CHB-MIT数据描述.pdf

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Application of Machine Learning To Epileptic Seizure Detection Ali Shoeb ashoeb@mit.edu John Guttag Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139 guttag@mit.edu Abstract seizures. We present and evaluate a machine learn- ing approach to constructing patient-speciﬁc classiﬁers that detect the onset of an epileptic seizure through analysis of the scalp EEG, a non-invasive measure of the brain’s electrical activity. This problem is challenging because the brain’s electrical activity is composed of numerous classes with overlapping character- istics. The key steps involved in realizing a high performance algorithm included shap- ing the problem into an appropriate machine learning framework, and identifying the fea- tures critical to separating seizure from other types of brain activity. When trained on 2 or more seizures per patient and tested on 916 hours of continuous EEG from 24 pa- tients, our algorithm detected 96% of 173 test seizures with a median detection delay of 3 seconds and a median false detection rate of 2 false detections per 24 hour period. We also provide information about how to download the CHB-MIT database, which contains the data used in this study. The most common way to infer the onset of a seizure before it becomes clinically manifest is through anal- ysis of the scalp electroencephalogram (EEG), a non- invasive, multi-channel recording of the brain’s electri- cal activity. The characteristics of EEG vary signiﬁ- cantly across patients. In fact, EEG associated with seizure onset in one patient may closely resemble a be- nign pattern within the EEG of another patient. This cross-patient variability in seizure and non-seizure ac- tivity causes patient non-speciﬁc classiﬁers to exhibit poor accuracy or long delays in declaring the onset of a seizure (Wilson et al., 2004). In some cases, however, patient non-speciﬁc classiﬁers can exhibit impressive performance when restricted to analyzing seizure types that vary little across patients (Meier et al., 2008). This paper is about using machine learning to con- struct patient-speciﬁc detectors capable of detecting seizure onsets quickly and with high accuracy. Unlike previous eﬀorts, which focused on adult EEG, we eval- uate our patient-speciﬁc detectors on pediatric scalp EEG because it exhibits greater variability in seizure and non-seizure activity. Patient-speciﬁc seizure onset detection remains a challenge because 1. Introduction Seizures are transient aberrations in the brain’s elec- trical activity. People with epilepsy, a central ner- vous system disorder, suﬀer from recurrent seizures that occur at unpredictable times and usually without warning. Seizures can result in a lapse of attention or a whole-body convulsion. Frequent seizures increase an individual’s risk of sustaining physical injuries and may even result in death. A device capable of quickly detecting and reacting to a seizure by delivering ther- apy or notifying a caregiver could ease the burden of • Patients with epilepsy have considerable overlap in the EEG associated with seizure and non- seizure states. This forces algorithm designers to confront a steep tradeoﬀ between detector sensi- tivity and speciﬁcity. • The EEG of epilepsy patients constantly tran- sitions between regimes both within the seizure and non-seizure states, and is therefore a non- stationary process. Characterizing the short-term evolution of EEG activity can be critical to infer- ring the brain’s underlying state. Appearing in Proceedings of the 27 th International Confer- ence on Machine Learning, Haifa, Israel, 2010. Copyright 2010 by the author(s)/owner(s). • Numerous medical applications require seizure on- sets to be detected quickly, i.e., a detector needs to ascertain that the brain has transitioned into

Application of Machine Learning To Epileptic Seizure Detection a seizure state using few samples from that state. This forces algorithm designers to confront an- other steep tradeoﬀ, between detector latency and speciﬁcity. • Since seizures are rare events, algorithm designers must craft methods that work with a paucity of seizure training data. Since the goal of seizure detection is to segment the brain’s electrical activity in real-time into seizure and non-seizure periods, one could consider using an on- line, unsupervised time-series segmentation algorithm. Unfortunately, the many regimes of seizure and non- seizure EEG (Agarwal et al., 1998) cause such al- gorithms to return numerous segmentations beyond those demarcating seizure and non-seizure periods. A seizure detector needs to be taught which signal regimes and transitions are relevant. For this reason, we elect to solve the seizure detection problem within a supervised, discriminative framework. Within the discriminative framework we choose to solve a binary classiﬁcation problem, despite the fact that the underlying physiological activity is multi- class. We do so because it is neither easy nor practical for an expert to identify and label the subclasses of the seizure and non-seizure states. In contrast, asking an expert to divide a record of the brain’s electrical ac- tivity into two encompassing classes, seizure and non- seizure, is consistent with standard clinical practice. The key to our classiﬁer’s high accuracy is a completely automated process for constructing a feature vector that uniﬁes in a single feature space the time-evolution of spectral and spatial properties of the brain’s elec- trical activity. Previous patient-speciﬁc algorithms (Qu & Gotman, 1997; Shoeb et al., 2004; Haas et al., 2007) classiﬁed temporal, spectral, and spatial features separately, and required an individual skilled in inter- preting the brain’s electrical activity to specify how such features should be combined. Furthermore, our feature vector can be extended with information ex- tracted from other physiologic signals. This is useful for detecting seizures associated with subtle changes in the EEG, but less subtle inﬂuence on other observable physiologic processes. It is important to distinguish our work from previous investigations concerned with using machine-learning to detect (Grewal & Gotman, 2005; Gardner et al., 2006) seizures using intracranial EEG. Algorithms that process intracranial EEG rely on features that cannot be observed within the scalp EEG because of the spatial averaging eﬀect of the dura and skull. Fur- thermore, intracranial EEG is immune to corruption by artifacts (e.g. scalp muscle contractions) that can mask the onset of seizure activity within the scalp EEG. Finally, in evaluating our approach to seizure detec- tion, we avoided testing methodology that might re- In (Gardner et al., sult in overly optimistic results. 2006), seizures of a single type were used (temporal lobe seizures); data was hand-selected by an expert to be free of artifacts; and only 29 seizure and 41 non- seizure epochs each of 15-minute duration were tested (for a total of 17 hours of test data that included 29 seizures). In contrast, our dataset contains numer- ous seizure types as well as 916 hours and 173 test seizures. In (Mirowski et al., 2009), high speciﬁcity was achieved because test non-seizure feature vectors (33% of data) were not separated in time from train- ing non-seizure feature vectors (66% of data). This creates testing and training sets that are highly corre- lated, since EEG exhibits signiﬁcant temporal correla- tion. To evaluate our detector’s speciﬁcity, we create test sets by removing hour-long records from a cor- pus of EEG data as opposed to removing second-long epochs. In Section 2 we This paper is organized as follows. review properties of the scalp Electroencephalogram (EEG). In Section 3 we discuss both the feature ex- traction and classiﬁcation stages of our binary classi- ﬁer. Our detector’s performance is analyzed in Section 5. Finally, in Section 7, we illustrate how seizure detec- tion can be enhanced through the addition of features extracted from another physiologic process. 2. The Scalp Electroencephalogram EEG measures the electrical activity of the brain us- ing electrodes that are uniformly arrayed on the scalp. An EEG channel is formed by taking the diﬀerence between potentials measured at two electrodes, and captures the summed potential of millions of neurons. Following the onset of most seizures, a set of EEG channels develops rhythmic activity that is typically composed of multiple frequency components. The identity of the EEG channels involved and the struc- ture of the rhythmic activity diﬀers across individuals. For example, Figure 1 and Figure 2 illustrate seizures from diﬀerent patients. Patient A’s seizure in Figure 1 begins following 2994 seconds and is characterized by the appearance of rhythmic activity most prominently on the channels FP2-F4 and T8-P8. Patient B’s seizure in Figure 2 begins at 1723 seconds with a spike followed by a period of low amplitude

Application of Machine Learning To Epileptic Seizure Detection types of rhythmic activity are normal while others are abnormal but not associated with seizures. For exam- ple, the rhythmic activity observed between 2989-2992 seconds in Figure 1 is a normal feature of sleep EEG known as a spindle, and should not be confused with the seizure activity seen later in the same ﬁgure. 2989 2990 2991 2992 2993 2994 2995 2996 2997 Classiﬁcation 3. Seizure Detection as Binary FP1−F7 F7−T7 T7−P7 P7−O1 FP1−F3 F3−C3 C3−P3 P3−O1 FP2−F4 F4−C4 C4−P4 P4−O2 FP2−F8 F8−T8 T8−P8 P8−O2 FZ−CZ CZ−PZ Figure 1. A seizure within the scalp EEG of Patient A. EEG. Next, rhythmic activity develops most promi- nently on the channel F3-C3, and, over the period of a few seconds, increases in amplitude and decreases in frequency. This seizure illustrates the non-stationarity of EEG within the seizure state. FP1−F7 F7−T7 T7−P7 P7−O1 FP1−F3 F3−C3 C3−P3 P3−O1 FP2−F4 F4−C4 C4−P4 P4−O2 FP2−F8 F8−T8 T8−P8 P8−O2 FZ−CZ CZ−PZ 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 Figure 2. A seizure within the scalp EEG of Patient B. Though seizures vary across individuals, the seizures of any given individual exhibit considerable consistency, provided that they emerge from the same brain region. Figure 3 illustrates a second seizure from patient B. Note the similarity in the spatial, spectral, and tem- poral character of this seizure and the seizure shown in Figure 2. FP1−F7 F7−T7 T7−P7 P7−O1 FP1−F3 F3−C3 C3−P3 P3−O1 FP2−F4 F4−C4 C4−P4 P4−O2 FP2−F8 F8−T8 T8−P8 P8−O2 FZ−CZ CZ−PZ 6206 6208 6210 6212 6214 6216 6218 6220 6222 Figure 3. Another seizure in the scalp EEG of Patient B. Not all rhythmic activity observed within the scalp EEG is a reﬂection of an underlying seizure. Certain Our goal is to construct a function f (X ) that maps a feature vector X derived from the EEG onto the labels Y = ±1 depending on whether X is representative of seizure or non-seizure EEG. In the following subsec- tions we discuss how we construct the feature vector X , the discriminant function f (X ), and the training sets. 3.1. Feature Vector Design In section 2, we noted that features important for characterizing EEG include its spectral structure, the channels on which it manifests, and its short-term tem- poral evolution. In the following subsections we illus- trate how these features are extracted and encoded. 3.1.1. Spectral Features The rhythmic activity associated with the onset of a seizure is often composed of multiple frequency com- ponents. For instance, the blue curve in Figure 4 represents the spectrum of the rhythmic activity ob- served on the channel FP2-F4 following the onset of the seizure in Figure 1. The rhythmic activity is com- posed of strong frequency components at 2, 5, and 11 Hz. Considering multiple spectral components is necessary for detecting seizures with high accuracy. The spec- tral content of a seizure epoch may overlap the domi- nant frequency of an epoch of non-seizure activity, but what distinguishes the two is the presence or absence of other spectral components. For example, the red curve in Figure 4 represents the spectrum of a sleep spindle. The two spectra overlap in the 10-12 Hz range, but the seizure spectrum contains stronger low-frequency spectral components. Because of EEG non-stationarity, it is important to extract spectral features from a reasonably small time epoch. Since EEG cannot be segmented into short physiologically relevant units, two second long epochs are commonly used. We extract the spectral structure of a sliding window of length L = 2 seconds by passing it through a ﬁlterbank composed of M = 8 ﬁlters, and

Application of Machine Learning To Epileptic Seizure Detection Seizure Onset Sleep Spindle P8) that are not involved in the sleep spindle event. To automatically capture the spectral and spatial in- formation contained within each two second EEG epoch at time t = T , we concatenate the M = 8 spectral energies extracted from each of N = 18 EEG channels. This process forms a feature vector XT with M × N = 144 elements as shown in the middle portion of Figure 5. 16 18 20 22 24 3.1.3. Time Evolution 200 150 100 50 0 ) B d ( s l e b i c e D −50 0 2 4 6 8 10 Frequency (Hz) 12 14 Figure 4. Superposition of the frequency spectra of a sleep spindle and seizure activity. then measuring the energy falling within the passband of each ﬁlter. For channel k, the energy measured by ﬁlter i is denoted by the feature xi,k as shown on the left side of Figure 5. The ﬁlterbank spans the frequency range 0.5-25 Hz since most seizure and non- seizure EEG activity falls within this range. We characterized the performance of our detector as a function of using a ﬁlterbank composed of M = 2, 4, and 8 equal bandwidth ﬁlters. Increasing the num- ber of ﬁlters did not greatly impact how quickly our detector recognizes the onset of a seizure, but helped to better discriminate between seizure and non-seizure states. Figure 5. Feature extraction stages. 3.1.2. Spatial Features The identity of the EEG channels involved in seizure and non-seizure activity can further diﬀerentiate be- tween these two classes. As an example, consider the sleep spindles between 2989-2992 seconds, and the seizure activity following 2994 seconds in Figure 1. The seizure involves EEG channels (C4-P4 and T8- The feature vector XT does not capture how an epoch relates to those in the recent past. Consequently, XT cannot represent how a seizure emerges from back- ground EEG nor how it evolves. To capture such in- formation, we form a stacked feature vector XT that is the result of concatenating the feature vectors from W contiguous, but non-overlapping 2 second epochs as shown on the right side of Figure 5. Note that encoding the temporal evolution of EEG through the formation of XT is not equivalent to form- ing a single feature vector XT using a longer epoch length. In the former approach, discrete events are preserved and, in the latter, the spectral signatures of discrete events are smeared. Electroencephalographers require an EEG abnormal- ity to persist and evolve for a minimum of 6-10 sec- onds before considering the abnormality a seizure or a component of a seizure. To incorporate this domain knowledge, we set W = 3 so that our classiﬁer consid- ers the evolution of feature vectors over a period of 6 seconds. We also characterized the performance of our detector as W was swept from 2 to 4. As expected, we noted that increasing W decreases the detector’s false detection rate and increases the latency with which it recognizes the onset of a seizure. 3.1.4. Non-EEG Features The earliest measurable sign of some seizures may not be rhythmic EEG activity, but a reﬂection of the phys- ical sequelae of the seizure such as scalp muscle con- tractions or eye ﬂutter. However, these activities are not reliable indicators of a seizure, since they are rou- tinely observed outside the seizure state. To ascertain whether such routine activity is associ- ated with a seizure, information beyond that within the EEG needs to be incorporated into the detection process. The additional information can be derived using a second physiologic signal whose dynamics are inﬂuenced by the presence or absence of a seizure. The second signal and the EEG complement each other if the changes in each signal that suggest the presence

Application of Machine Learning To Epileptic Seizure Detection ) z H 1 1 − + 5 2 ( / y g r e n E 1 0.8 0.6 0.4 0.2 0 0 0.2 5 34 2 1 0.4 Energy (0−16Hz) 0.6 0.8 1 Figure 6. Nonlinear decision boundary separating seizure (red) and non-seizure (blue) feature vectors. of a seizure rarely coincide during non-seizure states, and often coincide at the time of an actual seizure. Patient-speciﬁcity remains essential since the manner in which the EEG and the second signal jointly change during the seizure state is patient-speciﬁc. The electrocardiogram (ECG), a non-invasive measure of the electrical activity of the heart, is a good can- didate for a complementary signal. It is easy to ac- quire, and there is signiﬁcant evidence that seizures for many patients are associated with changes in heart rate (Zijlman et al., 2002). Unfortunately, ECG data was not available for most of the patients we were able to study. However, we did have one patient (patient 24) for whom the EEG and ECG were simultaneously recorded. In Section 7 we describe how features de- rived from that patient’s ECG were concatenated with our EEG features in order to improve detector perfor- mance. 3.2. Feature Vector Classiﬁcation We classify a feature vector as representative of seizure or non-seizure activity using a support-vector machine (SVM). Since the seizure and non-seizure classes are often not linearly separable, we generate non-linear decision boundaries using an RBF kernel. Still the SVM does not always enclose the most important data points. As an example, Figure 6 depicts a nonlinear boundary separating seizure (red) and non-seizure (blue) feature vectors extracted from a single EEG channel. The seizure feature vectors form two clusters: one cluster with fewer feature vectors that lies in close proximity to the non-seizure vectors, and a second cluster with a much greater number of vectors far away from the non-seizure vectors. The seizure vectors closest to the non-seizure vectors are associated with the onset of a seizure, and those further away are associated with Figure 7. Training and missed test seizure within the EEG of Patient 15. later stages. In this example, the earliest seizure vector that is correctly classiﬁed is the third, which means a delay in declaring seizure onset. It is possible to choose SVM parameters that will force the construction of a more eccentric boundary that encloses the early seizure points, but at the expense of enclosing many more non- seizure points. We train the SVM on seizure vectors computed from the ﬁrst S seconds of K seizures, and on non-seizure vectors computed from at least 24 hours of non- seizure EEG. A long period of non-seizure EEG is used to ensure that awake, sleep, abnormal, and artifact- contaminated EEG are included. Selecting the value of S involves a tradeoﬀ. A small S focuses the SVM on seizure onset, but also causes the SVM to fail to detect a seizure whenever the onset changes. Increas- ing S expands the decision boundary and enables the detection of later seizure stages, but increases the de- tector’s false-alarms because the extended boundary encloses more non-seizure vectors. In our experiments we set S = 20 seconds. The impact of the number of training seizures K on performance is discussed in Section 6. The SVM uses a number of hyperparameters, and it is not reasonable to expect clinicians to set them on a patient-speciﬁc basis. In our experiments we used a single ﬁxed set of SVM hyperparameters within the SVMLight software package (Joachims, 1999). These parameters, which were determined through analy- sis of a diﬀerent pediatric EEG dataset (Shoeb et al., 2004), include RBF kernel parameter γ = 0.1, and trade-oﬀ between classiﬁcation margin and error C = 1. 4. Evaluation Methodology 4.1. Scalp EEG Data Set The data set used to evaluate our detector consists of 916 hours of continuous scalp EEG sampled at 256 Hz. The data set was recorded from 23 pediatric patients at Children’s Hospital Boston and one adult patient at Beth Israel Deaconess Medical Center. While the recordings were being made, the patients experienced 173 events that were judged to be clinical seizures by

Application of Machine Learning To Epileptic Seizure Detection tector on NS seizure records and on NN S − 1 non- seizure records. The detector is then run on the with- held non-seizure record. This process is repeated NN S times. For each round, we note the number of false detections. Figure 8. Rhythmic burst and seizure within the EEG of Patient 13. 5. Performance experts. For each clinical seizure, an expert indicated the earliest EEG change associated with the seizure. The data was segmented into one hour long records. Records that do not contain a seizure are called non- seizure records and those that contain one or more seizures are called seizure records. The pediatric EEG data used in this paper is contained within the CHB-MIT database, which can be downloaded from the PhysioNet website: http://physionet.org/physiobank/database/chbmit/ 4.2. Performance Metrics We characterized the performance of our seizure detec- tor in terms of sensitivity, speciﬁcity, and latency. Sen- sitivity refers to the percentage of test seizures iden- tiﬁed. Speciﬁcity refers to the number of times, over the course of 24 hours, that the detector declared the onset of seizure activity in the absence of an actual clinical seizure. Latency refers to the delay between expert-marked seizure onset within the EEG and de- tector declaration of seizure activity. 4.3. Performance Metric Measurement To estimate our detector’s performance on data from a patient, we use a leave-one-record-out cross-validation scheme. We considered an evaluation method based on leaving out epochs rather than hour-long records, but that approach leads to misleadingly good results by including in the training set feature vectors in close temporal proximity to those in the test data. Let NN S denote the number of non-seizure records and NS denote the number of seizure records. To estimate the detector’s latency and sensitivity, we train the detector on NN S non-seizure records from a patient (median NN S = 33) and on NS − 1 seizure records (median NS = 5). The detector is then tasked with detecting the seizure in the withheld seizure record. This process is repeated NS times so that each seizure record is tested. For each round we record whether the test seizure was detected, and if so, with what latency. To estimate the detector’s speciﬁcity, we train the de- 5.1. Sensitivity and Latency Overall, 96% of the 173 test seizures were detected. The mean latency with which the detector declared seizure onset was 4.6 seconds. More speciﬁcally, 50% of the 173 test seizures were detected within 3 seconds, 71% within 5 seconds, and 91% within 10 seconds. Our detector misses or has a large detection latency when a test seizure diﬀers signiﬁcantly in spatial or spectral character from all of the seizures in the train- ing set. For instance, the rhythmic activity within the red box in the top panel of Figure 7 is associated with almost all of the seizures from Patient 15. The bot- tom panel of the ﬁgure highlights one seizure from the same patient that begins with a train of spikes instead of the rhythmic activity. When the detector is trained on all seizures except the one in the bottom panel and tested on the one in the bottom panel, the latency is 55 seconds. 5.2. Speciﬁcity Figure 9 shows the number of false detections declared by the detector per 24 hours for each of the 24 subjects. The median false detection rate is 2 false detections per 24 hour period. Figure 9. Speciﬁcity of our patient-speciﬁc detector. For Patient 13 the algorithm declared 20 false de- tections per 24 hour period. This is attributable to the presence of bursts of activity that resemble the seizure’s onset. The top panel of Figure 8 illustrates a burst of activity that elicits a false detection. The burst closely resembles the seizure activity highlighted within the bottom panel of Figure 8. The main diﬀer- ence between the two is the seizure’s temporal extent.

Application of Machine Learning To Epileptic Seizure Detection Fp1−F3 F3−C3 C3−P3 P3−O1 Fp2−F4 F4−C4 C4−P4 P4−O2 Fp1−F7 F7−T3 T3−T5 T5−O1 Fp2−F8 F8−T4 T4−T6 T6−O2 To improve the speciﬁcity of our detector for Patient 13, one could modify the detector so that it declares a seizure only when activity suspected of being a seizure persists for a duration of time greater than the aver- age length of the rhythmic bursts. The cost of such a modiﬁcation would be an increase in detection latency. 5.3. Rate of Learning Figure 10 illustrates how the average detection latency and miss rate decrease with an increasing number of training seizures. The curve was derived using data from ﬁve randomly selected patients, but is represen- tative of the behavior of the detector. With a single training seizure, the detector has an average latency greater than 7 seconds and misses more than 45% of the test seizures. With three training seizures, the de- tector has an average latency close to 4 seconds and misses less than 5% of test seizures. 1484 1486 1488 1490 1492 1494 1496 1498 1500 1502 1504 1506 1508 1510 140 130 120 110 100 90 80 e t u n M i r e P s t a e B 70 1460 1465 1470 Seizure Onset 1475 1480 1485 1490 1495 1500 Time (Seconds) Figure 11. A seizure with an onset lacking rhythmic activ- ity, but which is accompanied by a heart-rate accleration. 7. Combining Physiologic Signals For Better Seizure Detection In this section, we illustrate how combining EEG and ECG information can improve seizure detection per- formance. 7.1. EEG-ECG Based Seizure Detection To include information within the ECG into the detec- tion process, we augment the feature vector shown in Figure 5 with two ECG features. The features, which are extracted from 6 second epochs, are mean heart rate (X1,ECG), and the diﬀerence between the terminal and initial heart rates within an epoch (X2,ECG). Con- catenating EEG and ECG features enables the SVM to automatically learn the relationship between spec- tral, spatial, and temporal information extracted from the EEG with heart rate and heart rate change infor- mation extracted from the ECG. 7.2. Evaluation 7.2.1. Data Set We recorded 10 seizures and 66 hours of synchronized EEG-ECG from Patient 24. Figure 11 illustrates a typical seizure. The onset of the seizure, at 1486 sec- onds, involves rapid eye-blinking, which manifests in Figure 10. Latency and sensitivity as a function of number of training seizures K 6. Comparison to a Patient Non-Speciﬁc Classiﬁer We compared our performance to the performance of the Reveal algorithm (Wilson et al., 2004). Reveal is an oﬄine, commercially available, patient non-speciﬁc detector that uses machine learning. Reveal relies on a neural network trained on hundreds of seizure and non- seizure epochs from a large number of pediatric and adult patients. When evaluated on the data set dis- cussed in Section 4.1, Reveal detected 61% of seizures with a false detection rate of 33 false detections per 24 hour period. Although trained on a lot more data, Reveal’s sensitivity and speciﬁcity are far worse than the 97% sensitivity and 2 false detections per 24 hour that we report.

Application of Machine Learning To Epileptic Seizure Detection References Agarwal, R., Gotman, J., Flanagan, D., and Rosen- blatt, B. Automatic EEG analysis during long-term monitoring in the ICU. Electroencephalography and Clinical Neurophysiology, 107(1):44–58, July 1998. Gardner, A., Krieger, A., Vachtsevanos, George, and Litt, Brian. One-class novelty detection for seizure analysis from intracranial eeg. Journal of Machine Learning, 7:1025–1044, Dec 2006. Grewal, S. and Gotman, J. An automatic warning sys- tem for epileptic seizures recorded on intracerebral eegs. Clinical Neurophysiology, 116(10):2460–2472, Oct 2005. Haas, S., Frei, M., and Osorio, I. Strategies for adapt- ing automated seizure detection algorithms. Medi- cal Engineering and Physics, 29(8):895–909, Octo- ber 2007. Joachims, T. Making large-scale svm learning prac- tical. Advances in Kernel Methods-Support Vector Learning, 1999. Meier, R, Dittrich, H, Schulze-Bonhage, A, and Aert- sen, A. Detecting epileptic seizures in long-term human EEG: A new approach to automatic online and reat-time detection and classiﬁcation of poly- morphic seizure patterns. Journal of Clinical Neu- rophysiology, 25:119–131, Jun 2008. Mirowski, P., Madhavan, D., LeCun, Y., and Kuzniecky, R. Classiﬁcation of patterns of eeg synchronization for seizure prediction. Electroen- cephalography and Clinical Neurophysiology, 120 (11):1927–1940, Nov 2009. Qu, H. and Gotman, J. A patient-speciﬁc algorithm for the detection of seizure onset in long-term EEG- monitoring: Possible use as a warning device. IEEE Transactions On Biomedical Engineering, 44:115– 122, Feb 1997. Shoeb, A., Edwards, H., Connolly, J., Bourgeois, B., Treves, S., and Guttag, J. Patient-speciﬁc seizure onset detection. Epilepsy and Behavior, 5(4):483– 98, Aug 2004. Wilson, S., Scheuer, M., Emerson, R., and Gabor, A. Seizure detection: Evaluation of the Reveal algo- rithm. Clinical Neurophysiology, 10:2280–2291, Oct 2004. Zijlman, M., Flanagan, D., and Gotman, J. Heart rate changes and ECG abnormalities during epilep- tic seizures: prevalence and deﬁnition of an objec- tive clinical sign. Epilepsia, 43(8):847–854, 2002. Figure 12. Latency of detectors that use feature vectors containing EEG alone, or both EEG and ECG. the EEG as high-amplitude deﬂections on the channel FP1-F7. With the onset of eye-ﬂutter, the patient’s heart rate accelerates as can be seen in the heart rate proﬁle in the bottom panel of Figure 11. Finally, at 1492 seconds, rhythmic activity appears on the chan- nel T4-T6, and the patient’s heart rate remains ele- vated. 7.2.2. Performance We compared the performance of the detector in Fig- ure 5 when feature vectors are assembled using both EEG and ECG, and when they are assembled using EEG alone. Figure 12 illustrates the latency with which each detector declares the ten test seizures. The detector that only used EEG had a mean latency of 4.2 seconds, detected all but seizure 4 (note the miss- ing bar), and declared 9 false detections per 24 hour period. The addition of ECG information improved performance. The detector that combined EEG and ECG had a mean latency of 2.7 seconds, detected all seizures, and declared 5 false detections per 24 hour period. 8. Conclusion We presented and evaluated a method of using SVM’s to construct patient-speciﬁc classiﬁers that use scalp EEG signals to detect the onset of epileptic seizures. We believe that the method outlined in this paper is suitable for clinical use. Using the detector requires no expertise in machine learning, and the labeling of training data can be derived from information that is routinely supplied as part of clinical care. We are now in the process of working with physicians to conduct a prospective study in which detectors built in the way described here are used as part of a closed loop control system for a vagus nerve stimulator.

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