We designed this study to investigate the usability of time and frequency measures—signal energy, spectral energy, spectral entropy—and to compare the performances of five different classifiers with the goal of developing a patient-independent seizure detection system. To achieve this objective, we gathered relatively uniform scalp-recorded EEG data from patients with well-characterized TLE and applied time- and frequency-domain measures to detect the electrical onset of seizures. Although most studies have included temporal and extratemporal focal seizures to achieve generality, we restricted our investigation to temporal lobe seizures.7,17,43,45–47 Compared with scalp-recorded EEG, intracranial EEG recordings are largely free of artifacts. However, as intracranial EEG is invasive and expensive, it is undertaken on only in a minority of selected patients with antiepileptic drug-resistant epilepsy, in whom scalp-recorded EEG data have failed to provide localization of the seizure onset. Because of these reasons, studies that have used intracranial EEG are likely to give different results from ours.36,40,44,48
An SVM-based algorithm applied to pediatric patients with a variety of seizure types detected the seizures within 8 ± 3.2 seconds of electrical onset with 94% sensitivity and a false alarm rate of 0.25 hours.31 In another study, recurrent neural network–based system trained using spectral, wavelet, statistical, and complexity measures identified seizures with mean preonset and onset detection latency of −51 seconds (range: −1,140 to −61 seconds) and +4 second (−12 to +51 seconds), respectively.32 A clinical seizure onset detection and warning system with tunable threshold mechanism showed a sensitivity of 76% with median detection latency of 10 seconds.13 An SVM-based detection system using intracranial EEG identified the electrical onset of seizures with median/mean latency (5 seconds/6.9 seconds) and with 97% sensitivity.15 A patient-specific seizure detection algorithm using Combined Seizure Index of wavelet coefficients extracted from multichannel scalp EEG identified 90.5% of the 63 seizures with median detection latency of 7 seconds.46 However, most of the systems proposed in previous studies were patient-dependent and required expert input for channel selection and training data to reconfigure the system for another patient. A generic SVM system using 6 ictal morphologies identified 91 seizures of 57 patients with a median delay of +1.6 seconds (range: −4 to +10 seconds) detection latency and >96% sensitivity. In the system proposed by Meyer et al.,43 each sample of the feature vector was mapped to a probability by comparing the 5 second history with a 25 second baseline history to obtain generalizability across patients.
We aimed to develop a self-sustaining system and therefore normalized each segment using the maximum and minimum values computed from all 29 seizure recordings. The proposed algorithm makes the EEG comparable across patients and does not need any change to use the system for another patient. The features computed for each windowed signal on N − 1 patient recordings were used to train the classifier, and the remaining recording was used to validate the classifier. Each classifier identified the electrical onset of seizure at a different latency compared with manual detection. Comparing the detection latency and performance of the classifiers, the SVM algorithm performed better by detecting the electrical onset well before the clinical onset, with sensitivity and specificity of 80% and 86%, respectively. In 25/29 seizures, the electrical onset was detected by the SVM classifier with median/mean latency of 2 seconds/5.8 seconds (range: −4 to +32 seconds), whereas two seizures are identified at the clinical onset, and one seizure was identified 2 seconds after the clinical onset. The LDA and NB classifiers performed with very low false-positive rates but a high missed seizure rate. The DT and KNN algorithms had good sensitivity but a comparatively low specificity.
We found that for all the 29 seizure recordings, the profile of wavelet energy correlated with spectral energy in 1 to 3, 3 to 6, 6 to 12, 12 to 25, 25 to 50, 50 to 100, and 100 to 200 Hz bands. In a previous study, we observed that the spectral energy in 1 to 3, 3 to 6, 6 to 12, and 12 to 25 Hz increased at the electrical onset of seizure.51 Hence, we combined the lower frequency bands, and the entire signal was divided into 1 to 25, 25 to 100, and 100 to 200 Hz bands for the measurement of spectral energy. The selection of these frequency bands made the analysis and comparisons simple and efficient due to the resulting reduced feature set, yielding better distinguishability of normal and seizure patterns. The spectral entropy in the 3 to 12 Hz band used for seizure electrical onset detection was not analyzed in previous studies with scalp EEG recordings. The spectral entropy in the 1 to 200 Hz frequency band has not shown large changes at the electrical onset of seizures. The largest decrease in entropy was obtained in the 3 to 12 Hz frequency band, and therefore, this was chosen to measure the spectral entropy.
We acknowledge the following limitations of our study. Among our 29 seizure recordings, we could identify the electrical onset in 26 seizures by the classifiers. Among the rest, for two seizures, the clinical onset was detected by all the five classifiers and one seizure was identified by none of the classifiers. Because the algorithm was trained with 29 EEG samples recorded in a routine clinical environment with the presence of movement and myogenic artifacts, the classifiers produced relatively low specificity. Among the 29 seizures, 4 seizures (P4_s1, P13_s1, P19_s1, and P20_s2) were identified with low sensitivity. In all the four seizures, the spectral entropy showed significant variations at the electrical onset of seizure. The significant changes in the profile of signal energy were observed at the clinical onset of P13_s1 and P20_s2 seizures. The seizures P4_s1, P13_s1, and P19_s1 showed no significant variations in the spectral energy in 1 to 25 Hz bands. Because of the lack of significant variations in Elowband and signal energy measures during the seizure activity, the detection of these four seizures resulted in more false normal. Two seizures P15_s1 and P21_s1 were identified with low specificity. Having a good profile of Elowband and signal energy measures, the decrease in the spectral entropy values of normal EEG leads to the misclassification of normal as seizure condition. In particular, the spectral entropy pattern of the normal EEG of P15_s1 overlapped with the seizure region, the seizure detection was resulted in false alarm. However, we admit that the performance of classification was poor in the above-stated scenarios, which emphasizes the need for further research focused to address these issues.
Among the five classifiers, the SVM was found most suitable for the design of an automated system for clinical study. Also, we observed that the performance of the system mainly depended on three factors: (1) normalization scheme, (2) feature extraction, and (3) machine learning algorithm. If the above three parameters are properly selected, the design of a patient-independent system is feasible, and it can be used in a clinical environment. An automated seizure detection system would be helpful for timing ictal SPECT studies as well as for warning patients about upcoming clinical seizures. Such an automated seizure detection system would assist neurologists in visual analysis of long-term EEG recordings and considerably reduce the time spend for reviewing the data.
We investigated the usability of time and frequency measures to improve the performance of a patient-independent system for detection of the electrical onset of seizures in patients with TLE. We selected signal energy, spectral energy in the 1 to 25 Hz band, and spectral entropy in the 3 to 12 Hz band as useful features and trained five classifiers using them. An SVM-based system distinguished normal and seizure patterns with sensitivity and specificity of 80% and 86%, respectively. If the system is trained with data from a larger number of patients, the resulting patient-independent system might be of value in the presurgical evaluation of patients with drug-resistant TLE.
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