seven patients with several detection methods, such as ESD[21], SESD [22], and NLESD [23]. Detection parametersof these methods were tuned for each patient according to[21]–[23]. These detection methods were validated in Matlaband comparative results of the detection methods using thesame data are shown in Fig. 13. The result shows that theESD, NLESD, and SESD methods have lower SXT becausethe methods sometimes detected brief high-frequency bursts asseizures, but did not detect the seizure onset characterized bylow-voltage fast activity. If the detection parameters of ESD,NLESD, and SESD methods were adjusted to low-voltage fastactivity seizure onset, the methods detected low-amplitudephysiological rhythms and other similar activity. Therefore, theproposed algorithm is based on the time frequency and time am-plitude analysis, and avoids false detections of interictal spikesand polyspikes, movement artifacts, physiological rhythms


(e.g., sleep spindles), and brief asymptomatic high-frequencyvoltage activities or very brief electrical seizures. The averageDTD of the proposed SOD is higher than the ones given by theother methods, but well before onset of clinical manifestations.Moreover, the external low-frequency instrumental noise maycause false detection. Thus, the proposed algorithm focusesmore on noise reduction; however, the detection algorithm ofthe RNS system [2] is intended for data reduction. Furthermore,the detection criteria of the RNS system are based on high-fre-quency tracking of amplitude variations in icEEG recordings,but the proposed algorithm detects a progressive increase of thehigh-frequency signal in icEEG. In addition, the total powerdissipation and DTD of the RNS system are unknown.

VI. CONCLUSION

We have described the design and implementation of a newimplantable SOD chip responsive to ictal low-voltage fast ac-tivity patterns, focusing on low power and on the noise reductionof involved circuits. Experimental results, reported from sevenpatients with drug-resistant partial epilepsy, demonstrate thatthe early modulation and proper rectification of icEEG can iden-tify the progressive increase in amplitude and high frequencyof the signal efficiently. The fabricated SOD chip modulatesicEEG recordings, amplifies the desired amplitude level of thesignal, extracts fast activity information using VLDs, demodu-lates the signal to extract the original frequency using the RCcircuit, and detects the seizure by evaluating the frequency offast activities and the progressive increase in amplitude.

ACKNOWLEDGMENT

The authors would like to thank the NSERC for their support,the Canada Research Chair in Smart Medical Devices, le FondsQuébécois de la Recherche sur la Nature et les Technologies(FQRNT), and the EEG technicians at Notre-Dame Hospital,Montréal, QC, Canada.

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Muhammad Tariqus Salam received the B.A.Sc.degree in electrical and electronics engineering fromIslamic University of Technology, Bangladesh, in2003, the M.A.Sc. degree in electrical and computerengineering from Concordia University, Montréal,QC, Canada, in 2007, and the Ph.D. degree inelectrical engineering from École Polytechnique,Montréal.

Currently, he is with the Polystim Neurotechnolo-gies Laboratory and the Epilepsy Monitoring Unit,CHUM—Hôpital Notre-Dame, Montréal, where his

research focuses on implantable microdevices for the pre-surgical evaluation of


patient candidates for epilepsy surgery and ultra-low-power seizure detectorsand subsequent treatments, including direct drug delivery and electrical stim-ulation. He was a Research Assistant and Teaching Assistant with ConcordiaUniversity in 2006, Lecturer at Prime University, Part-Time Electrical Engineerwith the Bengal Company in 2003, and an intern with the Atomic Energy Com-mission and Ghoralshal Power Station in 2002.

Mohamad Sawan (S’88–M’89–SM’96–F’04)received the Ph.D. degree in electrical engineeringfrom Université de Sherbrooke, Sherbrooke, QC,Canada, in 1990.

He joined Ecole Polytechnique, Montréal in 1991,where he is currently a Professor of Microelectronicsand Biomedical Engineering.

Dr. Sawan is Deputy Editor-in Chief of the IEEETRANSACTIONS ON CIRCUITS AND SYSTEMS-II:EXPRESS BRIEFS, Associate Editor of the IEEETRANSACTIONS ON BIOMEDICAL CIRCUITS AND

SYSTEMS, and Editor of Mixed-Signal Letters. He is founder of the Interna-tional IEEE-NEWCAS Cnference and of the Polystim NeurotechnologiesLaboratory, and Co-Founder of the International IEEE-BioCAS Conference,and the International IEEE-ICECS. His scientific interests are the design andtesting of analog and mixed-signal circuits and systems, signal processing,modeling, integration, and assembly.

Dr. Sawan received the Barbara Turnbull 2003 Award for spinal cord research,the Medal of Merit from the President of Lebanon, the Bombardier Medal ofMerit, and the American University of Science and Technology Medal of Merit.Dr. Sawan is Fellow of the Canadian Academy of Engineering and Fellow of

the Engineering Institutes of Canada. He is also “Officer” of the National Orderof Quebec. He holds the Canada Research Chair in Smart Medical Devices, andhe is leading the Microsystems Strategic Alliance of Quebec.

Dang Khoa Nguyen received the M.D. degree andcompleted his neurology residency at the Universityof Montreal, Montreal, QC, Canada.

Currently, is an Associate Professor of Medicineat the University of Montreal, with expertise inepilepsy. His training included a two-year fellowshipat Yale University, New Haven, CT, with specializedformation on the care of complex refractory epilepticpatients, presurgical evaluation of patients who arecandidates for epilepsy surgery, and interpretationof continuous video-EEG monitoring using scalp

or intracranial electrodes. He is currently practicing at Notre-Dame Hospital,Montreal, where he is the Director of the Epilepsy Monitoring Unit. Hisresearch interests focus on the study of medically intractable epilepsies, espe-cially nonlesional cases. He and collaborators are developing and evaluatingnovel methods to better localize the epileptogenic zone, allowing its surgicalresection in refractory cases: electrical impedance tomography, near-infraredspectroscopy, high-field magnetic resonance imaging with phased array coils,functional magnetic resonance imaging combined with electroencephalog-raphy, magnetoencephalography, and novel intracranial electrodes. His team isalso involved in several international trials testing novel antiepileptic treatmentoptions: retigabine, pregabalin, brivaracetam, lacosamide, and vagus nervestimulation.

A Novel Low-Power-Implantable Epileptic Seizure-Onset Detector

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