Dynamic Mode Decomposition Based Epileptic Seizure Detection from Scalp EEG

Solaija, Muhammad Sohaib J.; Saleem, Sajid; Khurshid, Khurram; Hassan, Syed Ali; Kamboh, Awais Mehmood

doi:10.1109/access.2018.2853125

Cited by 81 publications

(34 citation statements)

References 42 publications

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“…The RUSBoost algorithm is a hybrid approach that uses a combination of sampling and boosting, whereby it performs random undersampling of the majority class before building an ensemble of classifiers ( Seiffert et al., 2009 ). RUSBoost algorithm has been successfully used for sleep apnea detection in polysomnography ( Veauthier et al., 2019 ), and recent studies offer further backing to its use in seizure detection, through direct comparison with black-box classifiers (SVM or KNN) ( Solaija et al., 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings

Marín

Villegas-Martínez

Bermúdez³

et al. 2021

iScience

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Section: Discussionmentioning

confidence: 99%

Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings

Marín

Villegas-Martínez

Bermúdez³

et al. 2021

iScience

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“…where c i is the ith entry of c given in Equation (11). Meanwhile, the DMD mode power is defined by φ i 2 2 [14,20]. Figure 5 illustrates the variation in the spectral information and DMD modes corresponding to signals from different data regions of the EEG recordings, such as interictal in the sleep state, interictal in awake, ictal in sleep, and ictal in awake.…”

Section: Dynamic Mode Decompositionmentioning

confidence: 99%

“…m ∈ W train ∪ W test be a jth data matrix of signal sets in Equation (14). Then, the augmented matrix for the decomposition is represented by:…”

Section: Feature Extractionmentioning

confidence: 99%

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Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

et al. 2020

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In this paper, we propose a new method based on the dynamic mode decomposition (DMD) to find a distinctive contrast between the ictal and interictal patterns in epileptic electroencephalography (EEG) data. The features extracted from the method of DMD clearly capture the phase transition of a specific frequency among the channels corresponding to the ictal state and the channel corresponding to the interictal state, such as direct current shift (DC-shift or ictal slow shifts) and high-frequency oscillation (HFO). By performing classification tests with Electrocorticography (ECoG) recordings of one patient measured at different timings, it is shown that the captured phenomenon is the unique pattern that occurs in the ictal onset zone of the patient. We eventually explain how advantageously the DMD captures some specific characteristics to distinguish the ictal state and the interictal state. The method presented in this study allows simultaneous interpretation of changes in the channel correlation and particular information for activity related to an epileptic seizure so that it can be applied to identification and prediction of the ictal state and analysis of the mechanism on its dynamics.

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“…The EEG signals are amplified during recording, after which different signal processing techniques from both time and frequency domain analysis could be used for extracting the required features, such as wavelet transform [16], empirical mode decomposition [17], [18], and scattering transform [19]- [23], and dynamic mode decomposition [24], [25], etc. For analysis of these signals, different machine learning algorithms like support-vector machine (SVM), principal component analysis (PCA), lineardiscriminant analysis (LDA), neural networks, decision-tree classifiers, and sometimes a combination of these techniques are employed [26]- [31].…”

Section: Introductionmentioning

confidence: 99%

Epileptic Seizure Detection With a Reduced Montage: A Way Forward for Ambulatory EEG Devices

et al. 2020

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Electroencephalogram (EEG) is one of the fundamental tools for analyzing the behavior of brain and particularly helpful for treatment of epilepsy and detection of associated seizures. For longterm recording of EEG signals, current research is heading towards simple, unobtrusive and ambulatory devices with a small number of channels. The primary contribution of this paper is to assess the performance difference between the seizure detection results using features from all channels versus only the channels in/around the temporal region. For this purpose, we develop a supervised seizure detection algorithm that uses time domain features extracted sequentially for every 1-second epoch. By using this algorithm, we obtained sensitivity values of 0.95 and 0.92, specificity values of 0.99 and 0.99 and false positive per hour values as 0.16 and 0.21 for all 23 channels and 10 temporal region channels, respectively. These results show that restricting the EEG analysis to temporal region results only in a graceful and gradual degradation of classifier performance. We conclude that EEG ambulatory devices with a montage local to the temporal region could demonstrate satisfactory performance. This presents a promising way forward for the use of ambulatory devices with compact wearable design.

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Dynamic Mode Decomposition Based Epileptic Seizure Detection from Scalp EEG

Cited by 81 publications

References 42 publications

Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings

Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings

Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

Epileptic Seizure Detection With a Reduced Montage: A Way Forward for Ambulatory EEG Devices

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