Identifying Pre-ictal Period of Seizure and Better Brain Lobes for Seizure Detection using EEG Biomarkers
01 Jan 2020-Vol. 1451, Iss: 1, pp 012010
About: The article was published on 2020-01-01 and is currently open access. It has received None citations till now. The article focuses on the topics: Ictal & Electroencephalography.
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TL;DR: Patients who have many seizures before therapy or who have an inadequate response to initial treatment with antiepileptic drugs are likely to have refractory epilepsy.
Abstract: BACKGROUND: More than 30 percent of patients with epilepsy have inadequate control of seizures with drug therapy, but why this happens and whether it can be predicted are unknown. We studied the response to antiepileptic drugs in patients with newly diagnosed epilepsy to identify factors associated with subsequent poor control of seizures. METHODS: We prospectively studied 525 patients (age, 9 to 93 years) who were given a diagnosis, treated, and followed up at a single center between 1984 and 1997. Epilepsy was classified as idiopathic (with a presumed genetic basis), symptomatic (resulting from a structural abnormality), or cryptogenic (resulting from an unknown underlying cause). Patients were considered to be seizure-free if they had not had any seizures for at least one year. RESULTS: Among the 525 patients, 333 (63 percent) remained seizure-free during antiepileptic-drug treatment or after treatment was stopped. The prevalence of persistent seizures was higher in patients with symptomatic or cryptogenic epilepsy than in those with idiopathic epilepsy (40 percent vs. 26 percent, P=0.004) and in patients who had had more than 20 seizures before starting treatment than in those who had had fewer (51 percent vs. 29 percent, P<0.001). The seizure-free rate was similar in patients who were treated with a single established drug (67 percent) and patients who were treated with a single new drug (69 percent). Among 470 previously untreated patients, 222 (47 percent) became seizure-free during treatment with their first antiepileptic drug and 67 (14 percent) became seizure-free during treatment with a second or third drug. In 12 patients (3 percent) epilepsy was controlled by treatment with two drugs. Among patients who had no response to the first drug, the percentage who subsequently became seizure-free was smaller (11 percent) when treatment failure was due to lack of efficacy than when it was due to intolerable side effects (41 percent) or an idiosyncratic reaction (55 percent). CONCLUSIONS: Patients who have many seizures before therapy or who have an inadequate response to initial treatment with antiepileptic drugs are likely to have refractory epilepsy.
4,326 citations
TL;DR: Efficient detection of epileptic seizure is achieved when seizure events appear for long duration in hours long EEG recordings and the proposed method develops time–frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.
Abstract: Objective : This paper investigates the multivariate oscillatory nature of electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure detection. Methods : The empirical wavelet transform (EWT) has been explored for the multivariate signals in order to determine the joint instantaneous amplitudes and frequencies in signal adaptive frequency scales. The proposed multivariate extension of EWT has been studied on multivariate multicomponent synthetic signal, as well as on multivariate EEG signals of Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) scalp EEG database. In a moving-window-based analysis, 2-s-duration multivariate EEG signal epochs containing five automatically selected channels have been decomposed and three features have been extracted from each 1-s part of the 2-s-duration joint instantaneous amplitudes of multivariate EEG signals. The extracted features from each oscillatory level have been processed using a proposed feature processing step and joint features have been computed in order to achieve better discrimination of seizure and seizure-free EEG signal epochs. Results : The proposed detection method has been evaluated over 177 h of EEG records using six classifiers. We have achieved average sensitivity, specificity, and accuracy values as 97.91%, 99.57%, and 99.41%, respectively, using tenfold cross-validation method, which are higher than the compared state of art methods studied on this database. Conclusion : Efficient detection of epileptic seizure is achieved when seizure events appear for long duration in hours long EEG recordings. Significance : The proposed method develops time–frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.
291 citations
25 Apr 2017
TL;DR: The proposed algorithm is effective in seizure onset detection with 96% sensitivity, 0.1 per hour median false detection rate, and 1.89 s average detection latency, indicating potential usage in real-time applications.
Abstract: This paper proposes a novel patient-specific real-time automatic epileptic seizure onset detection, using both scalp and intracranial electroencephalogram (EEG). The proposed technique obtains harmonic multiresolution and self-similarity-based fractal features from EEG for robust seizure onset detection. A fast wavelet decomposition method, known as harmonic wavelet packet transform (HWPT), is computed based on Fourier transform to achieve higher frequency resolutions without recursive calculations. Similarly, fractal dimension (FD) estimates are obtained to capture self-similar repetitive patterns in the EEG signal. Both FD and HWPT energy features across all EEG channels at each epoch are organized following the spatial information due to electrode placement on the skull. The final feature vector combines feature configurations of each epoch within the specified moving window to reflect the temporal information of EEG. Finally, relevance vector machine is used to classify the feature vectors due to its efficiency in classifying sparse, yet high-dimensional data sets. The algorithm is evaluated using two publicly available long-term scalp EEG (data set A) and short-term intracranial and scalp EEG (data set B) databases. The proposed algorithm is effective in seizure onset detection with 96% sensitivity, 0.1 per hour median false detection rate, and 1.89 s average detection latency, respectively. Results obtained from analyzing the short-term data offer 99.8% classification accuracy. These results demonstrate that the proposed method is effective with both short- and long-term EEG signal analyzes recorded with either scalp or intracranial modes, respectively. Finally, the use of less computationally intensive feature extraction techniques enables faster seizure onset detection when compared with similar techniques in the literature, indicating potential usage in real-time applications.
167 citations
TL;DR: The application results of the proposed method demonstrated that seizures in ten out of eleven awakening preictal episodes could be predicted prior to the seizure onset, that is, its sensitivity was 91%, and its false positive rate was about 0.7 times per hour.
Abstract: Objective: The present study proposes a new epileptic seizure prediction method through integrating heart rate variability (HRV) analysis and an anomaly monitoring technique. Methods: Because excessive neuronal activities in the preictal period of epilepsy affect the autonomic nervous systems and autonomic nervous function affects HRV, it is assumed that a seizure can be predicted through monitoring HRV. In the proposed method, eight HRV features are monitored for predicting seizures by using multivariate statistical process control, which is a well-known anomaly monitoring method. Results: We applied the proposed method to the clinical data collected from 14 patients. In the collected data, 8 patients had a total of 11 awakening preictal episodes and the total length of interictal episodes was about 57 h. The application results of the proposed method demonstrated that seizures in ten out of eleven awakening preictal episodes could be predicted prior to the seizure onset, that is, its sensitivity was 91%, and its false positive rate was about 0.7 times per hour. Conclusion: This study proposed a new HRV-based epileptic seizure prediction method, and the possibility of realizing an HRV-based epileptic seizure prediction system was shown. Significance: The proposed method can be used in daily life, because the heart rate can be measured easily by using a wearable sensor.
127 citations
01 Jan 2016
TL;DR: The experiments show that the proposed method is less sensitive to artifacts and provides higher prediction accuracy and lower number of false alarms compared to the state-of-the-art methods using intracranial EEG signals in different brain locations of 21 patients from a benchmark data set.
Abstract: Automated seizure prediction has a potential in epilepsy monitoring, diagnosis, and rehabilitation. Electroencephalogram (EEG) is widely used for seizure detection and prediction. This paper proposes a new seizure prediction approach based on spatiotemporal relationship of EEG signals using phase correlation. This measures the relative change between current and reference vectors of EEG signals which can be used to identify preictal/ictal (before the actual seizure onset/ actual seizure period) and interictal (period between adjacent seizures) EEG signals to predict the seizure. The experiments show that the proposed method is less sensitive to artifacts and provides higher prediction accuracy (i.e., 91.95%) and lower number of false alarms compared to the state-of-the-art methods using intracranial EEG signals in different brain locations of 21 patients from a benchmark data set.
87 citations