Key result
EMD-based dictionary approach achieves ~88% accuracy in automatic patient-specific seizure detection.
Why the study?
Data-driven automatic seizure detection methods that do not rely on hand-crafted features and require only small amounts of historical EEG data for classifier training are limited.
A data-driven methodology utilizing EMD-based dictionaries and SVM classifiers provides robust and computationally efficient patient-specific automatic seizure detection.
Analysis of long-term multichannel EEG signals for automatic seizure detection is an active area of research that has seen application of methods from different domains of signal processing and machine learning. The majority of approaches developed in this context consist of extraction of hand-crafted features that are used to train a classifier for eventual seizure detection. Approaches that are data-driven, do not use hand-crafted features, and use small amounts of patients' historical EEG data for classifier training are few in number. The approach presented in this paper falls in the latter category, and is based on a signal-derived empirical dictionary approach, which utilizes empirical mode decomposition (EMD) and discrete wavelet transform (DWT) based dictionaries learned using a framework inspired by traditional methods of dictionary learning. Three features associated with traditional dictionary learning approaches, namely projection coefficients, coefficient vector and reconstruction error, are extracted from both EMD and DWT based dictionaries for automated seizure detection. This is the first time these features have been applied for automatic seizure detection using an empirical dictionary approach. Small amounts of patients' historical multi-channel EEG data are used for classifier training, and multiple classifiers are used for seizure detection using newer data. In addition, the seizure detection results are validated using 5-fold cross-validation to rule out any bias in the results. The CHB-MIT benchmark database containing long-term EEG recordings of pediatric patients is used for validation of the approach, and seizure detection performance comparable to the state-of-the-art is obtained. Seizure detection is performed using five classifiers, thereby allowing a comparison of the dictionary approaches, features extracted, and classifiers used. The best seizure detection performance is obtained using EMD based dictionary and reconstruction error feature and support vector machine classifier, with accuracy, sensitivity and specificity values of 88.2, 90.3, and 88.1%, respectively. Comparison is also made with other recent studies using the same database. The methodology presented in this paper is shown to be computationally efficient and robust for patient-specific automatic seizure detection. A data-driven methodology utilizing a small amount of patients' historical data is hence demonstrated as a practical solution for automatic seizure detection.
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Kaleem et al. (2021) studied Epilepsy (intractable seizures) (n=23). Empirical mode decomposition (EMD)-based dictionary approach with SVM classifier vs. Discrete wavelet transform (DWT)-based dictionary approach and other classifiers was evaluated on Seizure detection accuracy. An empirical mode decomposition (EMD)-based dictionary approach using the reconstruction error feature and a support vector machine classifier achieved automatic patient-specific seizure detection with 88.2% accuracy, 90.3% sensitivity, and 88.1% specificity.
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