Low-Power DWT-Based Quasi-Averaging Algorithm and Architecture for Epileptic Seizure Detection

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1 Low-Power DWT-Based Quasi-Averaging Algorithm and Architecture for Epileptic Seizure Detection Himanshu Markandeya 1, Georgios Karakonstantis 1, Shriram Raghunathan 2, Pedro Irazoqui 2 and Kaushik Roy 1 1 School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA {hmarkand, gkarakon, kaushik}@purdue.edu ABSTRACT In this paper, we have developed a low-complexity algorithm for epileptic seizure detection with a high degree of accuracy. The algorithm has been designed to be feasibly implementable as battery-powered low-power implantable epileptic seizure detection system or epilepsy prosthesis. This is achieved by utilizing design optimization techniques at different levels of abstraction. Particularly, user-specific critical parameters are identified at the algorithmic level and are explicitly used along with multiplier-less implementations at the architecture level. The system has been tested on neural data obtained from in-vivo animal recordings and has been implemented in 90nm bulk-si technology. The results show up to 90 % savings in power as compared to prevalent wavelet based seizure detection technique while achieving 97% average detection rate. Categories and Subject Descriptors B.7.1 [Integrated Circuits]: Types and Design Styles VLSI (very large scale integration) General Terms Algorithms, Design Keywords Epilepsy, Seizure Detection, Low Power, Biomedical. 1. INTRODUCTION Technology scaling has allowed the integration of millions of transistors, enabling more complex functionality to be implemented in a single silicon chip. However, such complex functionality may require significant amount of computation leading to power consumption of insurmountable proportions. This is further aggravated in biomedical applications, especially in case of implants, which are powered by limited energy source and must ensure reliable operation to avoid any catastrophic failure. Hence, the designs of biomedical implants have stringent constraint on power consumption. This is to keep the on-chip temperature within safe limits and to increase durability, reducing the frequency of replacement of the energy source, which might be expensive, requiring intrusive surgery. This paper considers one such implantable application Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ISLPED 10, August 18 20, 2010, Austin, Texas, USA. Copyright 2010 ACM /10/08...$ Department of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA {sraghun, pip}@purdue.edu Epileptic Seizure Detection -- wherein, it is absolutely essential to conserve energy. Epileptic seizure is a dynamically nonstationary transient symptom of excessive or synchronous neuronal activity in certain sections or the whole of the brain. Often, it culminates into physical convulsions, commonly known as fits. Such a condition prevalent in human body is called Epilepsy. It is one of the most common chronic neurological disorders. Medical treatments for epilepsy have focused mainly on controlling the occurrence of seizures and not on curing it. Among the estimated 50 million people affected worldwide, about 30% do not show any improvement in reduction of seizure frequency with medication [1]. This justifies alternative treatments, among which electrical stimulation seems to be a promising one [2]. Some of the commercially available stimulators are based on continuous stimulation which arguably has long term negative effect [1]. Hence, responsive stimulation is preferable mode of treatment. This, however, has prerequisite of a correct and confident detection of seizure, before administration of necessary treatment/stimulation. Hence, it is essential to design an algorithm which detects epileptic seizures with high efficacy. The type of Epilepsy under consideration in this paper is Focal Temporal Lobe Epilepsy. The hippocampal structures in the brain have been documented to be the epileptogenic focus for most temporal lobe epileptics. We use a rat model for modeling human epilepsy [1]. An efficient detection of the onset of the seizure requires a fair amount of processing of the recorded neural signals. This results in high complexity hardware and hence increased power consumption. However, for such a system, performance (in terms of speed) is not of utmost importance due to the relatively low frequency spectrum of the neural signals. This relaxation in the performance constraint enables the usage of various techniques such as voltage and frequency scaling to implement ultra low power systems without compromising significantly on the functionality or efficacy of the system. Decades of research have resulted in a variety of algorithms, many of which are very promising in terms of detection rates [1]. However lack of power efficient hardware implementation of these algorithms on the silicon chip has been the primary obstacle in making market-ready closed loop epilepsy prosthesis available [1]. The majority of the existing seizure detection systems are based on the Electroencephalogram (EEG) which is the neural recording at the surface of the brain. Since the brain is a timevariant non-linear system, the recordings in EEG are a complex combination of all the signals that reach the surface after undergoing many transformations. These result in significant nonlinear modulation of the seizure signals making it harder to detect, thereby, requiring increased signal processing prior to detection of the seizure onset. It should also be noted that the seizure would already have spread through the brain before reaching the surface 301

2 creating latency between its origin and detection. On the contrary, recent developments have made it possible to record cumulative neural activity within a volume of brain tissue using an electrode tip. These are termed Local Field Potentials (LFP) [1]. Being at close proximity to the epileptogenic foci, LFPs provide a much cleaner signal for extracting the features that characterize seizures. The proximity to the origination point of the seizure helps to overcome the latency of seizure spreading through the brain before being detected (as is in the case of EEG). Thus, implants or epilepsy prostheses can make good use of LFPs for early detection and quick mitigation of seizure. The detection of seizures can be based on the observation that there is a gradual surge in amplitude of the signal in specific frequency bands. Numerous algorithms make use of this fact by analyzing the Fast Fourier Transform (FFT) of the recorded signals. However, the use of FFT results in loss of temporal information and affects the detection accuracy. Short-Time Fourier Transform (STFT) by Gabor retains the temporal information by applying the FFT through a fixed size time window [3] [4]. Also, FFT engine being computationally intensive, due to large number of calculations required might result in a power hungry architecture. Apart from this, the false alarms (false positives) due to neural recordings with seizure-like characteristics are a cause of concern. A higher proportion of false positives degrades the detection rate of the algorithm and results in undesirable stimulation. Such undesirable stimulation results in wasteful usage of energy and is detrimental to the neuronal environment as well. In this paper, we have used Discrete Wavelet Transform (DWT) to process neural signals and detect the onset of seizure. DWT preserves both temporal and frequency domain information contained in the signal. Since the temporal window size in a DWT is variable, it leads to a much higher time-frequency resolution of the signal as compared to FFT or STFT [4]. This enables detection of the seizure onset with a significantly higher accuracy as compared to other methods (viz.fft/stft) and lower probability of false positives [1]. DWT decomposes the input signal into its comprising frequency components called coefficients viz. approximation and detail coefficients. While the approximation coefficients correspond to the lower frequencies contained in the signal, the detail coefficients correspond to the higher frequencies in the signal. DWT has been used fairly recently as a tool for extracting features from the electro-encephalogram (EEG) to identify a seizure [5][6]. Various existing wavelet based algorithms use Artificial Neural Networks (ANNs) to analyze the recorded EEG signals [6][7][8]. Although, these are helpful for analyzing the seizure data, they come at the cost of increased detection latency. Apart from that, the use of ANNs would make the system power hungry. Algorithms, which use simpler event-based methodology by monitoring spiking neural activity [1], consume lower power. However, their efficacy could be debated under noisy recording conditions. In this paper, the information preservation property of wavelet transform is combined with the simplistic multiplier-less and memory-less implementation techniques. This results in low-power userspecific seizure detection architecture. Figure 1 illustrates the flowchart of the proposed seizure detection technique. In Section 2, we discuss the overview of the proposed design methodology. Next, in Section 3, we describe the design of the seizure detection algorithm in detail along with the training methodology associated with it and its mapping to a power efficient architecture. Section 4 Input: Recorded Data of the subject Algorithm Training Wavelet Transform and Quasi-Averaging Determine the user specific critical parameters Threshold (Th), Detail Coefficients (Di), Weights (Wi) Implementation Map the algorithm to an architecture using critical parameters & explore design techniques for low power Wavelet Transform architecture Low Power Seizure Detection System Figure 1. Flowchart of Proposed Seizure Detection Technique discusses the post implementation analysis methodology for calculation of efficacy and efficiency associated with the algorithm implementation. It also summarizes the results from a real time simulation on live animal data. The circuit performance parameters viz. the power dissipation and area for various architectures used are also reported. Finally, conclusions are drawn in Section DESIGN METHODOLOGY OVERVIEW As mentioned in Section 1, at the algorithm level, wavelet transform is utilized as a signal processing tool to extract seizure specific features from the LFPs recorded at the epileptogenic focus. Due to the inter-subject variability of LFPs, the algorithm needs to be tuned to user-specific cases. We use the detail (DWT) coefficients, for analyzing the signal and detecting the onset of seizure. These are selected based on the level of accuracy needed in detection and the observed characteristics of the recorded LFPs. It is followed by simple, real-time, weighted averaging, to smoothen out the occasional spikes which might result in a false positive. In order to make a real-time low-power memory-less implementation feasible, the averaging operation is implemented as a quasi-averaging operation. Due to elaborate processing using DWT, the quasi averaging approximation does not degrade the efficacy of the algorithm. Since the algorithm works on a continuously moving window, it results in more accurate characterization of the onset of seizure as it takes each recorded data into account. The onset detection signal is the processed signal that is used for generating the detect flag at the onset of the seizure. This is computed using weighted quasi-averaged values of the DWT coefficients over a moving window. The weights needed for this purpose are calculated by performing a statistical study of the neural recording history of the subject. This onset detection signal can be compared to a pre-fixed threshold value to indicate detection. The parameters (DWT coefficients, quasi-average weights & threshold) in the algorithm (Figure 1) can be fixed in the Training phase. Since the training is based on a data set of a specific subject, the selected parameters as well as the corresponding hardware implementation is user-specific. The weights and the coefficients are selected so that the usage of power consuming logic elements in the circuit can be minimized. This would result in the algorithm and the implemented architecture, both being scalable for each individual subject. Subsequently, with the help of the user specific constant 302

3 parameters, the algorithm is mapped on to an energy/power efficient architecture. At the architecture and circuit levels, low power design techniques are used to reduce power consumption. The lower frequency of operation, due to slower speeds required, provides a good possibility of operating the circuit at scaled V DD. In addition, the use of multiplier-less filters in implementation of the wavelet transform block and memory-less approximation for averaging helps in further reduction of power consumption. This would result in a low-power implantable architecture for epileptic seizure detection prosthesis. The algorithm and its implementation are explained in detail in the next section. 3. DESIGN AND IMPLEMENTATION In this section, we describe, in detail, the design technique for the algorithm, followed by the training methodology used to tune the algorithm. Further, we discuss the mapping of the algorithm to a feasible hardware using energy efficient architectures. A. Algorithm The algorithm is designed on the basis of the observation that, during seizure, there is a gradual surge in the amplitude of the signal in certain frequency bands in the LFP spectrum. However, the bandwidth of these frequencies of interest is in tens of Hz. Due to such narrow bandwidth it is infeasible to isolate these frequencies using simple filtering techniques (such as using a band pass FIR filter). A preliminary analysis shows that to obtain the required narrow bandwidth and sharp cut off, a very high order filter (order of 300) is needed. Implementation of a filter of such high order is practically infeasible. This problem can be overcome by using the interleaving property of DWT. While computing the DWT of a signal over multiple levels, down sampling by factor of 2 enables the use of wider bandwidth filters in succession to effectively achieve a very narrow bandwidth. Thus, the characteristic change in the amplitude of the LFP recordings can be captured by the algorithm in specific frequency components (DWT coefficients). The LFPs from the epileptogenic focus are digitized in 9-bit 2 s complement form and provided as the input to the algorithm. We use the Daubechies-4 (DB-4) mother wavelet function to compute the DWT of the input. The DB-4 mother wavelet has been shown to have the spiking and smoothening consistency with the neural signals prevalent in the brain [6]. The wavelet transform is applied to obtain six levels of DWT coefficients. Each of these levels corresponds to a pair of narrow band of the component frequencies in the neural signal. Depending on the frequencies of interest for a particular subject, specific DWT coefficients are selected for computation of the onset detection signal. Subsequently, these coefficients are quasi averaged over a continuously moving window. Quasi-averaging is an approximation technique, which accurately models the average of a continuously moving window. A preliminary analysis and comparison with standard average definition over a randomized set of data shows that the mean square error involved in this approximation is of the order The approximation is based on the assumption, that each individual data point in a window (being averaged) can be represented by the average itself. It should be noted that the lengths of the wavelet coefficient vectors at subsequent levels in DWT differ by a factor of two [3]. These lengths can be equalized by adjusting the window size. Next, the windowed quasi-averages of all the selected coefficients are weighted appropriately and added to generate the onset detection Figure 2. Six Level 1-D DWT of Neural Recording signal. Due to successive filtering, the amplitudes of the processed data become very large. The weights are needed to normalize these amplitude levels. Finally, the onset detection signal is compared with a preset threshold (fixed in training) to identify and detect a seizure. However, the above designed algorithm uses several parameters, which are tuned or selected based on user specific requirements in the training phase. B. Training In order to train the algorithm, we apply it to a set of training data. The training data is the digitized LFP recording consisting of both the baseline and the seizure signal. The baseline signal refers to the part of neural signal when there is no electrographic or visual evidence of seizure. It is essential to include the baseline in the training data set in order to train the algorithm to minimize detection of false positives. This data is analyzed using Matlab. The first step in the training is computation of a 6-level DWT of the training data. Next, the DWT coefficients, isolating the seizure like activity prior to the onset, are identified. Due to a large inter-subject variability in the neural recordings, the coefficients selected for the detection of onset would vary between subjects. An example of such decomposition is illustrated in Figure 2. As can be observed, the three coefficients viz. D4, D5 and D6 show significant seizure like activity. These are the DWT coefficients in the 4 th, 5 th and 6 th level decomposition of the wavelet transform. Since the sampling frequency of the neural recording is 1.5 KHz, decomposition levels correspond to frequency bands of Hz, 24-48Hz and Hz respectively. The above mentioned DWT coefficients are selected and subjected to a statistical analysis using CDF (Cumulative Distribution Function) in the subsequent step for training. We use a window size of 4096 data values for this analysis. The CDF curves are then cut off at 98% point. Note that the cut-off point can be changed depending on the required sensitivity and the epileptic history of the subject. Next, the ratio of the magnitudes of D4:D5:D6 is evaluated at the cut-off point. In the decomposition shown in Figure 2, this ratio was computed and approximated to be 0.5:1:2. Subsequently, the equalizing weights are chosen according to the ratio obtained from the statistical analysis. These weights are used to generate the onset detection signal using the algorithm described in previous section. Finally, depending upon the required sensitivity, a threshold is chosen as a percentage of the maximum value of the onset detection signal. For the illustrated data set, a threshold of 75% of the maximum value was chosen. Figure 3 shows the detection signal obtained using the algorithm for a sample training dataset. 303

4 H 2 C j+1 Seizurelike events c j H 2 G 2 C j+1 d j+1 G 2 d j+2 Figure 3. Training Data Set Analysis with Seizure Detection It can be deduced visually that it consists of 3 seizure-like events. Note that, during the seizure event, the detection signal shows a gradual increase in the amplitude. This amplitude surge, in combination with a properly selected threshold, can be used to raise a detection flag. The wavelet transform filters away the high frequency components comprising the occasional spiking activity which could have led to false detection. The occasional spikes in the selected frequency bands, are further smoothened out by the quasi averaging operation over the moving window. Next, we map the algorithm to an energy efficient architecture. C. Architecture Mapping In order to map the algorithm into an implementable architecture, we discuss some of the architecture level techniques to reduce hardware and hence, the energy consumption of the system, without degrading the efficacy. A top level block diagram of the system is shown in Figure 4. It consists of a Wavelet Decomposition block followed by the Quasi-averaging and the Thresholding block. (i) Wavelet Decomposition Block The wavelet decomposition block is the most computationally intensive and power hungry block in the system. It consists of the discrete wavelet transform block to compute the DWT of the input and a counter to synchronize its operation. There are various ways suggested in the literature to implement efficient wavelet transform architecture [9]. These architectures are generally apt for data compression applications and result in the loss of the intermediate derivable coefficients. However, the requirement of this system is to retain these coefficients in real time. Hence, the folded architectures [9] cannot be utilized without the use of memory elements and are ruled out. In contrast to this, a simpler approach can be taken to implement the wavelet transform in accordance to Mallat s algorithm [3] (Figure 5). According to Mallat, the coefficients of a wavelet transform can be computed Wavelet Decomposition Quasi Averaging Thresholding Clock COUNTER (CLOCK DIVIDER) 9-bit 2-C Input DISCRETE WAVELET TRANSFORM BLOCK QUASI AVG BLOCK (D4) *W 1 QUASI AVG BLOCK (D5) *W 2 QUASI AVG BLOCK (D6) *W 3 R E D A R T O A R P A M O C THRESHOLD Figure 4. Block Diagram of the System Architecture DETECT FLAG Figure 5. Mallat s Algorithm representation for finding DWT using a fast algorithm which consists of cascaded discrete convolutions with a high pass filter (G) and a low pass filter (H) with intermediate outputs being sub-sampled. In order to implement the algorithm, which evaluates on the basis of D4, D5 and D6 DWT coefficients, 6 cascaded stages of the G and H filters of 8 th order are needed. The number of G filters can be reduced as D1 through D3 coefficients are unused. Each of these filters would require very large number of power intensive multiplier and adder blocks. In order to overcome this problem, energy efficient wavelet transform block can be constructed using multiplier-less architecture for FIR filter (G and H). We achieve this using the Computation Sharing Multiplier (CSHM) architecture for the filters. Another commonly used multiplierless technique for achieving power savings is by elimination of the common sub-expressions within the filter coefficient vector. Such approach is referred to as Common Sub-expression Elimination implementation (CSE) [10]. CSHM is based on the principle that in vector scaling operations, any scalar s i can be decomposed into smaller bit sequences a k (alphabets) such that s i can be rebuilt from these sequences by few shifts and adds. Using these alphabets the coefficient vector (C) can be constructed spanning the entire set of filter coefficients [10]. For instance, an alphabet set consisting of {1, 3, 7, 11} can be used to represent the coefficients {103 &139} as shown in Table 1. This reduces the entire multiplication operation of the filter to shift/select and add operations using Select/Shift and Adder (SSA) units instead of the power intensive multipliers. Figure 6 shows the SSA unit utilized in implementation of the seizure detection system [10].Using the method in [10] we reduce the number of alphabets required to represent the filter coefficients {1, 3} with negligible degradation in filter response. The pre-computer computes the product of alphabets and input vector X in advance and stores them for reuse. The shifter inside the SSA selects the appropriate precomputed product depending upon the bit-pattern of the coefficient alphabet. The pre-computed product is then shifted according to the decomposition of coefficient (Table 1). Finally, these shifted pre-computed values are added to generate the SSA output (C*X) [10]. The FIR filter can be implemented by using adequate number of alphabets and replacing the multiplier units with the SSA units. Apart from CSHM, several other methods have been developed to identify and eliminate common subexpressions within the filter coefficients [11]. In this paper, we use a level-constrained CSE algorithm [11], which can constrain the number of adders along with the number of adder levels (AL) required to compute each of the coefficient outputs. This reduction in number of AL translates to lower complexity and architecture, thus, lower power. Table 1. Coefficient Representation using Pre-computation Coefficients Decomposition c 0= (103) c 1= (139) c 0 x = 2 5 (0011) x + (0111) x c 1 x = 2 7 (0001) x + (1011) x 304

5 X A i X (1X) A i X (10X(<<1)+X) Bank of Precomputers C<0:3> C<4:7> MUX (4:1) index Figure 6. Implementation of Select/Shift Add block (SSA) Using the above mentioned multiplier-less techniques, the DWT circuit can be implemented for the Wavelet Decomposition block. In this application, a total of 6 Low pass filters (H) and 3 High pass filters (G) are used. The filter coefficients are digitized and approximated to binary powers. This minimizes the number of 1 s, and the number of pre-computed products needed. This reduces the power and area consumption of the implemented hardware. In accordance with Mallat s algorithm, the down sampling operation between every successive stage of the DWT is performed using a ripple counter which acts as a clock divider. The adjacent output bits of a counter are used to clock the successive filter stages in the DWT block. The DWT coefficients along with clock signals are propagated to the next stage. (ii)quasi Averaging Block The Quasi Averaging (QA) block (Figure 4) consists of an absolute value component followed by quasi averaging units for each of the selected DWT coefficients. The DWT coefficients are in the 2 s complement form. Since the input is a non stationary random signal, calculating a moving window average on its component frequency will yield zero value. To overcome this, the coefficients are converted into their absolute magnitudes for further analysis. Next, the unsigned coefficients obtained are provided to the QA blocks to calculate the quasi average over a continuously moving window. A block diagram showing the principle of quasi averaging is shown in Figure 7. The function of a continuously moving-window quasi-averaging operator is explained as follows. If S i:i+w is the sum of elements x i in the window W k of window size w, then the average of W k is given by 1 < W >= S (1) k i: i w w + Zero MUX ISHIFTER (2:1) SHIFTER select Select/Shift Final Adder Select/Shift & Adder (SSA) The average of the next window W k+1 can be calculated as 1 < Wk + 1 >= ( Si : i+ w - xi + xi + w+ 1 ) (2) w However, this would require storing the first w elements for subsequent usage. The QA technique approximates this calculation by assuming that the average of a window is the true representation of all the elements contained in it. The equation (2) can then be written as 1 < Wk + 1 >= ( Si : i+ w- < Wk > + xi+ w+ 1 ) (3) w This helps implement the moving window in a much simpler circuit and more importantly, without the usage of memory. This technique greatly facilitates implementation to achieve real time operation, for the targeted application. Three such QA modules with window size 512, 256 and 128 for D4, D5 and D6 respectively are used. This corresponds to a window of 4096 in the raw data sequence. The selected window sizes simplify the shift Select/Shift X*C Input (x i ) Subtractor (-) Accumulator (S ) Divider ( N) Figure 7. Block Diagram for the Quasi Averaging Technique hardware implementation by avoiding complex divider blocks. The windowed quasi average of each detail coefficients <D4>, <D5>, <D6> is then weighted with weights of 2, 1, and 0.5 respectively. The weighted sum of the quasi averages forms the onset detection signal, which is used in the next stage to generate the seizure detection flag. (iii)thresholding Block In the Thresholding Block, a simple comparator compares the onset detection signal to the prefixed threshold value and generates the detect flag at the onset of the seizure. Note that the frequency of operation of the entire circuit is the same as the sampling frequency of the LFP recording. Since this frequency is very low, V DD scaling techniques can be utilized to further reduce power dissipation due to quadratic dependence of power on V DD. The obtained results from simulations are discussed in the next section. 4. RESULTS AND DISCUSSION The wavelet transform and quasi average based epileptic seizure detection system was designed and simulated using 90nm (IBM) bulk-si library. The neural data was obtained using an implantable LFP neural recording system from live animals (Figure 8) [1]. The operation of the algorithm was verified by comparing the results from software simulation with hardware simulation. The system was evaluated on two aspects viz. Algorithmic Efficacy and Hardware Efficiency. (i)algorithmic Efficacy Algorithmic Efficacy provides an insight to the correctness of the detection. It is based on three parameters Sensitivity, Specificity and Average Detection Rate (ADR). To obtain these parameters, the input data was recorded from live animals (rats) treated with Kainic acid to induce seizures [1]. This data was then used along with the hardware implementation of the algorithm for a real time system simulation using ModelSim. The seizures in the data were identified and time stamped by electrographic evidence and visual confirmation. Based on this time stamped data, the detection was classified as a True Positive (TP), False Positive (FP), and True Negative (TN) and False Negative (FN). The most important among these classified detections are the FP and FN. It is essential to minimize both these parameters. The specificity, sensitivity and the ADR were calculated as per following [7]. TP Sensitivity = 100% TP + FN TN Specificity = 100% TN + FP Sensitivity + Specificity ADR = 100% 2 Quasi Moving Average The process was repeated over neural recordings obtained from five different animals. The corresponding results are tabulated in Table 2. It should be noted that specificity and sensitivity are mutually related parameters. If during the training phase, the threshold chosen is much lower, it would lead to a higher value of FP. This would also mean that the system is very sensitive to any seizure-like events in the recorded data. A higher threshold would (4) (5) (6) 305

6 Figure 8. LFP Neural recording system implanted in a rat result in reduced FP. However, this would lead to high probability of missing a seizure resulting in increased FN. Since specificity and sensitivity are complementary to each other; the ADR is a good measure to calculate the overall efficacy of the system. As can be seen from Table 2, the algorithm achieves an average detection rate ranging from 91% to 99%. The low value of ADR for Subject-3 is because of the fact that a seizure was missed from detection. A lower threshold would result in a higher sensitivity but also cause a degradation of specificity and ADR. (ii)hardware Efficiency As mentioned before, apart from the algorithm efficacy, it is also important to see the feasibility of the algorithm. The hardware implementation of the algorithm using the architectures discussed was simulated using IBM 90nm bulk-si library. The functionality was checked using Modelsim and Synopsys Design Compiler was used to synthesize the system. The synthesized Verilog netlist was converted to Hspice netlist. Nanosim was used to simulate the system at transistor level to obtain the power results. The power dissipation for the various architectures at nominal and scaled V DD is tabulated in Table 3. Note that the system was designed for a maximum clock speed of 1 MHz However, due to requirement of low frequency of operation (1.5 KHz), V DD can be scaled down and power is further reduced due to quadratic dependence. As seen from Table 3, the use of multiplier- less architectures along with V DD scaling results in reduction in power consumption by over 80 %. It can also be observed that there is almost a 90% improvement in power consumption as compared to the ANN and wavelet based seizure detection system. This is because of the fact that the ANN based system uses a large number of ADCs in its wavelet processor [8]. We also observe a significant reduction in area of the entire system due to the elimination of the multipliers from the FIR filter and sharing or elimination of common sub-expressions in CSHM/CSE [10]. An FFT based system(based on EEG) is also shown to consume significantly more power owing to increased computation 1V, 180nm) [12]. Both the STFT and the ANN based algorithm would also have to be made user scalable due to the inter-subject variability to maintain a good efficacy at the given power. As compared to them, the proposed algorithm and its implementation (in 90nm) is better in performance and lower in power consumption making it a more suitable candidate for epilepsy prosthesis. Table 2. Algorithm Efficacy for 5 Different Subjects Data Source Sensitivity Specificity ADR Subject % % % Subject % % % Subject % % % Subject % % % Subject % % % Table 3. Power Dissipation and Area Improvement Power (mw) Architecture Area V DD=1.0 V V DD=0.5V CSE (multiplier-less) X CSHM (multiplier-less) X CSA multiplier X ANN based wavelet[6] 1V 6X 5. CONCLUSION We have developed a novel low-power epileptic seizure detection system based on wavelet transform and quasi-averaging operation, tunable to user specific needs, by utilizing power efficient design techniques at various levels of abstraction. At the algorithmic level user-specific critical parameters (DWT coefficients, weights and threshold) were identified to accurately detect the onset of seizure. These parameters were explicitly used to develop a highly accurate low complexity algorithm. Further, this algorithm was mapped to a low power hardware implementation. Multiplier-less techniques were utilized at architectural level to reduce the power consuming logic elements. The resulting epileptic seizure detection system showed high efficacy, when tested on data from in-vivo animal recordings, while achieving low power operation. The proposed system can be used to implement battery-powered implantable epilepsy prosthesis, achieving both user scalability and area, power efficiency. 6. REFERENCES [1] S. Raghunathan et al., The design and hardware implementation of a low-power real-time seizure detection Algorithm, J. Neural Engineering, 2009, Vol 6, pp [2] D. K. Binder et al., Recent advances in epilepsy research, Ch 17, Kluwer Academic/Plenum, [3] Stéphane Mallat, A wavelet tour of signal processing, Academic Press, [4] M.K. Kiymik et al. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application, Computers in Biology and Medicine, 2005 Vol 35, Issue 7, pp [5] I. Osorio et al. 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