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COMPUTERS AND BIOMEDICAL RESEARCH ARTICLE NO.

29, 303–313 (1996)

0022

Detection of Seizure Activity in EEG by an Artificial Neural Network: A Preliminary Study N. PRADHAN,*,1 P. K. SADASIVAN,*

AND

G. R. ARUNODAYA†

*Department of Psychopharmacology and †Department of Neurology, National Institute of Mental Health & Neurosciences, Bangalore-560029, India

Received December 19, 1995

Neural networks, inspired by the organizational principles of the human brain, have recently been used in various fields of application such as pattern recognition, identification, classification, speech, vision, signal processing, and control systems. In this study, a two-layered neural network has been trained for the recognition of temporal patterns of the electroencephalogram (EEG). This network is called a Learning Vector Quantization (LVQ) neural network since it learns the characteristics of the signal presented to it as a vector. The first layer is a competitive layer which learns to classify the input vectors. The second, linear, layer transforms the output of the competitive layer to target classes defined by the user. We have tested and evaluated the LVQ network. The network successfully detects epileptiform discharges (EDs) when trained using EEG records scored by a neurologist. Epochs of EEG containing EDs from one subject have been used for training the network, and EEGs of other subjects have been used for testing the network. The results demonstrate that the LVQ detector can generalize the learning to previously ‘‘unseen’’ records of subjects. This study shows that the LVQ network offers a practical solution for ED detection which is easily adjusted to an individual neurologist’s style and is as sensitive and specific as an expert visual analysis.  1996 Academic Press, Inc.

INTRODUCTION With the availability of high-end computing systems, artificial neural networks (ANN) have found application in many areas of signal processing. Complex signals like the electroencephalogram (EEG) are of massive volume and often require the personal judgment of an expert for analysis. The detection and quantification of epileptiform discharges is essential for the clinical diagnosis and monitoring of patients in the treatment of epilepsy. There has been a keen interest in automated, computer-based techniques for the recognition of epileptiform discharges (EDs) for event detection and data reduction. Epilepsy-monitoring units generate large amounts of normal data, intermixed with relatively rare 1

To whom correspondence should be addressed. Fax: 0091-080-6631830. 303 0010-4809/96 $18.00 Copyright  1996 by Academic Press, Inc. All rights of reproduction in any form reserved.

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EDs and seizures. Some success has been achieved in the automatic recognition of EDs, primarily through rule-based approaches (1–6). Rule-based systems, which produce a large number of false positive results, are complex and require considerable computing time (7). There have been a number of approaches to automate the detection of interictal EDs and/or seizures. Two rule-based or ‘‘expert’’ systems, developed by Davey et al. (8) and Glover et al. (7) use specific methods of organizing and applying knowledge to efficiently test candidate EDs by a series of ‘‘if . . . then . . .’’ statements. These systems produce sensitivities and specificities comparable to expert visual analysis. However, due to their programming environments and processing demands, these are not efficient enough to run on inexpensive and readily available personal computers (6, 9). The third rule-based system, developed by Gotman et al., uses a combination of a rule base, statistical analysis of background EEG, and parameterization of EDs to detect epileptiform activity for identifying both interictal EDs and seizures (1, 3, 4). This system is the first automated method of ED detection to become commercially available. It has been further revised and expanded to decrease the percentage of false positive events detected, since this may impair its performance in busy monitoring environments (3, 4). Artificial neural networks may be seen as an alternative to rule-based systems for computer analysis of the EEG. ANNs may be of practical value in screening voluminous EEG data in real recording situations. ANNs may obviate the need for articulating rules, as they are trained by example. However, ANN applications can be computation intensive; though with an intelligent design, they may also be capable of rapid and reliable pattern recognition (10). ANNs have been used for ED detection, but only using preprocessed data. Recent applications of ANNs for ED detection (9, 11–16) have used feed-forward neural networks and back-propagation learning rules. The neural networks operating on digital EEG data have the potential of being used ‘‘on-line.’’ The ‘‘off-line’’ screening of EEG records is a common requirement in EEG laboratories. The use of ANNs in the off-line screening tasks may save considerable time and effort. The theory and computational aspects of neural networks are described in several recent monographs and books (10, 17–19). It is difficult to compare the performance of various ANN methods as there are no common training, no clear test cases, and no agreed-upon method for the assessment of performance. Therefore, differences exist between the various ANN approaches to ED detection including clinical conditions, data acquisition methods, number of subjects, number of channels of input data, and the computational efficiency of the ANN for real-time implementation. However, the most important difference between methods lies in how the EEG data are prepared for presentation to the ANN for both training and testing. The method of preprocessing influences the features of a particular approach. An ideal procedure should require minimum preprocessing and be applicable to raw data. An ideal ANN may generalize and detect ‘‘unseen’’ cases. Independence of the test and training data sets is an issue only for ED detectors that are trained

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by examples like ANNs, since rule-based systems do not generally have training sets. If the test set for such a system includes data from subjects that have been used to train it, then the performance on data from new subjects is less predictable because the new data may have ED morphology that the ANN has not previously experienced, particularly if the original training set contains only a limited range of patterns. We have used the Learning Vector Quantization (LVQ) method on the data set of a single subject and have evaluated its generalization to other records from different subjects (20). The number of false positive and false negative records have also been evaluated. METHOD Five subjects in the age group of 15–25 years were recruited for this study. They were known epileptics with uncontrolled seizures and were admitted to the neurology wards of the National Institute of Mental Health and Neurosciences, Bangalore. Despite anticonvulsant medication, the subjects showed seizure discharges in their EEGs. The eight channels of analog EEG data (Fp1, Fp2, F7, F8, T3, T4, O1, and O2) were acquired from the subjects by direct digitization to a microcomputer (PC-AT). The data were acquired at 256 samples/sec/channel from a conventional EEG machine (Nihon Kohden) by the use of an ADC and an array processor (Data Translation, DT-2841, DT-7020). The recording conditions followed Guideline 7 of the American EEG Society (21) and electrodes were placed according to the International 10–20 system. For a given sensitivity, the outputs of the EEG amplifiers were around 1.2 to 6.2 V, which was of a sufficient magnitude for digitization by the 12-bit ADC with adequate resolution (22). The data from the PC-AT were ported to a high-speed graphics workstation (HP-9000/735) for further analyses. The raw EEG signals were filtered on-line through a bandpass (0.05–32 Hz) 4th-order Butterworth filter twice cascaded. The filtering removed many unwanted signals. The filtered data were found to be suitable for visual analysis by the neurologist. The data were displayed on the computer monitor and a computer-assisted program was used for collection of epochs containing EDs. The records were scored as definite, probable, and non-EDs (normal background activity) independently by two experts who differed in their scorings by less than 5%. The differences were resolved by discussion and a consensus rating was assigned to the discrepantly rated epochs. The filtered EEG signals were segmented to 12-sec durations which were suitable for display on a 19-in. monitor. The training records were then selected. If any of these records contained eye-blink artifacts, certain background EEG activity containing eye blinks were incorporated into the training data. This was done to ensure the reduction of common confounding patterns among the various defined classes of EEG. The ANN was then trained on a large number of data sets from a single subject’s EEG for EDs, probable EDs, and normal background EEG activity. Of the five subjects’ EEG records, only one subject’s record was used for obtaining the training epochs for the ANN while the rest were used for

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FIG. 1. LVQ network architecture.

testing. Data from all channels of each EEG record were used. The software was developed using Matlab 4.2 (Mathworks, Inc., U.S.A., under Unix). LVQ NETWORK LVQ is a method for training competitive layers of a back-propagation network in a supervised manner (20). A competitive layer automatically learns to classify input vectors. However, the classification is dependent only on the distance between the input vectors. If two input vectors are similar, the competitive layer puts them into the same class. There is no mechanism in a competitive layer design to detect whether or not any two input vectors are in the same or different class. But the LVQ competitive layer learns to classify input vectors in target classes chosen by the user. This supervised training may reduce errors of classification due to the proximity of vector distances. Network Architecture The LVQ network architecture (20) is shown in Fig. 1. In the figure, R is the number of inputs, S1 is the number of competitive neurons, S2 is the number of linear layer neurons, W1 is the weight matrix of the competitive neurons, and W2 is the weight matrix of the linear layer neurons. The LVQ network consists of two layers. The first layer is a competitive layer and its learning characteristics are similar to the competitive layers in selforganizing networks. The second layer transforms the competitive layer’s classes and produces user-defined target classifications. Both the competitive and the linear layers have one neuron per class. Thus the competitive layer can learn up to S1 classes. These, in turn, are combined by the linear layer to form S2 target classes (20). The outputs of the competitive layer and that of the linear layer are given by the following equations,


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a1 5 compet(2dist(W1 , p)) a2 5 purelin(W 2*a1), where a1 is the negative of the distance between input vector p and the competitive layer weight matrix W1 , and a2 is the linear combination of the output linear layer weight matrix W2 and a1 . Network Initialization Given the input vector p, the number of competitive neurons S1 , and target vectors t, the weights W1 and W2 of both layers of LVQ network are initialized with biases zeroes. Each weight W(i, j) associated with an input hij is set to the center of the interval spanned by that input hij. The indices for training vector classes provided by the user are converted to target vectors t. The row vector of the indices is converted to a matrix-form M 3 N where N is the number of indices and M is the largest index in the class. The target vectors t contain S2 elements where S2 is the number of target classes. In addition, the target vectors are single-value vectors with 1 in one position and 0 in all other positions. It is also important that the input/target vectors be properly defined for the correct initialization of second-layer weights. The relative distribution of target classes should correspond to the input vectors. The number of neurons allocated to a class depends upon the number of input vectors belonging to that class. Learning Rule Once an LVQ network is initialized, the neurons in the competitive layer are assigned to neurons in the linear layer and the learning can begin. The LVQ learning rule is developed from the Kohonen rule (23). When applying the Kohonen rule, the competitive neuron whose weight vector forms the closest match with the input vector wins the competition and outputs the value 1. The winning neuron’s weights are then updated with the Kohonen rule to move the weight vector closer to the input vector. The Kohonen rule is given below, DW1(i, j) 5rl a1(i)[ p( j) 2 W1(i, j)],

[1]

where DW1(i, j) is the update of the weight of the winning competitive neuron hij, rl is the learning rate, and p is the input vector. In LVQ networks the target vector t1 for the competitive layer is found using the linear layer weights W2 , which assign competitive neurons to linear neurons and the network targets t as follows: t1 5 W T2t,

[2]

where superscript T stands for the transpose of the weight matrix W2 . This expression transforms a target vector t for the linear layer, containing a single 1 in the position of the target class, into a target vector t1 for the competitive layer, containing 1’s in the positions of all neurons which form subclasses of the

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target class. The Kohonen rule is used, as shown above, to update the weights of a winning competitive neuron hij when that neuron’s target is 1 (23). Thus, a winning neuron’s weights move toward the input vector only if the neuron forms a subclass of the current target class. If the winning neuron’s target is 0 (i.e., the neuron forms a subclass of a class other than the current target class), then the Kohonen rule is applied, but with the sign changed (23). This moves the winning neuron’s weight vector away from the input vector. It results in competitive neurons moving toward input vectors which belong to their class and away from input vectors that belong to other classes. Competitive neurons forming the same class compete with each other to form subclasses. Given the competitive layer weights W1 and the input vector p, the competitive layer outputs a1 and target t, and a learning rate r1 , the learning process results in changes in weights of competitive layer neurons (Eq. [1]).

Training The LVQ network is trained by repeatedly applying the Kohonen learning rule to input and target vector pairs in a random order. The weights W1 and W2 and input and target vectors ( p and t) are used in the training to return new weights (20). The training cycles are repeated at a learning rate of 0.05 until the competitive neurons get closer to their designated class of input vectors. The training is terminated when the competitive neurons are close to their designated class of input vectors and the output of the linear layer of the winning neuron is 1 and that of all other linear neurons is 0. This is a function of the number of training cycles.

Testing of the Network After the training, the network is presented sequentially with a number of test vectors. At any given time, only one of the linear output neurons may have an output of 1. Depending upon the number of output neurons, the target classes are shown. The network response is calculated from the matrix of the input test vector p, the weight matrices W1 and W2 , and the bias vector b of the linear layer. Thus, LVQ networks classify the given input test vectors into target classes by using a competitive layer to find subclasses of input vectors and then combine them into the target classes. Unlike single-layer perceptrons, LVQ networks can classify any set of input vectors. The only requirement is that the competitive layer must have enough neurons and each class must be assigned enough competitive neurons. To ensure that each class is assigned an appropriate amount of competitive neurons, it is important that the target vectors used to initialize the LVQ network have the same distribution of targets as the data on which the network is trained. This results in the target classes with more vectors becoming a union of a number of subclasses. In this study, three quantities were used to calculate sensitivity and specificity:


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FIG. 2. A representative record (eight-channel EEG data) used in training the LVQ network. Class 1 stands for definitive EDs, class 2 for probable EDs, and class 3 for normal background activity.

(1) ED count—the number of events the neurologists marked as definite or possible, (2) match count—the number of LVQ outputs that matched events marked by the neurologists as definite or possible (that is, the number of correct detections), and (3) detector count—the total number of events produced by the LVQ to a given unseen test vector. The specificity is defined as the ratio of matched counts/LVQ detector counts and the sensitivity is given by matched counts/total EDs present in the records.

RESULTS In this paper, we present our preliminary investigation of the usefulness of the LVQ network for ED detection. A representative data set used for training the network is shown in Fig. 2. We have used 6–24 data sets (3072 points each) containing EDs from a single subject as the training sets. The training data sets are grouped into two classes, class 1 with definite EDs and class 2 with probable EDs. In addition to these, 2–6 data sets that do not contain EDs (class 3) are also included in training the network. It is observed that the training takes a

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PRADHAN, SADASIVAN, AND ARUNODAYA TABLE 1

Visual score by neurologist LVQ score Matching of visual and LVQ

No. of epochs scored ‘‘definite’’

No. of epochs scored ‘‘probable’’

Total

320 292 288

128 125 24

448 417 312

smaller number of cycles when there is an appreciable distance among vector classes. The test data sets were obtained from the EEGs of four subjects. The entire EEG record of each subject was tested. In most cases, the LVQ network accurately classified the test data. The results are presented in Table 1. The four test subjects have 32 (8 channels 3 4 subjects) data files. The EDs marked by the neurologists as definitive were present in 10 data files amounting to 320 epochs (3072 points each). Four data files having 128 epochs were scored as probable EDs. All 80 class 3 vectors (normal background activity) were detected as class 3 by the LVQ network. The LVQ detected 292 epochs (91.25%) of the definite EDs and 125 epochs (97.65%) of the probable EDs. The overall performance of the LVQ network [(the definite EDs 1 probable EDs detected by the LVQ network/the definite EDs 1 probable EDs detected by the neurologist) 3 100] is 93.08%. The specificity is found to be 0.748 and the sensitivity 0.67, which is in the acceptable range of clinical tests. DISCUSSION This study demonstrates that an LVQ network trained on the data of a single subject can detect EDs in the EEG of other subjects with an acceptable sensitivity and specificity. Large sets of ED records containing definitive and probable EDs have been used in the training. In addition, data sets containing normal background EEG activity are also included in the training sets. The number of output neurons is evidently equal to the number of the target classes (3 in this study). Increasing the number of competitive neurons improves the performance of the LVQ network. However, in the case of EEG, the signal spaces of various input vectors do not form definitive clusters according to the classes. So, an increase in the number of competitive neurons beyond the number of input vectors does not improve the network performance significantly. We have evaluated the performance of a LVQ network by increasing the number of competitive neurons from 3 to 30 with an input of six vectors. A large number of competitive neurons (30) improves performance of the network but at a considerable cost of computer time for training (77.8 hr). However, once trained, the LVQ network is quick to classify 3072 points of data across eight channels of EEG. Therefore, the trained network is capable of being used in real time in neurological-monitor-


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ing situations. The detection rate of 93.08% is considered to be high and may even be superior to other rule-based methods. Other research groups have used ANN-based ED detectors with variable success. Ozdamar et al. and Yayali et al. have achieved sensitivities and specificities of 90 and 69% and 92.8 and 78.5%, respectively (13, 16). The ANN used by Yayali and Ozdamar consists of two sets of ANNs cascaded and a three-layer ANN for each of 16 channels. The outputs of these ANNs are fed to another three-layer ANN that produces the final result. The test and training sets used in Ozdamar’s study contain 60 training and 60 test patterns. Webber et al. have used 1200 training events and 4000 test events (9). The test sets of most investigators include some subjects that have been included in the training sets. This may account for their improved results. A large number of training sets also contribute to improved discriminations. The investigators have found differences in the sensitivity of ANN-based ED detection because of preprocessing, where certain parameters of ED are used for training. They have used very short blocks of data for testing. However, the preprocessing may introduce many adhoc measurers at the cost of improved performance. What is required is a simple method that can discriminate the normal background EEG activity from that of EEG activities containing EDs. This has been achieved by the LVQ network. For definite EDs, there were a few false positive epochs detected by the LVQ network. One of these could be attributed to an artifact. This epoch when reviewed by the experts was also found by them to be confusing. This epoch was preceded by EDs in the same signal and could have been mistaken for ED by novel observers. This suggests that good results can be obtained with artifact-free raw EEG data with the LVQ network. The LVQ network provides quick ED detection and may be seen as a practical solution to data reduction and analysis in units employing 24-hr continuous video and EEG monitoring. The performance of the LVQ ANN should be viewed in the context of availabilitiy of high-performance desktop computer systems which may be exploited for their practical application in settings where continuous and real-time processing of large volumes of EEG data is required (5, 16, 24). Importantly, the LVQ network can generalize its behavior to previously unseen data, unlike the previous studies in this field. One limitation of this study is the false positive cases encountered due to artifacts. More tests involving large numbers of data sets are essential for evaluating network generalization. Extensive tests of performance of the LVQ ANNs are currently under way in our laboratory. The importance of the method is that the LVQ ANN can be implemented on large data vectors. The LVQ networks are advantageous for their easy adaptability, both to the individual neurologist’s style and to specific clinical situations, and their flexibility in relation to a supervised method of training. The user can manipulate and define the classes as small or large. Our investigation shows that the LVQ network successfully works on raw EEG data without any preprocessing or parameterization. The ED-positive epochs of an EEG record which carry the relevant information about the clinical status of the subject may be stored.

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CONCLUSION Artificial neural networks are clinically useful in feature detection and classification of EEG signals in neurological diseases. A simple and reliable architecture of an ANN is always welcome. We have demonstrated the utility of a LVQ network with supervised training as an efficient and reliable tool for ED detection in the EEG. The LVQ network is a neural network with associative memory. The network is a simple two-layered architecture consisting of a competitive layer and a linear output layer. The competitive neurons learn to classify the input vectors and the linear layer transforms the output of the competitive layer to user-defined target classes. The above architecture is found to be sufficient for learning temporal patterns in EEG for ED detection. Further, the network learning is generalized to unseen vectors. Once trained with EEG from one subject, the network is capable of detecting EDs in EEG records of other individuals. This makes the LVQ network a potential tool for ED detection in neurological laboratories and monitoring conditions. The sensitivity and specificity of the network are similar to that of expert visual analysis. It can be potentially used for on-line seizure monitoring due to its simple architecture and generalized learning properties. REFERENCES 1. GOTMAN, J., GLOOR, P., AND SCHAUL, N. Comparison of traditional reading of the EEG and automatic recognition of interictal epileptic activity. Electroencephalogr. Clin. Neurophysiol. 44, 48 (1978). 2. FROST, J. D., JR. Automatic recognition and characterization of epileptiform discharges in the human EEG. J. Clin. Neurophysiol. 2, 231 (1985). 3. GOTMAN, J. Automatic recognition of interictal spikes. Electroencephalogr. Clin. Neurophysiol. 37, 93 (1985). 4. GOTMAN, J., AND WANG, L. Y. State-dependent spike detection: Concepts and preliminary results. Electroencephalogr. Clin. Neurophysiol. 79, 11 (1991). 5. GOTMAN, J., AND WANG, L. Y. State dependent spike detection: Validation. Electroencephalogr. Clin. Neurophysiol. 83, 12 (1992). 6. WEBBER, W. R. S., LITT, B., LESSER, R. P., FISHER, R. S., AND BANKMAN, I. N. Automatic EEG spike detection: What should the computer imitate? Electroencephalogr. Clin. Neurophysiol. 87, 364 (1993). 7. GLOVER, J. R., JR., RAGHAVAN, N., KTONAS, P. Y., AND FROST, J. D., JR. Context-based automated detection of epileptogenic sharp transients in the EEG: Elimination of false positives. IEEE Trans. Biomed. Eng. 36, 519 (1989). 8. DAVEY, B. L., FRIGHT, W. R., CARROL, G. J., AND JONES, R. D. Expert system approach to detection of epileptiform activity in the EEG. Med. Biol. Eng. Comput. 27, 365 (1989). 9. WEBBER, W. R. S., LITT, B., WILSON, K., AND LESSER, R. P. Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: A comparison of raw and parameterized EEG data. Electroencephalogr. Clin. Neurophysiol. 91, 194 (1994). 10. ZURADA, J. M. ‘‘Introduction to Artificial Neural Systems.’’ West Pub., St. Paul, 1992. 11. EBERHART, R. C., DOBBINS, R. W., AND WEBBER, W. R. S. In ‘‘Proceedings 11th Annual Conference IEEE/EMBS,’’ Seattle, 1989. 12. WEBBER, W. R. S., WILSON, K., LESSER, R. P., FISHER, R. S., AND EBERHART, R. C. On-line detection of epileptic spikes using a patient’s independent neural network. Epilepsia 31, 687 (1990).


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