Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise☆
Introduction
Epilepsy is one of the most common neurological disorder that is characterized by recurrent occurrences of seizures, which are characterized by neuronal discharges with high and/or extended periods of strong spatial phase coherence. Epilepsy affects about 0.5–1.5% of the population of the world and deteriorates the quality of lives of the patients. In the United States alone, 150,000 new instances of epilepsy are being diagnosed every year [1]. The electrical activities in the neurons of the brain are represented by Electroencephalogram (EEG) signals which are obtained from electrodes placed on the scalp. EEG is an inexpensive modality and is commonly used to diagnose patients with neuro-pathologies. It is therefore widely used in epileptic seizure detection and epilepsy diagnosis [2]. Traditionally, epileptic seizure identification is performed from visual inspection of EEG signals by expert neurologists into various categories such as healthy, seizure (ictal), and non-seizure (inter-ictal).
Seizure detection based on visual observation has a surfeit of caveats associated with it. The feasibility of a device that can constantly and reliably monitor epilepsy patients at home, especially when the patients are asleep is reliant on a successful automated seizure detection algorithm. Moreover, seizure detection by visual scoring is onerous, time-consuming, reliant on expensive human resources, and subject to error due to fatigue [3]. Computer-assisted seizure detection algorithm can eliminate the onus of clinicians of visually analyzing a gargantuan bulk of data and expedite diagnosis. Besides, for the feasibility of a closed-loop and implantable neuro-stimulation devices for seizure suppression, developing an automated epileptic seizure detection scheme is a precondition [4], [5]. So it goes without saying that there is a dire need of an automated epileptic seizure detection algorithm.
Researchers have devised various algorithms for seizure recognition from EEG recordings. Prior works in the literature have employed empirical mode decomposition (EMD) [6], [7], [8], Hilbert marginal spectrum analysis [9], dual-tree complex wavelet transformation [10], multivariate autoregressive modeling [11], or extracted various features directly from the EEG signals [12], [13]. Even though EMD is data-adaptive, it suffers from mode mixing problem. Wavelet based seizure detection schemes, on the other hand, are reliant on the choice of a basis function. The best basis function varies across data-sets, making the detection scheme less data-driven.
Guo et al. [12] employed line length feature in conjunction with artificial neural network for epileptic seizure identification. Song et al. [13] implemented optimized sample entropy and extreme learning machine classifier for automated seizure detection. Bajaj et al. [7] computed modulation bandwidth features in the empirical mode decomposition (EMD) domain and used LS-SVM to perform classification. Alam et al. [8] used EMD and artificial neural network to identify epileptic seizure. Both the aforementioned methods suffer from mode-mixing problem. Peker et al. [10] utilized dual-tree complex wavelet transformation and complex-valued neural network to classify epileptic seizure. Wang et al. [11] implemented multivariate autoregressive model, partial directed coherence and svm classifier for automatic seizure detection. Samiee et al. [14] put forward a rational discrete short-time Fourier transform and statistical feature based feature extraction scheme for epileptic classification. Riaz et al. [6] utilized temporal and spectral statistics in the EMD domain and SVM to devise an automated seizure detection scheme. Fu et al. [9] implemented Hilbert marginal spectrum analysis and SVM for seizure detection from EEG data.
A schematic outline of the proposed framework is given in Fig. 1. The computerized epileptic seizure detection algorithm proposed in this work implements complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for feature generation. This is the first implementation of CEEMDAN for epileptic seizure detection to the best of the authors’ knowledge. Ar first, using CEEMDAN, EEG epochs or signal segments are decomposed into intrinsic mode functions. Epileptic seizures are associated with aberrant synchronization of neural rhythms. Since CEEMDAN is ideal to analyze nonlinear and nonstationary signals, we hypothesize that we will observe statistical differences in the distributions of CEEMDAN modes between healthy and epileptic classes. The estimated NIG parameters are then computed from each of the mode functions as we can see from Fig. 1. These parameters are used to construct the train and the test matrices, which are eventually fed into the classifier. The classifier used in the proposed scheme is Adaptive Boosting (AdaBoost). Nevertheless, the performance of NIG parameter based features in the CEEMDAN domain is analyzed for various classification models. The efficacy of our feature extraction scheme is validated by intuitive, graphical, and statistical analysis. 10-fold cross-validation is employed both for making optimal choices of AdaBoost parameters and classification model’s prediction capability assessment. The outcomes of our experiments reveal that the performance of the proposed seizure detection scheme is better or comparable than that of the state-of-the-art works in the literature.
The rest of the paper is organized as follows. Section 2 gives a description of our experimental recordings, presents the proposed seizure detection framework, and demonstrates the effectiveness of NIG parameter based features in the CEEMDAN domain. We then present our experimental results and explicate their significance in Section 3. Finally, Section 4 discusses some of the possible future extensions of this work and brings the article to conclusion.
Section snippets
Experimental data
To conduct our experiments, we utilize university of Bonn’s widely used and publicly available epilepsy data-set in this work [15]. The data-set contains 500 single-channel EEG signal segments. Each of the segment is 23.6-s in duration. The recordings have been grouped into five sets, namely A, B, C, D, and E, each of which contains 100 EEG epochs. Both sets A and B contain surface EEG recordings that have been collected from five healthy subjects in relaxed and awake state. International
Experimental results and discussions
To evaluate the proposed seizure detection scheme, we conducted various experiments. Here we describe our experiments, present the outcomes, and explicate their significance. The performance metrics used in this work are accuracy, sensitivity, specificity, and Cohen’s Kappa co-efficient. Cohen’s kappa measures inter-annotator agreement for categorical items statistically. It has been considered as a more robust measurement of performance than simple percent agreement calculation owing to the
Conclusions and future directions
In this study, the problem of automatic seizure detection from single-channel EEG signals has been tackled by performing NIG modeling of CEEMDAN mode functions. AdaBoost is implemented to perform classification. The efficacy of NIG parameters as features has been demonstrated. The performance of NIG parameters in the CEEMDAN domain has been inspected for various common classification models. Performance comparison with competing methods in the literature suggests that our method gives
CRediT authorship contribution statement
Ahnaf Rashik Hassan: Conceptualization, Methodology, Software, Writing - original draft. Abdulhamit Subasi: Supervision, Writing - review & editing. Yanchun Zhang: Supervision, Writing - review & editing.
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No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.knosys.2019.105333.