GA-SVM Modeling of Multiclass Seizure Detector in ...

GA-SVM Modeling of Multiclass Seizure Detector in Epilepsy Analysis System Using Cloud Computing Chia-Ping Shen1, Feng-Sheng Lin2,5, Andy Yan-Yu Lam1, Wei Chen1, Weizhi Zhou1, Hsiao-Ya Sung2, Yi-Huei Kao4, Ming-Jang Chiu4, Jeng-Wei Lin6,*, Fang-Yie Leu6, and Feipei Lai1,2,3 1

Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 3 Department of Electric Engineering, National Taiwan University, Taipei, Taiwan 4 Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan 5 Institute of Information Science, Academia Sinica, Taipei, Taiwan 6 Department of Information Management, Tunghai University, Taichung, Taiwan

2

Abstract—In this paper, we present an Epilepsy Analysis System (EAS) for long term Electroencephalography (EEG) monitoring of seizure patients. In our previous works, a high accuracy seizure detection algorithm had been devised. After 980 features were extracted from raw EEG data, it adopted a genetic algorithm (GA) to select proper features for support vector machines (SVM) to classify EEG data into four classes, namely normal, spike, sharp wave, and seizure. However, some selected features were useless or had low impacts in the classification, and the training process was time consuming. In this study, we further enforce feature reduction in the GA-based feature selection. As a result, the average number of selected features is reduced significantly from 133.5 to 92.5 and the overall classification accuracy improves from 88.8% to 90.1% on a 363-hour EEG data set. The clinical EEG records were acquired from 28 participants, 3 of which are normal, and 28 are patients with epilepsy. The seizure detector can process a 10-second EEG signal within 0.6 seconds. It gracefully meets the real-time requirement for online EEG monitoring. To speed up the training process of the seizure detector, we design a two-layer MapReduce architecture on top of Apache Hadoop framework. When 15 servers are used, the training time is reduced from 38.3 to 4.9 hours. Therefore, when new EEG data are confirmed, an up-to-date seizure detector can be rebuilt quickly. The experiment results show our approach contributes very much to the effectiveness and efficiency of the EAS. Index Terms—Epilepsy, Seizure, Electroencephalogram, EEG, Genetic Algorithm, Support Vector Machine, Feature Selection, Feature Reduction, MapReduce

I. INTRODUCTION Today, biomedical signals, such as Electroencephalogram (EEG), Phonocardiogram (PCG), and Electrocardiogram (ECG), can provide much important information of patients for clinical practice [1]-[3]. Correct and fast analysis of these biomedical signals is very active in clinical researches. Techniques in digital signal processing and data mining, such as adaptive filtering, spectrum estimation, feature selection, pattern classification, and so on, have been used to analyze these signals. To improve the quality of care and to reduce medical cost, physicians began to use these intelligent algorithms to assist clinical decision-making. Many biomedical signal analysis systems have been developed to delivery useful biomedical information to physicians or patients. They are designed as solutions of diagnostic assistance and as an automatic system capable of clinical operations in real-time. Epilepsy afflicts approximately 1% of the global population [4]. In general, seizures attack when clusters of brain neurons discharge abnormally, that may temporarily cause abnormalities of the patients in consciousness, behaviors, movements and actions. One way to recognize seizure attacks is to analyze the EEG signals of the patients. EEG records cerebral electrical activities by measuring voltage changes from ionic current flows within the neurons of the brain [5], which shows temporal and spatial information of the brain, and is very useful in the diagnosis of epilepsy. Studies in the literature have showed that EEG signals provide high sensitivity and specificity for the diagnosis of epilepsy [6]. Many algorithms to detect epileptic seizures have been proposed [7]-[12]. In [9], an artificial neural network worked with features derived using short-time Fourier transform has been reported. In [10], an automatic detection method of epileptiform events based on features derived using independent component analysis (ICA) has been presented. In [11], based on one’s priori EEG patterns of seizure, the authors proposed an automatic process to model a patient-specific seizure detector using statistically optimal null filters (SONFs). In [12], an artificial neural network worked with wavelet feature sets has been reported. With successful implementation of these technologies, the quality of life and safety of the patients with epilepsy could be improved. In this paper, we present an Epilepsy Analysis System (EAS) for long term EEG monitoring of seizure patients. The duration of long term EEG monitoring may last for several hours. To our best knowledge, there is still short of such detection system in clinical practice. The EAS can discriminate against four kinds of EEG data (normal, spike, sharp wave, and seizure) of patients so that physicians can real-time scan the results of the EEG monitoring and classification. As well, when new EEG data are confirmed and annotated by physicians, we can include them to train an up-to-date classification model, especially for the EEG data from patients newly confirmed with epilepsy. In [1], we had presented a high accuracy seizure detection algorithm. After preprocessing, some features were extracted from * Corresponding author: J.-W. Lin is with the Department of Information Management, Tunghai University, Taichung, 704, Taiwan (R.O.C) (e-mail: [email protected]).

User

Graphic User Interface Client Site Authentication and Authorization

SOAP / HTTP

Dataset / XML

Data Preprocessing Feature Extraction

Feature Selection

Server Site

Classification

Data Access

(a)

(b) NTUH Database

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Fig. 1. The system architecture of EAS

NASION Fp1

Database

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EEG data. A naïve genetic algorithm (GA)F4 was F7 F8 then adopted to select features for support vector machines (SVM) to build the F3 Fz seizure detector. However, some useless or low-impact features in classification were still included. Moreover, the training A1 A2 process of the seizure detector was time consuming. This diminishes the frequency to rebuild a latest classification model of the T3 C3 Cz C4 T4 seizure detector. Pz In this study, feature reduction P3 is enforced P4 T5 T6in the GA-based feature selection. Among 980 extracted features, the average number of selected features is reduced from 133.5 to 92.5. I.e., 30.7% features are furthermore excluded. The overall accuracy O1 O2 improves from 88.8% to 90.1%. To huge computing power provided cloud computing we propose two-layer Fig.leverage 2. (a) EEGthe signal m and reliableINION onitoring using by International 10–20 system;technologies, (b) Unipolar montage; (c) Bipolara montage. MapReduce architecture on top of Apache Hadoop framework [30] to speed up the training process of the seizure detector. When 15 servers are used, the training time is reduced from 38.3 to 4.9 hours significantly. The training process accelerates 7.8 times. Our approach successfully improves the effectiveness and efficiency of the EAS. In the following sections, we first elaborate the system architecture of the EAS in Section II. Then, we present the feature reduction method enforced into the GA-based feature selection, as well as the design of the two-layer MapReduce architecture to parallelize the training process of the seizure detector in Section III. We show the experiment results and have discussions in Section IV. Finally, we conclude the paper in Section V. II. EPILEPSY ANALYSIS SYSTEM A. System Architecture Fig. 1 shows the system architecture of the user-friendly web-based EAS, which contains three major portions: the client site, the server site and the database. The client site provides a friendly graphical user interface for physicians and healthcare practitioners to interact with the server site and the database. Users must get authentication from the server site to validate the security information stored in database. System functionalities, such as the EEG monitor and seizure detector, are implemented as services at the server site. The database stores EEG data, the parameters of classification models, and so on. Most components of the system exchange information in XML (Extensible Markup Language), and communicate with others based on SOAP (Simple Object Access Protocol) over HTTP [13]. B. Data Acquisition and Preprocessing The database stores the EEG data of subjects receiving long-term EEG monitoring in the Department of Neurology, National Taiwan University Hospital (NTUH). The EEG signals are recorded from 16 channels and sampled at 200 Hz according to the standard International 10-20 System, as shown in Fig. 2. Both unipolar and bipolar montages are used. All EEG signals are segmented into two-second epochs. Each epoch has a label indicating its type. There are four epileptiform types: normal, spikes, sharp waves, and seizure, and two additional system types: rejected, and uncertain. All epochs used in the training and testing sets in this study are labelled by the physicians in NTHU. When two or more physicians have different opinions of an epoch, it is initially labeled as uncertain and will be resolved in the future. As well, when a physician has a question about the classification result of an epoch, he or she can manually re-label the epoch as uncertain. Since long-term EEG monitoring may last for several hours, not all epochs have to be analyzed. The main purpose of data preprocessing is to identify and to reject the presence of artifacts and noises that may affect the detection specificity [14]-[16]. In this study, a simple 60 Hz Butterworth low pass filter [17] is adopted to identify high-amplitude artifacts in epochs [1][15]. These epochs are labelled as rejected and will not be further analyzed. After preprocessing, features of each epoch are extracted as follows.

WT Low Pass Filter

Beta wave (15-30Hz)

Level 1

Alpha wave (8-15Hz)

WT Level 2 WT

Theta wave (4-8Hz)

Level 3 WT

Level 4 Delta wave (0-4Hz)

Preprocessing

Classification

Gamma wave (30-60Hz)

Feature Selection & Modeling

EEG Signal

Total Variation, Sample Entropy, …

C. Seizure Detection Algorithm 1) Feature Extraction Wavelet transform [18][19], which can extract the time and frequency characteristics of a signal simultaneously, is suitable to extract the spike-and-wave feature of EEG signals [1]. The seizure detection algorithm applies a four-stage discrete wavelet transform using Daubechies filter pairs to decompose the EEG signals in each epoch into five primary frequency bands (i.e., delta, theta, alpha, beta, and gamma) [20][21], as shown in Fig. 3. The algorithm extracts 980 features, categorized under three parts. In the first part, the algorithm calculates five features, namely the standard deviation, energy, total variation, skewness, and sample entropy, of each frequency band [22]. Thus, 400 features are extracted from 16 unipolar channels, each of which has 5 frequency bands. Based on the 16 unipolar channels, 16 bipolar channels are derived and additional 400 features are extracted in the same way. In this study, given a sequence X=[x1, x2, …, xN], whose mean and standard deviation are μ and σ, its energy, total variation, and skewness are defined as (1), (2), and (3) respectively.

Feature Extraction

Fig. 3. EEG signal processing

Energy (X ) = Ni=1| xi |. TotalVariation (X ) =

Skewness (X )=

(1)

Ni=2| xi − xi-1 | max(xi)−min(xi)

Ni=1(xi − μ)3 . 1 [NNi=1(xi − μ)2]3/2 1 N

.

(2)

(3)

A subsequence Xi of length m of X is defined as [xi, xi+1, …, xi+m−1], 1iN−m+1. Operator d [Xi, Xj], defined as (4), is used to assess the similarity between two subsequences of X, where r = k  σ is a constant threshold (k=0.1~0.9). 1, Xi − Xj  r d [Xi, Xj] =  . 0, otherwise

(4)

Sample Entropy of X is defined as (5), where Am(X, r) is defined as (6). SampEn(X, m, r) = −ln



Am(X, r) =

Am+1(X, r)  . Am(X, r) 

ΣN−m+1 ΣN−m+1 d [Xi, Xj] i=1 j=1 (N−m+1)2

.

(5)

(6)

In the second part, 120 correlation coefficients are calculated from the 16 unipolar channels [23][24].

Phase Reversal NASION Fp1

Fp1M=Fp1-F7+Fp1-F3+Fp1-F7+Fp1-F3+F7-T3+F3-C3 Fp2M=Fp2-F8+Fp2-F4+Fp2-F8+Fp2-F4+F8-T4+F4-C4

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F3M=F3-F7+F3-C3+F3-F7+F3-C3+C3-P3 F7

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F4M=F4-F8+F4-C4+F4-F8+F4-C4+C4-P4

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C3M=C3-T3+C3-F3+C3-P3+C3-T3+C3-F3+C3-P3 C4M=C4-T4+C4-F4+C4-P4+C4-T4+C4-F4+C4-P4

A1

T3

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Cz

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P3M=P3-C3+P3-T5+C3-F3+P3-C3+P3-T5 P4M=P4-C4+P4-T6+C4-F4+P4-C4+P4-T6 O1M=O1-T5+O1-P3+O1-T5+O1-P3+T5-T3+P3-C3 O2M=O2-T6+O2-P4+O2-T6+O2-P4+T6-T4+P4-C4

T5

Pz

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F7M=F7-Fp1+F7-T3+F7-F3+F7-Fp1+F7-T3+F7-F3+T3-T5 F8M=F8-Fp2+F8-T4+F8-F4+F8-Fp2+F8-T4+F8-F4+T4-T6

O1

O2

T3M=T3-F7+T3-T5+T3-C3+T3-F7+T3-T5+T3-C3+F7-Fp1+T5-O1 T4M=T4-F8+T4-T6+T4-C4+T4-F8+T4-T6+T4-C4+F8-Fp2+T6-O2 T5M=T5-T3+T5-O1+T5-P3+T5-T3+T5-O1+T5-P3+T3-F7

INION

T6M=T6-T4+T6-O2+T6-P4+T6-T4+T6-O2+T6-P4+T4-F8

Fig. 4. Pseudo Channels of Phase Reversal Fig. 5. Interface of the EEG Monitor. Area 1 is the channel name; area 2 is the signal chart; area 3 is the patient list; area 4 is the even list; area 5 is the annotation column; and area 6 is the pre-page and next pages.

In the third part, a pseudo channel of phase reversal is derived for each unipolar channel. Then, the algorithm calculates three features, namely minimum, total variation, and energy, from each pseudo channel. Since there are 16 pseudo channels, there are 16 minimums, 16 total variations, and 16 energies. Thus, 48 features are extracted. In addition, 12 features are extracted, which are minimum, maximum, mean, and standard deviation of the 16 minimums, 16 total variations, and 16 energies respectively. 2) Classification SVM is widely used due to its high accuracy and flexibility in modeling diverse sources of data [25][26]. The seizure detection algorithm adopts SVM to classify an epoch into the four different EEG kinds. A one-against-one approach [27] is used to perform multi-class classification. For any two classes, a SVM is determined to separate training samples as clearly as possible. As there are 4 classes (normal, spike, sharp wave, and seizure), 6 SVMs are determined. We note that the 6 SVMs may choose different features from the 980 features to achieve highest classification accuracies. When a testing or unknown sample is given, all 6 SVMs vote. A max-wins strategy is adopted, i.e., the sample is classified as the class that gets the most votes. 3) Feature Selection Before training a SVM, feature selection plays an important role in high dimensional biomedical data analysis. In [1], a naïve GA-based feature selection was adopted. A GA mimics the nature selection of biological evolution to solve complex problems, i.e., through the processes of inheritance, crossover, and mutation [28]. In this study, a population consists of many binary vectors, each of which associates with a SVM. Each bit in the vector indicates the inclusion or exclusion of a feature in the feature set used by the SVM. According to the vector, the selected features from the training samples (epochs) are used to train the SVM. I.e., the vector is the chromosome of the SVM. The fitness of the vector is the classification accuracy of the SVM on the testing samples (epochs). Initially, the GA starts with a population of binary vectors which are generated randomly. The associated SVM of each vector is trained accordingly and its fitness is evaluated. After all vectors in the population have been evaluated, elite vectors (SVMs) are identified as they have higher classification accuracies. The GA produces a next generation through inheritance, crossover, and mutation. Elite SVMs have a higher probability to be used in these processes. Generation after generation, the population evolves until a termination condition is satisfied. When the GA stops, the SVM that has best classification accuracy in the population is chosen, i.e., determined, and then recorded in the database. The 6 SVMs for one-against-one 4-class classification are independently determined by the GA to form the kernel of the seizure detector. Finally, all reserved samples (epochs) are classified by the seizure detector to evaluate the overall classification accuracy. D. Prediction and Feedback Currently, the EAS is being tried out experimentally by the physicians in NTHU. When subjects receive EEG monitoring in NTUH, their EEG data will be sent to the EAS and classified by the seizure detector. As shown in Fig. 5, the physicians can easily scan the classification results of the EEG data of a patient. They can feedback comments about the classification results, e.g., they can correct a false classification result, annotate a general type or specific subtype, or give a question mark for review in the future. These feedbacks can be included to rebuild an up-to-date classification model of the seizure detector. III. CHALLENGES AND METHODS We note that training a SVM is a quadratic programming optimization problem. Because of the natural property of SVM, sometimes SVM can still work well even when a few useless or noisy features are used. Although GA-based feature selection is applied, some useless or low-impact features may persist in the elite SVMs. The feature sets of the 6 SVMs determined by the original naïve GA were still large. Among the total 980 features, 133.5 were used in average. As a result, it took more time for the seizure detector to extract these features and make a decision for new EEG data. When GA-based feature selection is applied, which evolves for MG generations with population size PS, PS*MG SVMs will

Fig.7. The Two-layer MapReduce Architecture

be trained to determine a best SVM. Since 6 SVMs must been determined, the training time of the seizure detector is very long. When one generic PC-based server was used, the training time was about 38.3 hours for 180 hour EEG training data. This

Fig. 6. Population composition of new generation

discourages the rebuilding of a latest classification model of the seizure detector significantly. A. Feature Reduction Enforced in the new GA Unlike the original naïve GA, which initializes the first generation of the population randomly, in this study, one half of them are initialized according to the Fisher scores of features [29]. Features with higher Fisher scores are preferred with a higher probability to be included. Another half of them are randomly generated. The Fisher score, I (k, p, q), of a feature k for two classes p and q is defined as (7), where μp,k and σp,k denote the mean and standard deviation of the values of the feature k of all samples in class p, and μq,k and σq,k denote those in class q. I (k, p, q) =

(μp,k – μq,k)2 . σ2p,k + σ2q,k

(7)

Since some features probably have little or even no impacts on the SVM classification accuracy but long live in the naïve GAbased feature selection just because they are included in some elite SVMs, we enforce a feature reduction method to exclude useless features from the elite SVMs. In the original GA with a population size PS, the best EP (Elite Preservation) SVMs in a current generation will be preserved in the next generation. They are identified to as elite SVMs. Their fitnesses, i.e., classification accuracies, however, are probably not very good. A fitness threshold and reduction probability are introduced. The fitness of a real elite SVM must be equal to or higher than the specified fitness threshold. If all elite SVMs are real, they will be preserved in the next generation. If all elite SVMs are fake, the feature reduction is not enforced. Otherwise, a fake elite SVM will not be preserved. It will be replaced by a feature reduction enforced descendant of the best SVM, which must be real elite. In the associated vector of the best SVM, each 1 has a reduction probability to become 0. Thus, every feature of the best SVM has a probability to be enforcedly excluded. If an important feature is excluded, the new SVM will have poor classification accuracy and be abandoned by the GA. On the other hand, a useless feature will be excluded from the best SVM successfully. As shown in Fig. 6, SVMs in a current generation are sorted according to their fitnesses. Real elite SVMs in the left, whose fitnesses are equal to or larger than the fitness threshold, will be preserved in the next generation. Fake elite SVMs in the middle are replaced by feature reduction enforced descendants of the best SVM. Others in the right will be produced by the GA with the crossover and mutation procedures. Thus, a new generation is produced. B. Two-Layer MapReduce Architecture To deal with the huge computation and storage demands for processing EEG data, cloud-computing techniques are adopted. As described above, there are many SVMs to be trained in the new GA with enforced reduction. We notice that the SVMs in a same generation can be trained at a same time. In this section, we present the design of a two-layer MapReduce architecture to

speed up the training process of the seizure detector. Apache Hadoop is an open source software under the Apache Software Foundation [30]. This framework has a distributed file system called Hadoop Distributed File System (HDFS) and supports MapReduce parallel programming paradigm. HDFS is well designed to handle data replications and hardware failure and thus to enhance system reliability. It is capable of handling data in the scale of terabytes and larger. Therefore, it is suitable for the EAS to store and process the EEG data which rapidly grow day by day. MapReduce is a new and simplified parallel programming paradigm. It consists of two phases. In the first phase, named Map, the problem will be divided and all computing nodes will be assigned to compute a part of the problem. The results from all computing nodes are then summarized in the second phase, named Reduce, by reduce nodes. MapReduce provides builtin load balancing and fault-tolerance that are crucial to distributed computing. It greatly solves the issues of scalability, reliability, and availability in parallel programming in a large distributed system. Fig. 7 shows the design of the two-layer MapReduce architecture. The outer layer, referred to as hyper-plane layer, determines the 6 SVMs of the seizure detector in parallel. The inner layer, referred to as GA-SVM distributed layer, uses the MapReduce programming paradigm to parallelize GA-based feature selection and reduction to determine a SVM. There are 6 concurrent Hadoop jobs submitted into the system. Each job determines a SVM for two classes by the GA. The GA is implemented in Java, which loops, generation after generation, until the terminated condition is satisfied. In each generation, a MapReduce job is invoked to train and evaluate all SVMs in the population in parallel by computing nodes in Map phase, and then their associated vectors and fitnesses are passed to and summarized at the reduce node in Reduce phase to produce the next generation. When the GA evolves with MG (Maximum Generation) generations or the best fitness does not improve for SL (Stagnation Limit) generations, the GA stops. The best SVM in the final generation is determined for the two classes. When all GAs in the inner layer stop, 6 SVMs of the seizure detector are determined. The seizure detector is rebuilt and evaluated with the reserved samples. IV. EXPERIMENT RESULTS To evaluate the EAS, we collected clinical data from subjects receiving long-term EEG monitoring in the Department of Neurology, NTUH. The EEG dataset was acquired from 28 participants (3 normal subjects and 25 patients with epilepsy), whose ages ranged from 20 to 89 year old. Totally, there are 363 hour EEG records transformed from analog to digital, and annotated into the 4 different EEG types by the physicians in NTHU. Training, testing, and reserved EEG data are randomly partitioned into a ratio of 1:1:2, i.e., 90 hours, 90 hours, and 183 hours. In the following experiments, 15 severs in the HiCloud are used. Each server has the following specification: 1.0 GHz DualCore CPU, 8GB RAM, and 100GB HDD. HiCloud is a commercial cloud environment operated by Chunghwa Telecom in Taiwan. A. Enforced Feature Reduction In the first experiment, we train the 6 SVMs for the seizure detector by the new GA with enforced feature reduction. Each GA evolves at most 300 generations, or stops when the best fitness does not improve for 35 generations. I.e., MG=300 and SL=35. The population size (PS) is 100. The fitness threshold (classification accuracy) and reduction probability for the enforced feature reduction are 90% and 0.8 respectively. Table I shows the classification accuracies of the seizure detector and other methods presented in [10-12]. Our approach outperforms the others clearly in terms of both classification accuracy and number of supported EEG types.

TABLE I ACCURACY OF EEG DATA CLASSIFICATION

Original naïve GA [1] New GA withfeature reduction [10] [11] [12]

Normal

Spike

Seizure

91.1% 92.5% N/A N/A N/A

85.7% 86.5% 76.0 % N/A N/A

96.2% 98.1% N/A 92.2% N/A

Sharp Wave 76.6% 80.9% N/A N/A 60.1%

Overall 88.8% 90.1% N/A N/A N/A

Fig. 8. Numbers of selected features in original GA and the new GA with enforced feature reduction. (No: Normal; Sp: Spike; Sh: Sharp wave; Se: Seizure.)

Fig. 8 shows the number of features selected by the original naïve GA, and the new GA with enforced feature reduction. The average feature number is reduced from 133.5 to 92.5. I.e., 30.7% features are further excluded, which are probably useless or have low impacts in classification. As a result, the classification accuracies for all the four classes improve, and the overall accuracy improves from 88.8% to 90.1%. In addition, the overall time to predict a 10-second EEG signal takes only 0.6 seconds, including data preprocessing, feature extraction, and classification. This gracefully meets the real-time requirement in a clinical environment. B. Training Time of the Seizure Detector Although new GA with enforced feature reduction is very effective, however, it spent 38.3 hours to determine the 6 SVMs of the seizure detector when a single HiCloud server was used. In a one-layer design, i.e., the EAS simply determines the 6 SVMs in parallel without the use of the inner layer, the time is reduced to 10.3 hours when 15 servers are used. The time is furthermore reduced to 4.9 hours in the two-layer design. The training process of the seizure detector is accelerated about 7.8 times with the two layer design. Fig. 9 shows the required time when different numbers of servers are used. We notice that the overhead is high using MapReduce. As the number of servers increases, the overhead from the database access and mutual communication between modules also increases. When there are more than 6 servers, the speedup slows down in the one-layer design because there are only 6 SVMs to be trained. On the other hand, in the two-layer design, the time continually reduces as the number of servers increases. However, when more than 10 servers are used, the reduction trend is not significant. This is because some of the 6 SVMs spend more time to be determined than others and the longest one decides the training time of the seizure detector. Table II compares the training time of the seizure detector between the one-layer and two-layer designs when different parameters are used in the new GA with enforced feature reduction. It shows that the larger population size and maximal generation, the larger speed-up factors. The two-layer design clearly outperforms the one-layer design. V. CONCLUSION AND FUTURE WORK In this paper, we present an Epilepsy Analysis System (EAS) to help medical staffs make clinical decisions. The EAS can recognize four different kinds of EEG patterns at the same time, namely normal, spikes, sharp waves, and seizure. With the EAS, the medical staffs can upload and read EEG records, view the classification result, and annotate feedbacks when necessarily. The seizure detector is trained by the new GA with enforced feature reduction. The overall classification accuracy improves from 88.8% to 90.1%. The average number of selected features used by the seizure detector is reduced from 133.5 to 92.5 significantly. Useless features are excluded. Therefore, the seizure detector can process EEG signals more efficiently. Currently, it can process a 10-second EEG signal within 0.6 seconds. This gracefully meets the real-time requirement for online EEG monitoring. Our approach outperforms the three methods presented in [10-12] in terms of both classification accuracy and number of supported EEG types. To speed up the training process of the seizure detector, we design a two-layer MapReduce architecture on top of Apache Hadoop framework to leverage cloud computing technologies. The experiment results show that when 15 servers are used, the training time of the seizure detector is reduced from 38.3 hours to 4.9 hours significantly. As a result, when new EEG data are confirmed, an up-to-date seizure detector can be rebuilt quickly. The experiment results show our approach contributes very

Fig. 9. Training time of the seizure detectors TABLE II COMPARISON OF ONE-LAYER AND TWO-LAYER MAPREDUCE DESIGNS Parameter Layer Time PS MG EP SL Sets Type (Hr) One 10.3 I 100 300 35 35 Two 4.9 One 15.4 II 150 300 50 300 Two 5.3 One 26.1 III 300 300 100 300 Two 6.1 One 57.4 IV 300 500 100 300 Two 9.3 PS: Population Size; MG: Maximum Generation; EP: Elitism Preservation; SL: Stagnation Limit.

much to the effectiveness and efficiency of the EAS. Currently, we are going to identify the epileptogenic focus, i.e., cortical neuron origin of spikes, from the pseudo channel of phase reversal. In addition to epilepsy, schizophrenia is also under investigation by the EAS. Our experiment shows that GA-SVM modeling (GA-based feature selection and reduction for SVM classification) has impressive performance for complex multi-class classification problems. Due to the natural property of SVM, enforced feature reduction plays an important role to effectively exclude useless or low-impact features. This is especially important for high dimensional classification problems. It is easy to see that GA-SVM modeling requires much more computer power. GA inherently can be decomposed into parallel jobs. MapReduce is a new and simplified parallel programming paradigm supporting builtin load balancing and fault tolerance. Apache Hadoop now has a mature MapReduce implementation that solves the issues of scalability, reliability, and availability greatly, which are crucial to parallel programming in a large distributed system. Our experiments shows that with a good design, GA-SVM modeling can accelerates several times using MapReduce. Since the overhead is still high, we are currently investigating more efficient designs to furthermore speed up the GA-SVM modeling. REFERENCES C.-P. Shen, S.-T. Liu, W. Zhou, F.-S. Lin, Y.-Y. Lam, H.-Y. Sung, W. Chen, J.-W. Lin, M.-J. Chiu, M.-K. Pan, J.-H. Kao, J.-M. Wu, F Lai, “A physiologybased seizure detection system for multichannel EEG,” PLOS ONE, vol. 8, no. 6. 2013, [2] W.-C. Kao, W.-H. Chen, C.-K. Yu, C.-M. Hong, S.-Y. Lin, “Portable real-time homecare system design with digital camera platform,” IEEE Trans. Consumer Electronics, vol. 51, no. 4, pp. 1035-1041, 2005. [3] C.-P. Shen, W.-C. Kao, Y.-Y. Yang, M.-C. Hsu, Y.-T. Wu, F. Lai, “Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines,” Expert Systems with Applications, vol. 39, no. 9, pp. 556-561, 2012. [4] H.M.d. Boer, M. Mula, J.W. Sander, “The global burden and stigma of epilepsy,” Epilepsy & Behavior, vol. 12, no. 4, pp. 540-546, 2008. [5] D.E. Cragar, D.T.R. Berry, T.A. Fakhoury, J.E. Cibula, F.A. Schmitt, “A review of diagnostic techniques in the differential diagnosis of epileptic and nonepileptic seizures,” Neuropsychology Review, vol. 12, no. 1, pp. 31-64, 2002. [6] W. Weng, K. Khorasani, “An adaptive structure neural network with application to EEG automatic seizure detection,” Neural Network, vol. 9, pp. 12231240, 1996. [7] E.D. Übeyli, I. Güler, “Features extracted by eigenvector methods for detecting variability of EEG signals,” Pattern Recognition Letter, vol. 28, nol. 5, pp. 592-603, 2007. [8] S. Ghosh-Dastidar, H. Adeli, “Principle component analysis enhanced cosine radial basis function neural network for robust epilepsy and seizure detection,” IEEE Trans. Biomedical Engineering, vol. 55, no. 2, pp. 512-518, Feb. 2008. [9] A.T. Tzallas, M.G. Tsipouras, D.I. Fotiadis, “Epileptic seizure detection in EEGs using time frequency analysis,” IEEE Trans. Information Technology in Biomedicine, vol. 13, no. 5, pp. 703-710, 2009. [10] M. Lucia, J. Fritschy, P. Dayan, D. Holder, “A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis,” Med. Biol. Eng. Comput., vol. 46, pp. 263-272, 2008. [11] R. Yadav, M. Swamy, R. Agarwal, “Model-based seizure detection for intracranial EEG recordings,” IEEE Trans. Biomed. Eng., vol. 59, pp. 1419-1428, 2012. [12] J.J. Halforda, R.J. Schalkoffb, J. Zhoub, S.R. Benbadisc, W.O. Tatumd, R.P. Turnera, S.R. Sinhae, N.B. Fountainf, A. Araing, P.B. Pritcharda, E. Kutluaya, G. Martza, J.C. Edwardsa, C. Watersh, B.C. Deanh “Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis,” Journal of Neuroscience Methods, vol. 212, no. 2, pp. 308-316, 2013. [1]

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