Selection of features for patient-independent detection of seizure events using scalp EEG signals

https://doi.org/10.1016/j.compbiomed.2020.103671Get rights and content

Highlights

  • Scalp EEG-based patient independent seizure detection model was investigated.

  • A novel time domain feature extraction algorithm MinMaxHist was proposed for EEG.

  • Other features in time domain, non-linear domain, and domain combination were used.

  • Feature selection algorithms were used to find the subset of features with best accuracy.

  • Different classifiers were also evaluated for their seizure prediction performances.

Abstract

Epilepsy involves brain abnormalities that may cause sudden seizures or other uncontrollable body activities. Epilepsy may have substantial impacts on the patient's quality of life, and its detection heavily relies on tedious and time-consuming manual curation by experienced clinicians, based on EEG signals. Most existing EEG-based seizure detection algorithms are patient-dependent and train a detection model for each patient. A new patient can only be monitored effectively after several episodes of epileptic seizures. This study investigates the patient-independent detection of seizure events using the open dataset CHB-MIT Scalp EEG. First, a novel feature extraction algorithm called MinMaxHist is proposed to measure the topological patterns of the EEG signals. Following this, MinMaxHist and several other feature extraction algorithms are applied to parameterize the EEG signals. Next, a comprehensive series of feature screening and classification optimization experiments are conducted, and finally, an optimized EEG-based seizure detection model is presented that can achieve overall values for accuracy, sensitivity, specificity, Matthews correlation coefficient, and Kappa of 0.8627, 0.8032, 0.9222, 0.7504 and 0.7254, respectively, with only 30 features. The classification accuracy of the method with MinMaxHist features was 0.0464 higher than that without MinMaxHist features. Compared with existing methods, the proposed algorithm achieved higher accuracy and sensitivity, as shown in the experimental results.

Introduction

Chronic lesion epilepsy induces transient brain dysfunction via the abnormal discharge of brain neurons [1,2]. Epilepsy is recognized as the second most common neurological disorder, ranked just after stroke [3]. More than 20% of epilepsy patients suffer from seizures, which are usually refractory to medication [4]. Epileptic seizures occur recurrently and unpredictably, and substantially impact the quality of life of the patient [5,6]. Various modern imaging technologies have been applied to diagnose epilepsy, including magnetic resonance imaging (MRI) [7,8], computed tomography (CT) [9], positron emission tomography (PET) [10,11] and electroencephalogram (EEG) [12].

EEG technology records electrophysiological signals to describe brain activity, and has been widely used to detect epilepsy [5,12]. Although EEG has characteristics of low cost, non-invasiveness, and high signal resolution [13,14], clinical annotation of EEG signals still relies heavily on human screening [15]. The visual inspection of EEG signals is recommended by guidelines, but is tedious and time-consuming [16].

Machine learning algorithms have become the research focus of EEG-based epileptic seizure detection problems [[17], [18], [19]], and several studies have investigated classification based on EEG signals [20,21]. Epileptic seizure detection is formulated as a classification problem of ictal and interictal EEG signals; various feature extraction algorithms have been utilized to generate features from a fixed-length window of EEG signals, including the time domain [22], frequency domain [23], and non-linear characteristics [24,25]. After the feature extraction step, a variety of feature selection [26,27] and classification algorithms [5,28] may be utilized to generate seizure detection models. Deep learning algorithms such as CapsNet and a deep belief network may also be utilized to characterize the phenotypic patterns within EEG signals [29,30].

Most studies calculate variables from EEG signals using feature extraction algorithms, and formulate EEG-based seizure detection as a machine learning problem using these extracted features [19,31,32]. Gabor et al. trained a self-organizing map (SOM) neural network to detect seizures in 24 long-term EEG recordings using time-frequency features, and achieved an accuracy of 90%, thus demonstrating the possibility of automated seizure detection [33]. Time-frequency features are widely used to describe EEG signals due to their non-stationary characteristics. Various forms of wavelet transformations have been utilized to extract time-frequency features from EEG signals. Saab and Gotman used wavelet decomposition to extract features from scalp EEG signals and achieved a sensitivity of 76.0% for the detection of seizures [34]. Discrete wavelet transformation (DWT) is a powerful tool for analyzing time-frequency domain data, and can give a more flexible representation of signals. An extreme learning machine (ELM) was used to train a seizure detection model in Ref. [35]. In addition to time-frequency features, non-linear features can also be extracted from EEG signals. Kannathal et al. investigated the seizure detection problem by integrating four entropy estimators: Shannon spectral entropy, Renyi's entropy, Kolmogorov-Sinai entropy and approximate entropy. Their entropy-based model achieved a seizure classification accuracy of 90% [36]. Another study demonstrated that the non-linear features such as correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), and entropy were accurate when used for seizure detection [37].

This study proposes a novel feature extraction algorithm in the time domain for the patient-independent seizure detection (pidSeizure) problem, and demonstrates that the integrated modeling of various feature sources can achieve satisfactory detection performance. Although much progress has been achieved in the epileptic seizure detection problem, most prior studies have focused on training a separate detection model for each patient. In this study, we hypothesize that a pidSeizure model might better serve epileptic seizure patients. Our experimental data also suggest that the extracted features can be further selected for better classification performance. The final detection model utilizes both feature extraction and feature selection to deliver accurate seizure detection performance.

Section snippets

Problem setting

This study investigated the patient-independent detection problem of epileptic seizures using scalp EEG signals, due to the following two challenges identified in existing studies.

Previous studies have usually used patient-specific modeling [21,24,[38], [39], [40]], i.e., they have trained a separate model for each patient. Although this approach can achieve high detection accuracy, clinicians must wait for several epileptic seizure events before a new patient can be monitored. In addition, the

Extraction of features from raw EEG data

Equal numbers of positive and negative samples were collected from each patient for further analysis, as shown in Table 1. Hence, the number of sample pairs collected from each patient was the minimum of the numbers of positive and negative samples for this patient. A total of 1260 pairs of samples were collected from all patients.

Two families of features were extracted from each channel of a given EEG sample, i.e., the time domain and the nonlinear domain, as shown in Table 2. We calculated 21

Conclusions

Epileptic seizure is a nervous system disorder that does not directly cause lethal damage to patients; however, seizures can substantially impact the quality of life of patients, and can give rise to major mental pressures. The accurate detection of seizures may provide vital information to help both patients and their family members to maintain their daily routines and quality of life.

This study focused on the patient-independent seizure detection problem, which is more challenging to solve

Data availability

The dataset used in this experiment was downloaded on May 4, 2018, and can be obtained from https://www.physionet.org/content/chbmit/1.0.0/.

Declaration of competing interest

None Declared.

Acknowledgements

This work was supported by the Jilin Provincial Key Laboratory of Big Data Intelligent Computing (20180622002JC), the Education Department of Jilin Province (JJKH20180145KJ), and the startup grant of the Jilin University. This work was also partially supported by the Bioknow MedAI Institute (BMCPP-2018-001), the High Performance Computing Center of Jilin University, and by the Fundamental Research Funds for the Central Universities, JLU. The insightful comments from the three anonymous

References (108)

  • Q. Yuan et al.

    Epileptic seizure detection based on imbalanced classification and wavelet packet transform

    Seizure

    (2017)
  • A.J. Gabor et al.

    Automated seizure detection using a self-organizing neural network

    Electroencephalogr. Clin. Neurophysiol.

    (1996)
  • M.E. Saab et al.

    A system to detect the onset of epileptic seizures in scalp EEG, Clinical neurophysiology

    Off. J. Int. Feder. Clin. Neurophysiol.

    (2005)
  • J.L. Song et al.

    Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine

    Neurocomputing

    (2016)
  • N. Kannathal et al.

    Entropies for detection of epilepsy in EEG

    Comput. Methods Progr. Biomed.

    (2005)
  • N. Kannathal et al.

    Characterization of EEG--a comparative study

    Comput. Methods Progr. Biomed.

    (2005)
  • H. Stefan et al.

    Magnetoencephalography adds to the surgical evaluation process

    Epilepsy Behav. : E&B

    (2011)
  • J. Gotman

    Automatic recognition of epileptic seizures in the EEG

    Electroencephalogr. Clin. Neurophysiol.

    (1982)
  • W.T. Blume et al.

    EEG morphology of partial epileptic seizures

    Electroencephalogr. Clin. Neurophysiol.

    (1984)
  • A. Gramfort et al.

    Time-frequency mixed-norm estimates: sparse M/EEG imaging with non-stationary source activations

    Neuroimage

    (2013)
  • L. Gupta et al.

    Non-linear alignment of neural net outputs for partial shape classification

    Pattern Recogn.

    (1991)
  • H. Sharma et al.

    EEG signal based classification before and after combined Yoga and Sudarshan Kriya

    Neurosci. Lett.

    (2019)
  • T. Zoughi et al.

    A wavelet-based estimating depth of anesthesia

    Eng. Appl. Artif. Intell.

    (2012)
  • X. Chen et al.

    The experimental signals analysis for bubbly oil-in-water flow using multi-scale weighted-permutation entropy

    Phys. Stat. Mech. Appl.

    (2015)
  • K. Ivanova et al.

    Application of the detrended fluctuation analysis (DFA) method for describing cloud breaking

    Physica A

    (1999)
  • T. Higuchi

    Approach to an irregular time series on the basis of the fractal theory

    Phys. Nonlinear Phenom.

    (1988)
  • B. Hjorth

    EEG analysis based on time domain properties

    Electroencephalogr. Clin. Neurophysiol.

    (1970)
  • A. Asif et al.

    Human stress classification using EEG signals in response to music tracks

    Comput. Biol. Med.

    (2019)
  • A. Ahmadi et al.

    Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention

    Comput. Methods Progr. Biomed.

    (2019)
  • S. Sahran et al.

    Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading

    Artif. Intell. Med.

    (2018)
  • S.K. Palei et al.

    Logistic regression model for prediction of roof fall risks in bord and pillar workings in coal mines: an approach

    Saf. Sci.

    (2009)
  • L. Rutkowski et al.

    The CART decision tree for mining data streams

    Inf. Sci.

    (2014)
  • Y. Freund et al.

    A decision-theoretic generalization of on-line learning and an application to boosting

    J. Comput. Syst. Sci.

    (1997)
  • M. Maniruzzaman et al.

    Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms

    Comput. Methods Progr. Biomed.

    (2019)
  • C. Jia et al.

    NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC

    J. Theor. Biol.

    (2018)
  • J.H. Friedman

    Stochastic gradient boosting

    Comput. Stat. Data Anal.

    (2002)
  • N.S. Malan et al.

    Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals

    Comput. Biol. Med.

    (2019)
  • M. Rawashdeh et al.

    Breast lesion shape and margin evaluation: BI-RADS based metrics understate radiologists' actual levels of agreement

    Comput. Biol. Med.

    (2018)
  • F. Hasanzadeh et al.

    Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal

    J. Affect. Disord.

    (2019)
  • A. Blumer et al.

    Occam's razor

    Read. Mach. Learn.

    (1987)
  • A.V. Bumanglag et al.

    No latency to dentate granule cell epileptogenesis in experimental temporal lobe epilepsy with hippocampal sclerosis

    Epilepsia

    (2018)
  • J. Corsini et al.

    Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation

    IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng.

    (2006)
  • Y. Zhang et al.

    Integration of 24 feature types to accurately detect and predict seizures using scalp EEG signals

    Sensors

    (2018)
  • G.L. Birbeck et al.

    Seizure reduction and quality of life improvements in people with epilepsy

    Epilepsia

    (2002)
  • X. Zhao et al.

    Role of conventional magnetic resonance imaging in the screening of epilepsy with structural abnormalities: a pictorial essay

    Am. J. Nucl. Med. Mol. Imaging

    (2017)
  • G.P. Winston

    The role of magnetic resonance imaging techniques in the diagnosis, surgical treatment and biological understanding of epilepsy

    Quant. Imag. Med. Surg.

    (2015)
  • P. Fu et al.

    Added value of NeuroGam software analysis in single photon emission computed tomography localization diagnosis of epilepsy in interictal stage

    Med. Sci. Mon. Int. Med. J. Exp. Clin. Res.

    (2018)
  • W.T. Kerr et al.

    Computer-aided diagnosis and localization of lateralized temporal lobe epilepsy using interictal FDG-PET

    Front. Neurol.

    (2013)
  • C. Minardi et al.

    Epilepsy in children: from diagnosis to treatment with focus on emergency

    J. Clin. Med.

    (2019)
  • J. Nizard et al.

    Non-invasive stimulation therapies for the treatment of refractory pain

    Discov. Med.

    (2012)
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