IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 4, APRIL

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1 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 4, APRIL Detection of Pseudosinusoidal Epileptic Seizure Segments in the Neonatal EEG by Cascading a Rule-Based Algorithm With a Neural Network Nicolaos B. Karayiannis, Senior Member, IEEE, Amit Mukherjee, John R. Glover, Senior Member, IEEE, Periklis Y. Ktonas, Senior Member, IEEE, James D. Frost, Jr., Richard A. Hrachovy, and Eli M. Mizrahi Abstract This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rulebased algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development. Index Terms Electroencephalography, epileptic seizure segment, feedforward neural network (FFNN), neonatal seizure, quantum neural network (QNN). I. INTRODUCTION SEIZURES are frequently the initial sign of neurological disease in the neonatal period (i.e., during the first four weeks of life) [5], [12], [14], [16], [17], [19], [27], and their detection is typically made on the basis of clinical (behavioral) signs, which may be associated with electroencephalographic (EEG) correlates. Seizures are manifested in the EEG as paroxysmal events characterized by stereotyped repetitive waveforms that evolve in amplitude and frequency before eventually decaying. Such discharges can be detected by considering the rhythmicity and repetitiveness of the EEG patterns. However, the clinical manifestations of neonatal seizures are often subtle, Manuscript received November 19, 2004; revised July 17, This work was supported in part by the National Institute of Neurological Disorders and Stroke under Grant 1 R01 NS and Contract N01-NS Asterisk indicates corresponding author. *N. B. Karayiannis is with the Department of Electrical and Computer Engineering, N308 Engineering Building 1, University of Houston, Houston, TX USA ( karayiannis@uh.edu). A. Mukherjee, J. R. Glover, and P. Y. Ktonas are with the Department of Electrical and Computer Engineering, University of Houston, Houston, TX USA. J. D. Frost, Jr. and E. M. Mizrahi are with the Peter Kellaway Section of Neurophysiology, Department of Neurology, Baylor College of Medicine, Houston, TX USA. R. A. Hrachovy is with the Peter Kellaway Section of Neurophysiology, Department of Neurology, Baylor College of Medicine, Houston, TX USA and also with the Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX USA. Digital Object Identifier /TBME which may prevent early detection and may result in significant delay in the institution of appropriate therapy. The identification of electrographic seizures from neonatal EEG is currently based on an electroencephalographer s interpretation of the graphical record. Since this process is both time-consuming and subjective, reliable automated detection of electrical seizure activity in the neonate would have significant clinical application. This has motivated a number of approaches aimed at the automated detection of seizures from neonatal EEG recordings [3], [4], [6] [10], [13], [15], [20], [25], [26]. The development of a fully automated seizure detection system may prove to be a task involving insurmountable challenges, mainly because of possible intersubject differences as well as presence of artifacts that may lead to false positive detections. Nevertheless, even a semi-automated system with high sensitivity and specificity would be a useful aid to physicians. The morphology of EEG patterns associated with neonatal seizures is varied. Our group is currently developing three detection schemes that address three different EEG morphologies of seizure patterns, namely the Type A detector for pseudosinusoidal seizure segments, the Type B detector for segments with complex morphology (high frequency activities superimposed on slow-waves), and the Type C detector for rhythmic runs of spike-like waves. Fig. 1 shows representative examples of Type A, Type B, and Type C seizure activity. The study presented in this paper focuses on Type A seizure segments only. Our ongoing research indicated that these three morphologies, although overlapping at times, cover most of the EEG seizure patterns in neonates. Automated detection of complete multichannel epileptic seizures in the neonatal EEG will be attempted by a three-stage process explained in [6], which is briefly described as follows. Stage 1) The purpose of Stage 1 is the detection of candidate EEG seizure segments involving rhythmic/repetitive/periodic waveforms. At this stage, for detection purposes only, we recognize EEG segments that resemble seizure patterns of the aforementioned types (i.e., Type A, Type B, and Type C). The schemes developed to detect EEG segments of these three types are applied simultaneously in parallel to each of the EEG channels. The resulting combined output will be a mix of candidate seizure segments of Types A, B, and C that will be appropriately tagged with their attributes. These segments must then undergo further processing in Stages 2 and /$ IEEE

2 634 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 4, APRIL 2006 algorithm was mainly influenced by Gotman s approach [7], [8]. Gotman s approach, which uses spectral features to detect pseudosinusoidal activity and another algorithm to detect repetitive spikes, is similar to ours. The first part of this paper presents the performance of conventional feedforward neural networks (FFNNs) and quantum neural networks (QNNs), which were trained by examples to detect pseudosinusoidal EEG seizure segments identified by a Type A rule-based algorithm in neonatal single-channel EEG data. The second part of the paper presents the performance evaluation of a detection scheme developed by cascading a Type A rule-based algorithm with a supervised feedforward neural network model (i.e., FFNN or QNN) to detect pseudosinusoidal EEG seizure segments in neonatal single-channel EEG data. Fig. 1. (a) Example of Type A seizure activity: low amplitude depressed brain type discharge, which is a pseudosinusoidal signal in channels C3-O1 and Fp1-T3, and also in the initial few seconds of channel C4-O2. (b) Example of Type B seizure activity: repetitive complex slow waves with superimposed higher frequencies in all the channels. (c) Example of Type C seizure activity: repetitive or periodic runs of sharp transients in channels Fp2-C4 and C4-O2. Stage 2) The purpose of Stage 2 is to remove those candidate segments from the output of Stage 1 that are determined to be artifacts or other nonepileptic EEG activity. Heuristic rules will identify those segments as nonseizure types, based on context information obtained mostly from non-eeg channels. Examples of artifacts for which there is ample context support are patting, sucking, respiratory, and EKG artifacts. Stage 3) In Stage 3, a clustering algorithm augmented by rules will create the final detected seizures out of the candidate seizure segments surviving Stage 2. In this process, isolated and inconsistent candidate seizure segments will be eliminated, and the final detected seizures will emerge. The proposed Type A detection scheme consists of a rulebased algorithm cascaded with a trained neural network model (i.e., a trained FFNN or a trained QNN). The rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is fed to feedforward neural network models (i.e., FFNN or QNN) trained to detect pseudosinusoidal EEG seizure segments. Among several published algorithms that would be appropriate for detecting pseudosinusoidal seizure segments [3], [7], [8], [15], our Type A rule-based II. COMPUTATIONAL METHODS AND TOOLS Seizure patterns in neonates are widely varied [12], [14]. Thus, the design of a classifier capable of assigning all seizures to the same class would require a large number of features. Such a strategy would also result in a highly complex classification problem that might be difficult to deal with in practice. These difficulties may be overcome by a priori classifying epileptic EEG segments into three types of activity as described in Section I. The rough definition of these three types of EEG activity was based on our experience with the available EEG data and the domain knowledge provided by a team of physicians, who also marked the onset and the end of seizures on the EEG recordings. However, due to the highly complex morphology of the data, it was not possible, even for the physicians, to establish a clear separation among segments representing Type A, B, or C activity. The Type A detection algorithm was designed to separate pseudosinusoidal seizure segments from background EEG activity. The term pseudosinusoidal is used to define those segments in which most of the spectral power is concentrated in one or two peaks of the power spectrum. The background class consists of those segments of EEG that lay outside the seizure epoch scored by the team of physicians. However, the detection of Type A segments was complicated by the lack of well-defined class boundaries for the three types of seizure segments. The absence of a distinct set of Type A segments was dealt with by developing a rule-based algorithm [26], which played a critical role in the formation of the training set utilized to train the neural networks in this paper. The Type A detection algorithm was not expected to detect seizure segments that were not pseudosinusoidal. Nevertheless, such segments would most likely be detected by the Type B and Type C detection algorithms. We relied on the rule-based algorithm instead of a linear discriminant function for two reasons: 1) The realization of a linear discriminant function requires the existence of predefined classes. Such classes were not available for our project (see Section I). 2) The rule-based algorithm allowed us to develop a classifier that incorporates domain knowledge of the physicians. Such knowledge is quite difficult to represent in terms of a linear discriminant function. Although the rule-based algorithm incorporated domain knowledge and experience, the empirical thresholds employed could only create linear partitions of the spectral feature space. This limited the discrimination ability of the Type A rule-based

3 KARAYIANNIS et al.: DETECTION OF PseudoSINUSOIDAL EPILEPTIC SEIZURE SEGMENTS IN THE NEONATAL EEG 635 algorithm, which was suppressed even further by the variety and complexity of seizure patterns. As a result, the Type A rule-based algorithm misclassified some background EEG segments and artifacts as true seizure segments because of similarity in their morphology. Therefore, we expected that cascading the rule-based algorithm with a neural network trained to detect Type A segments would contribute to a significant improvement of the Type A segment detection process. The rest of the epileptic seizure segments would most likely be detected by the schemes currently under development for detecting Type B and Type C segments. When available, these schemes would operate in parallel with the scheme proposed in this paper for the detection of Type A segments. In addition to the schemes developed to detect Type A, B, and C segments, the seizure detection system under development will also involve a rule-based clustering algorithm to select the final detected seizures out of the candidate seizure segments following the elimination of artifacts. Training neural networks to function as classifiers requires a set of examples that constitute a fair and balanced representation of the classes involved. Forming the training set directly from raw EEG data proved to be a challenging task due to the significant imbalance between seizure and nonseizure segments. In this study, the rule-based algorithm produced two balanced classes used for training feedforward neural network models (i.e., FFNNs and QNNs). During final operation, the complete Type A detection scheme would consist of the rule-based algorithm cascaded with a trained neural network model (i.e., a trained FFNN or a trained QNN). Fig. 2 depicts a block diagram of such a scheme. The remainder of this section presents a brief review of the computational methods and tools employed in this study. A. Type A Rule-Based Algorithm The rule-based algorithm developed in this project [26] is similar to that proposed by Gotman et al. [7], [8], who described a rule-based approach for pseudosinusoidal activity and another approach for spike activity. The following assumptions were made regarding Type A seizure activity: 1) the segment of EEG is at least 5 s in duration, and 2) the frequency range of the epileptiform discharge is Hz. The algorithm extracts various spectral features from each EEG segment and compares them with thresholds. A segment is identified as Type A seizure activity if it meets all the threshold requirements. The following is a list of features that the Type A algorithm uses in order to parametrize each 5-s EEG segment. First dominant frequency: the frequency corresponding to the tallest peak in the spectrum. Second dominant frequency: the frequency corresponding to the second tallest peak in the spectrum. Widths of dominant frequencies: the difference between the frequency corresponding to half of the peak amplitude of the dominant peak in its falling slope and the frequency corresponding to half of this amplitude in the rising slope. Percentage of power contributed by first dominant frequency: ratio of the power in the first dominant frequency within its width to the total power in the spectrum. Percentage of power contributed by the first two dominant frequencies: ratio of the power in the first two dominant frequencies within their widths to the total power in the spectrum. Fig. 2. Detection of seizure segments by (a) the Type A rule-based algorithm and (b) the Type A rule-based algorithm cascaded with a trained neural network. Q1: segments qualified as seizure activity by the Type A rule-based algorithm. R1: segments rejected by the Type A rule-based algorithm (consisting of the rest of nonseizure EEG data and some Type B and Type C seizure segments). Q2: segments qualified as seizure activity by both Type A rule-based algorithm and neural network (FFNN or QNN). R2: segments rejected by the neural network (consisting mainly of the false detections of the Type A rule-based algorithm). Peak ratio: the ratio of peak values corresponding to the first and second dominant frequencies. Stability ratio: a time-domain parameter that measures the amplitude stability of the EEG segment. The thresholds employed by the Type A rule-based algorithm could be altered to change the number of true detections (i.e., segments that lie between the onset and the end of seizures marked on EEG recordings by physicians) and false detections (i.e., segments that lie outside the region marked by the physicians as seizure activity). In this study, some of the thresholds were relaxed from their original values, including those for the percentage of power contributed by the first dominant frequency, the percentage of power contributed by the combination of the first and second dominant frequencies, and the ratio of the first dominant peak to the second dominant peak. Relaxing the thresholds is expected to increase the number of detected true seizure segments. However, such a strategy is also expected to produce an increased number of false positives, i.e., detection of artifacts and nonepileptic rhythmic EEG segments. The goal of the cascaded detection scheme is to increase the number of true detections while decreasing the number of false detections. Thus, relaxing the thresholds of the rule-based algorithm appears to be a promising strategy if its outputs are to be fed to another classifier (i.e., neural network) that may reject a good proportion of the false detections of the rule-based algorithm. This proposition can be schematically described with the help of set diagrams in Fig. 3. In this figure, the universal set (Region 5) consists of the entire EEG data, while the large rectangle (Region 4) represents seizure segments as indicated by the team of physicians. The circles enclosing Regions 1, 2, and 3 indicate hypothetical classes containing Type A, Type B, and Type C

4 636 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 4, APRIL 2006 Fig. 3. Set diagram representation of the EEG data indicating different seizure classes. Regions 1, 2, and 3 represent the hypothetical classes of Type A, Type B, and Type C seizure activity, respectively. Region 4 represents the total seizure class and Region 5 is the universal set containing the entire neonatal EEG data. The rectangle (a) represents the region covered by the Type A rule-based algorithm. The rectangle (b) represents the region covered by the Type A rule-based algorithm tested with relaxed thresholds. seizure segments, respectively. Note that these classes are overlapping and their boundaries are indicated by dotted lines as they are not well-defined. Assume that the small rectangle (a) in Fig. 3 represents the area covered by the rule-based algorithm, which includes the class of Type A segments, some portion of Type B, Type C and perhaps other seizure segments, and false detections of the rule-based detection algorithm (indicated in Fig. 3 by the portion of the rectangle (a) outside Region 4). Since Type A seizure activity is hypothetical and has no well-defined class boundaries, it was assumed that it is represented almost entirely by the true detections of the Type A rule-based algorithm. In order to ensure this, the thresholds of the Type A rule-based algorithm were set in such a way that the algorithm detects the overwhelming majority of pseudosinusoidal seizure segments that may be classified as Type A activity. Inevitably, this choice led to the detection of some other seizure segments and to the detection of nonseizure segments. The rectangle (b) in Fig. 3 may represent the area covered by the rule-based algorithm when the thresholds are relaxed. The neural network is trained to classify the data lying within that rectangle into two classes, namely those lying in Region 4, representing seizure segments, and those lying outside Region 4, representing nonseizure segments. B. Quantum Neural Networks Conventional feed-forward neural networks (FFNNs) contain one or more layers of sigmoid hidden units connected by adjustable synaptic weights [2], [11]. A recent theoretical study indicated that the graded responses of FFNNs with sigmoid output units (i.e., output values ranging from 0 to 1) do not necessarily represent true membership values, that is degree of belonging consistent with the distribution of the data on the feature space [24]. This study also proved that FFNNs are incapable of learning the inherent structure in the data. The inability of conventional FFNNs to identify and quantify uncertainty in data motivated the development of neuro-fuzzy models [1], [21]. Such models include a family of inherently fuzzy feedforward neural networks, known as quantum neural networks (QNNs) [22], [23]. QNNs are decision-making and inferencing tools, which are capable of obtaining an approximate classification for uncertain data without any restricting assumptions, such as the availability of a priori information in the form of a desired membership profile, limited number of classes of data, convexity of the classes, etc. QNNs are designed to achieve this goal through multilevel partitioning of the feature space. The capacity of QNNs for autonomously forming multilevel partitions of the feature space arises from their ability to create graded internal representations of the sample information provided by the training data. The sample information is encoded into graded internal representations by choosing multilevel activation functions for the hidden units, instead of the conventional sigmoid activation functions. If all the activation functions of the hidden units have the ability to form graded partitions, then these partitions can be collapsed-in or spread-out as required, using a suitable learning algorithm. Consider a QNN consisting of inputs, one layer of multilevel hidden units, and output units. The output units can be linear or sigmoid. Let be the synaptic weight connecting the th output unit to the th hidden unit. Let the synaptic weight connecting the th hidden unit to the th input be. Suppose the data set contains the feature vectors,. Then the input to the th hidden unit from is, with. If a multilevel hidden unit has discrete quantum levels, the response of the th multilevel hidden unit to can be written as a superposition of sigmoid functions, i.e., where is a sigmoid function, such as the logistic function used in this work, is a slope factor, and define the jump-positions in the activation function. The values of determine the step widths of the multilevel activation function, called the quantum intervals. If and,, then,, and the QNN reduces to a conventional FFNN. Fig. 4 shows a typical multilevel activation function formed as the superposition of five logistic functions with jump-positions at 9, 5, 1, 1, 9, and a slope factor each. The response of the th output unit to can be written as, where, with, if the unit is sigmoid and if the unit is linear. The synaptic weights of the QNN can be updated by a gradient-descent-based algorithm derived by minimizing a suitable function of the error between the expected outputs and the actual responses of the QNN [22]. The quantum intervals can be estimated by minimizing the class-conditional variances at the outputs of the hidden units [22], [23]. III. EVALUATION OF FFNNS AND QNNS This study relied on EEG recordings selected from a database developed by the Clinical Research Center for Neonatal Seizures (CRCNS) in Houston, TX, established by the National Institute of Neurological Disorders and Stroke [18]. The overall (1)

5 KARAYIANNIS et al.: DETECTION OF PseudoSINUSOIDAL EPILEPTIC SEIZURE SEGMENTS IN THE NEONATAL EEG 637 TABLE I SENSITIVITY AND SPECIFICITY OF THE TRAINED FFNN AND QNN. THE TRAINING AND TESTING SETS WERE FORMED FROM THE OUTPUTS OF THE RULE-BASED ALGORITHM. THE RESULTS ARE BASED ON A CUT-OFF THRESHOLD SET AT 0.5 FOR BOTH FFNN AND QNN Fig. 4. Typical example of a multilevel activation function with n =5 jump-positions at 09, 05, 01, 1, and 9. goal for this initiative was to develop a comprehensive understanding of the clinical and EEG features, predisposing risk factors, etiology and outcome of seizures in the newborn. As part of this work, bedside EEG/video/polygraphic monitoring was performed (minimum of two hours for initial study), followed by repeat one-hour studies 3 5 days after the initial seizure characterization, and at the time of discharge. This study utilized EEG recordings consisting of 12 channels of bipolar montages from 15 neonates exhibiting seizure activity. Each output of the Type A rule-based algorithm was labeled as true detection or false detection based on the scoring provided by a team of physicians. An output was labeled as true if it occurred between the starting and ending points of a seizure in that EEG channel. For this purpose, no attempt was made by the physicians to distinguish Type A from Type B or Type C segments; indeed the same segment could occasionally be labeled as seizure by either the Type A, Type B or Type C automated detection schemes (see Introduction). An output was labeled as false if it occurred outside the boundaries of the seizure as marked by the physicians. The candidate seizure segments identified by the Type A rulebased algorithm were fed to an FFNN or a QNN, which was trained by examples to distinguish between true and false Type A seizure segments (see Fig. 2). The QNN and FFNN contained a single hidden layer with five units and a single output unit with activation function and. Both models were trained to respond with 1 to input segments from the seizure class and with 0 to input segments from the nonseizure class. The number of hidden units was chosen in these experiments by trial-and-error in an attempt to ensure that the FFNN and QNN can implement the mapping defined by the training set without compromising their generalization ability. The FFNN was composed of hidden units with activation function and. The QNN was composed of multilevel hidden units with levels and. The input features contained the following spectral domain parameters: the frequencies corresponding to the three tallest peaks in the power spectrum, the spectral width corresponding to the peaks, and the percentage power contributed by them in the total power spectrum. In addition, the input features included the total power in the given segment and the power in four consecutive quarters of the segment. Both neural networks were trained by gradient descent, with identical initial weights, learning rates, and stopping criteria. The training and testing sets were constructed by randomly selecting an equal number of seizure and nonseizure segments identified by the Type A rule-based algorithm from EEG recordings of multiple subjects. The training set contained 1401 segments from the output of the Type A rule-based algorithm (689 belonging to the seizure class and 712 belonging to the nonseizure class). The testing set contained 1401 segments from the output of the rule-based algorithm (712 belonging to the seizure class and 689 belonging to the nonseizure class). Table I shows the sensitivity and specificity of the trained FFNN and QNN in the training and testing sets, respectively. An input segment was classified as a seizure (nonseizure) segment if the response of the output unit was above (below) 0.5. Table I reveals no substantial differences between the trained QNN and FFNN in this set of experiments. This may be attributed to the fact that the scheme employed for classifying the input segments is rather crude since it makes no distinction between input segments that produce responses in the neighborhood of 0.5 and those producing responses close to 0 or 1. This is illustrated by the outcome of the following set of experiments, which focused on the distribution of the responses of the trained FFNN and QNN over the interval [0,1]. Fig. 5 shows a histogram of the responses of the QNN and FFNN when presented with the same input EEG segments from the seizure and nonseizure classes used above for their training and testing. The outputs of the QNN and FFNN were confined in the interval [0,1], which was divided into 10 equal subintervals of length 0.1 each. Fig. 5 shows the percentage of input segments that produced a response falling in each of the 10 subintervals. According to Fig. 5, 84.01% of the responses produced by the QNN to input segments from the seizure class fell within the interval [0.5,1]. Only 15.99% of input segments from the seizure class produced QNN responses below 0.5, which would lead to classification errors. The FFNN also produced 15.56% of responses below 0.5 when presented with input segments from the seizure class. However, Fig. 5 reveals a major difference in the way the trained QNN and FFNN responded to input segments from the seizure class. More specifically, 47% of input segments from the seizure class produced responses of the FFNN in the interval [0.9,1]. In contrast, about 23% of the responses of the QNN to the same input segments were distributed in the same interval, whereas 31% of these responses were concentrated in the interval [0.8,0.9]. In contrast, only 15% of the responses of the FFNN were in the interval [0.8,0.9]. Similar inferences can be made about the responses of the trained QNN and FFNN to

6 638 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 4, APRIL 2006 TABLE II NUMBER OF TRUE DETECTIONS AND FALSE DETECTIONS PRODUCED FOR THE EEG RECORDINGS OF 15 PATIENTS BY THE TYPE ARULE-BASED ALGORITHM, THE RULE-BASED ALGORITHM CASCADED WITH A TRAINED FFNN, THE RULE-BASED ALGORITHM CASCADED WITH A TRAINED QNN, THE TYPE A RULE-BASED ALGORITHM TESTED WITH RELAXED THRESHOLDS, THE RULE-BASED ALGORITHM WITH RELAXED THRESHOLDS CASCADED WITH A TRAINED FFNN, AND THE RULE-BASED ALGORITHM WITH RELAXED THRESHOLDS CASCADED WITH A TRAINED QNN Fig. 5. Histograms of the responses of (a) the QNN to input segments from the seizure class, (b) the QNN to input segments from the nonseizure class, (c) the FFNN to input segments from the seizure class, and (d) the FFNN to input segments from the nonseizure class. input segments from the nonseizure class. According to Fig. 5, the responses of the QNN to input segments from the nonseizure class were more evenly distributed in the interval [0,0.5] than those of the FFNN. Only a small percentage of responses were above 0.5. However, there was an unexpected and unjustifiable increase in the percentage of input segments that produced responses in the interval [0.9,1]. Although this anomaly was observed in the responses of both trained models, the QNN produced fewer responses in the interval [0.9,1] than the FFNN. The results shown in Fig. 5 provided the motivation for another study, which confirmed that the responses of trained QNNs are more reliable indicators of uncertainty in the input data compared with the responses of trained FFNNs [13]. IV. EVALUATION OF THE PROPOSED SCHEME FOR THE DETECTION OF PSEUDOSINUSOIDAL SEIZURE SEGMENTS This section presents the application of the proposed scheme to the detection of pseudosinusoidal seizure segments. This set of experiments tested the performance of the detection scheme shown in Fig. 2(b). The detections identified by the Type A rule-based algorithm were fed to an FFNN or a QNN, which was trained by examples to distinguish between true and false Type A seizure segments. The evaluation of the Type A detection scheme relied on EEG data acquired from 15 patients. Each EEG recording was at least two hours long. Table II shows the number of true detections and false detections obtained when the detection of seizure segments relied on two versions of the Type A rule-based algorithm. According to Table II, cascading the Type A rule-based algorithm with the trained FFNN reduced the number of false detections by 83.3% while reducing the number of true detections by 20.4%. Cascading the rule-based algorithm with the trained QNN reduced the number of false (true) detections by 81.9% (19.9%). The substantial reduction of the number of false detections is by itself an interesting result. Another interesting result is that the number of true detections decreased by about 20% when the rule-based algorithm was cascaded with either the trained FFNN or the trained QNN. This indicated that the overall performance of the seizure detection scheme under development could be improved by increasing the number of candidate seizure segments presented to the trained FFNN or the trained QNN. This was accomplished in this study by employing the Type A rule-based algorithm with relaxed thresholds. Table II shows the number of true and false detections produced by the rule-based algorithm when it was tested with relaxed thresholds. The fifth row of Table II shows the number of true and false detections produced when the rule-based algorithm was tested with relaxed thresholds and its outputs were fed to the trained FFNN. The same results are shown in the last row of Table II for the rule-based algorithm tested with relaxed thresholds and cascaded with the trained QNN. Comparison of the first and fifth rows of Table II indicates that the detection scheme incorporating the rule-based algorithm with relaxed thresholds and the trained FFNN increased the true detections by 26.8%. The same scheme decreased the number of false detections from 4423 to 1043 (a decrease of 76.4%). Testing the rule-based algorithm with relaxed thresholds and feeding its outputs to the trained QNN increased the true detections by 27.8%. In this case, the number of false detections decreased by 74.5%. This indicated that the neural network was successful in creating finer decision boundaries compared to the Type A rule-based algorithm. In addition, the cascaded scheme exhibited superior performance in the classification of Type A segments. V. CONCLUSION This paper presented some of the results of a project aimed at the development of a seizure detection system, which is currently under way. More specifically, this paper described the application of a scheme developed by cascading a rule-based algorithm with a neural network in the detection of epileptic seizure segments from neonatal EEG. This was accomplished by training FFNN and QNN models to classify the outputs of

7 KARAYIANNIS et al.: DETECTION OF PseudoSINUSOIDAL EPILEPTIC SEIZURE SEGMENTS IN THE NEONATAL EEG 639 an algorithm developed to identify short segments of pseudosinusoidal EEG patterns based on features in the power spectrum. The first part of this study compared the performance of the FFNN and QNN in detecting pseudosinusoidal EEG segments obtained from the Type A rule-based algorithm. The results indicated that there is no significant difference in the performance of the detection schemes based on the FFNN or the QNN if the classification of each input EEG segment is based on a binary decision, that is, if the cut-off threshold is set exactly in the middle of the interval [0,1] (see Table I). The reason is that such a classification strategy fails to exploit the inherent ability of the QNN to generate graded transitions between classes when trained with the same sample data used for training the FFNN. The differences in the output profiles of the FFNN and QNN were revealed by the histograms shown in Fig. 5. The second part of this study compared the results of the Type A rule-based algorithm with the results obtained by cascading this rule-based algorithm with a trained feedforward neural network model (i.e., FFNN or QNN). Cascading the rule-based algorithm with a feedforward neural network model (either FFNN or QNN) decreased considerably the number of false detections (see Table II). However, the cascaded scheme missed some true pseudosinusoidal seizure segments that were selected as candidate seizure segments by the rule-based algorithm. This problem was dealt with by relaxing the thresholds used in the implementation of the rule-based algorithm. Our results indicated that the Type A rule-based algorithm produces a higher number of candidate pseudosinusoidal seizure segments when tested with relaxed thresholds. However, the candidate seizure segments selected by such an implementation of the rule-based algorithm contained a higher number of false detections. A good proportion of these false detections were rejected by the feedforward neural network model (i.e., the FFNN or QNN) cascaded with the Type A rule-based algorithm. Moreover, the feedforward neural network model detected more true seizure segments when the rule-based algorithm was tested with relaxed thresholds. In addition to relaxing the thresholds in the next stage of system development, we also plan to incorporate a regularizer in the error function minimized during the training of the neural networks in order to improve their generalization ability. Among the regularizers that could be incorporated in the error function, a curvature-driven regularizer seems to be the most promising for this application since it directly yields network mappings with lower network variance [2]. REFERENCES [1] J. C. Bezdek and S. K. Pal, Eds., Fuzzy Models for Pattern Recognition: Models That Search for Structures in Data. Piscataway, NJ: IEEE Press, [2] C. M. Bishop, Neural Networks for Pattern Recognition. New York: Oxford Univ. Press, [3] P. Celka and P. Colditz, A computer-aided detection of EEG seizures in infants: a singular spectrum approach and performance comparison, IEEE Trans. Biomed. Eng., vol. 49, no. 5, pp , May [4], Nonlinear nonstationary Wiener model of infant EEG seizures, IEEE Trans. Biomed. Eng., vol. 49, no. 6, pp , Jun [5] G. M. Fenichel, Neonatal Neurology, 3rd ed. New York: Churchill- Livingstone, [6] J. R. Glover, P. Y. Ktonas, M. Shastry, A. Thitai-Kumar, V. M. Muktevi, J. D. Frost Jr., R. A. Hrachovy, and E. M. Mizrahi, Methodology and system architecture for automated detection of epileptic seizures in the neonatal EEG, in Proc. 24th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, Houston, TX, Oct , 2002, pp [7] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, Automated seizure detection in the newborn: Methods and initial evaluation, Electroencephalogr. Clin. Neurophysiol., vol. 103, pp , [8] J. Gotman, D. Flanagan, B. Rosenblatt, A. Bye, and E. Mizrahi, Evaluation of an automatic seizure detection method for the newborn EEG, Electroencephalogr. Clin. Neurophysiol., vol. 103, pp , [9] J. Gotman, Automatic detection of seizures and spikes, J. Clin. Neurophysiol., vol. 16, pp , [10] H. Hassanpour, M. Mesbah, and B. Boashash, Time-frequency based newborn EEG seizure detection using low and high frequency signatures, Physiological Meas., vol. 25, pp , [11] S. Haykin, Neural Networks: A Comprehensive Foundation. Upper Saddle River, NJ: Prentice-Hall, [12] R. A. Hrachovy, E. M. Mizrahi, and P. Kellaway, Electroencephalography of the newborn, in Current Practice of Clinical Electroencephalography, D. Daly and T. A. Pedley, Eds. New York: Raven, 1990, pp [13] N. B. Karayiannis, A. Mukherjee, J. R. Glover, J. D. Frost Jr., R. A. Hrachovy, and E. M. Mizrahi, An evaluation of quantum neural networks in the detection of epileptic seizures in the neonatal electroencephalogram, Soft Computing J., vol. 10, no. 4, pp , [14] P. Kellaway and J. D. Frost Jr., Monitoring at the Baylor College of Medicine, Houston, in Long-Term Monitoring in Epilepsy, J. Gotman, J. R. Ives, and P. Gloor, Eds. Amsterdam, The Netherlands: Elsevier Science, 1985, pp [15] A. Liu, J. S. Hahn, G. P. Heldt, and R. W. Coen, Detection of neonatal seizures through computerized EEG analysis, Electroencephalogr. Clin. Neurophysiol., vol. 82, pp , [16] E. M. Mizrahi, Neonatal electroencephalography: Clinical features of the newborn, techniques of recording, and characteristics of the normal EEG, Am. J. EEG Technol., vol. 26, pp , [17], Neonatal seizures, in Childhood Seizures: Pediatric and Adolescent Medicine, S. Shinnar, N. Amir, and D. Branski, Eds. Basel, Switzerland: Krager, 1995, vol. 6, pp [18] E. M. Mizrahi, R. R. Clancy, J. K. Dunn, D. Hirtz, L. Chapieski, S. McGuan, P. Cuccaro, R. A. Hrachovy, M. S. Wise, and P. Kellaway, Neurologic impairment, developmental delay, and postneonatal seizures 2 years after EEG-video documented seizures in near-term and term neonates: Report of the clinical research centers for neonatal seizures, Epilepsia, vol. 42, no. suppl. 7, pp , [19] E. M. Mizrahi and P. Kellaway, Characterization of seizures in neonates and young infants by time-synchronized electroencephalographic/polygraphic/video monitoring, Ann. Neurol., vol. 16, p. 383, [20] A. Mukherjee, J. R. Glover, N. B. Karayiannis, J. D. Frost Jr., R. A. Hrachovy, and E. Mizrahi, Improvement of narrowband epileptic seizure detection in neonates using neural networks, in Proc. 21st Annu. Houston Conf. Biomedical Engineering Research, Houston, TX, Feb , 2004, p [21] S. K. Pal and S. Mitra, Neuro-Fuzzy Pattern Recognition. New York: Wiley, [22] G. Purushothaman and N. B. Karayiannis, Quantum neural networks (QNNs): Inherently fuzzy feedforward neural networks, IEEE Trans. Neural Netw., vol. 8, no. 3, pp , May [23], Feed-forward neural architectures for membership estimation and fuzzy classification, Int. J. Smart Eng. Syst. Design, vol. 1, pp , [24], On the capacity of feed-forward neural networks for fuzzy classification, in Intelligent Engineering Systems Through Artificial Neural Networks, C. H. Dagli, M. Akay, C. L. P. Chen, B. R. Fernandez, and J. Ghosh, Eds. New York: ASME Press, 1995, vol. 5, pp [25] M. Shastry, J. R. Glover, and R. A. Hrachovy, Automated detection of complex epileptic seizure waveforms in the neonatal EEG, presented at the 18th Annu. Houston Conf. Biomedical Engineering Research, Houston, TX, Feb , [26] A. Thitai-Kumar, V. M. Muktevi, J. R. Glover, P. Y. Ktonas, J. D. Frost Jr., R. A. Hrachovy, and E. M. Mizrahi, Automated detection of epileptic seizure segments in the neonatal EEG, presented at the 20th Annu. Houston Conf. 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8 640 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 4, APRIL 2006 Nicolaos B. Karayiannis (S 85 M 86 SM 01) was born in Greece, on January 1, He received the Diploma degree in electrical engineering from the National Technical University of Athens, Athens, in 1983, and the M.A.Sc. and Ph.D. degrees in electrical engineering from the University of Toronto, Toronto, ON, Canada, in 1987 and 1991, respectively. He is currently a Professor in the Department of Electrical and Computer Engineering, University of Houston, Houston, TX. From 1984 to 1991, he worked as a Research and Teaching Assistant at the University of Toronto. From 1983 to 1984, he was a Research Assistant at the Nuclear Research Center Democritos, Athens, where he was engaged in research on multidimensional signal processing. He has published more than 140 papers, including 65 in technical journals, and is the co-author of Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications (Kluwer, 1993). His current research interests include biomedical imaging and video, computer vision, image and video coding, neural networks, intelligent and neuro-fuzzy systems, wireless communications and networking, and pattern recognition. Dr. Karayiannis is the recipient of the W. T. Kittinger Outstanding Teacher Award (1994) and the University of Houston El Paso Energy Foundation Faculty Achievement Award (2000). He is also a co-recipient of a Theoretical Development Award for a paper presented at the Artificial Neural Networks in Engineering 94 Conference. He is an Associate Editor of the IEEE TRANSACTIONS ON NEURAL NETWORKS and the IEEE TRANSACTIONS ON FUZZY SYSTEMS. He also served as the General Chair of the 1997 International Conference on Neural Networks (ICNN 97), held in Houston, TX, on June 9 12, He is a member of the Technical Chamber of Greece. Periklis Y. Ktonas (S 69 M 73 SM 02) was born in Athens, Greece, in He received the B.S. degree from Stanford University, Stanford, CA, in 1968, and the M.S. and Ph.D. degrees from the University of Florida, Gainesville, in 1970 and 1974 in electrical engineering. He is a Senior Research Scientist at the Department of Psychiatry, University of Athens Medical School, Athens, Greece, and he is also affiliated with the Greek Center for Neurosurgery Research. From 1974 until 2004, he was a faculty member in the Department of Electrical and Computer Engineering at the University of Houston, where he is now Professor Emeritus. At the University of Houston he was also the Director of the Biomedical Engineering Program and of the Bioengineering Research Center. His research activities have focused on the development of methodologies for the accurate and efficient automated analysis of bioelectrical signals, with clinical applications in neurology and psychiatry. He is especially interested in epilepsy and in sleep research. He is a member of several professional societies, including the IEEE (Senior Member), the European Sleep Research Society and the American Clinical Neurophysiology Society. He has participated in the organization of national and international conferences on engineering applications in biomedicine. He was Program Chair for the Annual International IEEE EMBS Conference in He is an Associate Editor of the IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, as well as chair of the IEEE EMBS Technical Committee on Neuroengineering. He received the IEEE Third Millenium Medal for his contributions to biomedical engineering. Amit Mukherjee was born on December 22, He received the B.E. degree in instrumentation and control engineering from the University of Pune, Pune, India, in 2001 and the M.S. degree in electrical engineering from University of Houston, Houston, TX, in His research interest primarily includes signal and image processing, pattern recognition and neural networks. John R. Glover (S 67 M 68 SM 98) was born in Savannah, GA, on July 22, He received the B.A. and M.E.E. degrees in electrical engineering from Rice University, Houston, TX, in 1967 and 1968, respectively, and the Ph.D. degree in electrical engineering from Stanford University, Stanford, CA, in From 1970 to 1971, he worked as an Electronics Engineer at Headquarters, U.S. Army Security Agency, Arlington Hall Station, VA. From 1971 to 1974, he was a National Science Foundation Fellow at Stanford University. In 1975, he joined the Cullen College of Engineering at the University of Houston, Houston, where he is now a Professor in the Department of Electrical and Computer Engineering. His current research interests are in the area of intelligent signal interpretation, particularly as applied to biomedical signals. In 1981, Dr. Glover received the Outstanding Transactions Paper Award from the IEEE Education Society. James D. Frost, Jr. was born in Porterville, California. He received his B.A. degree in biology from Stanford University in 1958 and an M.D. degree from Baylor University College of Medicine in He subsequently did an internship at The Methodist Hospital in Houston in , and completed a fellowship in Clinical Neurophysiology in He has been a member of the faculty at Baylor College of Medicine, Houston, since 1963, and he is currently a Professor in the departments of Neurology and Neuroscience at that institution. He is a member of the medical staffs at Texas Children s Hospital, St. Luke s Episcopal Hospital, The Methodist Hospital, and the Harris County Hospital District, all in Houston. His specific areas of clinical and research interest include sleep disorders, epilepsy, and both basic and clinical neurophysiology. He established the first clinical sleep laboratory in the Houston area at The Methodist Hospital in 1971, and served as its Medical Director until His current research interests include the development of automated methods for analyzing and interpreting EEG activity, and investigation of the pathophysiology and treatment of childhood seizure disorders. He recently co-authored the book Infantile Spasms: Diagnosis, Management, and Prognosis (Kluwer Academic, 2003). Dr. Frost received Scientific-Technical Contribution Awards from the National Aeronautics and Space Administration in 1970 and 1971 for research conducted during the Skylab program, and in 1974 he was awarded the NASA Medal for Exceptional Scientific Achievement for this work. He has been awarded several patents for devices providing improved methods for EEG acquisition and analysis. He has served on the editorial boards of the journal Clinical Neurophysiology and the Journal of Clinical Neurophysiology, and has been a member of several NIH review committees. He is a member of the American Clinical Neurophysiology Society, the Harris County Medical Society, and the Texas Medical Association.

9 KARAYIANNIS et al.: DETECTION OF PseudoSINUSOIDAL EPILEPTIC SEIZURE SEGMENTS IN THE NEONATAL EEG 641 Richard A. Hrachovy received the B.S. degree in zoology from Texas A & M University, College Station, in 1970 and an M.D. degree from University of Texas Medical Branch, Galveston, in He completed a residency in neurology at University of Texas Medical Branch in 1976 and a fellowship in Clinical Neurophysiology at Baylor College of Medicine, Houston, TX, in Currently, he is a Professor of Neurology at Baylor College of Medicine. He is Deputy Neurology Care Line Executive, Michael E. DeBakey Veterans Affairs Medical Center and Associate Chief of EEG, St. Luke s Episcopal Hospital, Houston. He is also a member of the medical staffs at the Methodist Hospital, Texas Children s Hospital, and the Harris County Hospital District. He is on the editorial board of the Journal of Clinical Neurophysiology. His main research interest is seizures in the developing brain and involves both basic and clinical investigations. In collaboration with other investigators at Baylor College of Medicine, he has helped develop and study several animal models of early life seizures. He has led the clinical investigations of the pathophysiology and treatment of infantile spasms at Baylor College of Medicine. He has also worked with investigators from Baylor College of Medicine and the University of Houston to develop computer analysis of neonatal EEG and seizures. Recently, he co-authored two books Infantile Spasms: Diagnosis, Management, and Prognosis (Kluwer, 2003) and the Atlas of Neonatal Electroencephalography (Lippincott Williams & Wilkin, 2004). He is a member of the Alpha Omega Alpha Honor Medical Society, the American Academy of Neurology, the American Clinical Neurophysiology Society, the American Epilepsy Society, the Harris County Medical Society and the Texas Medical Association. Eli M. Mizrahi received the undergraduate degree in psychology from Emory University, Atlanta, GA, in 1971 and the M.D. degree from the University of Miami, Coral Gables, FL in He completed an internship and residency in Pediatrics at Albert Einstein College of Medicine, Bronx, NY; a residency in Neurology (Pediatric Neurology) at Stanford University Medical Center, Stanford, CA; and a postdoctoral fellowship in Clinical Neurophysiology at Baylor College of Medicine, Houston, TX, under the direction of P. Kellaway. He is Head of the Peter Kellaway Section of Neurophysiology; Professor of Neurology and Pediatrics; and Vice-Chairman, Department of Neurology; Baylor College of Medicine. He also serves as Chief of Neurophysiology Services at The Methodist Hospital and St. Luke s Episcopal Hospital and Chief, Neurophysiology Laboratory Services, Texas Children s Hospital. He directs the Baylor Comprehensive Epilepsy Center and the Clinical Research Center for Neonatal Seizures, both based at The Methodist Hospital. In 1982, he joined the faculty at Baylor College of Medicine. A main focus of his research has been on neonatal seizures. He has investigated the clinical aspects of characterization and classification, electroencephalographic and EEG-video features, pathophysiology and therapies. He has worked in collaboration with other investigators at Baylor and the University of Houston on computer analysis of both EEG and video imaging of neonatal seizures. Dr. Mizrahi received a clinician-scientist investigator award (K08) from the National Institutes of Health (NIH) early in his career, followed by additional NIH funding throughout his career. He was awarded the Michael Prize from the Stiftung Michael, Bonn, Germany (1988) and the American Epilepsy Society/Milken Family Medical Foundation Clinical Research Award (1992). He has served as the Chair, Professional Advisory Board, Epilepsy Foundation of America and President, American Clinical Neurophysiology Society.