Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping

Khalid, Muhammad Imran; Alotaiby, Turky N.; Aldosari, Saeed A.; Alshebeili, Saleh A.; Alhameed, Majed; Poghosyan, Vahe

doi:10.1109/access.2017.2718044

Cited by 15 publications

(17 citation statements)

References 49 publications

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“…However, the value of h cannot be very small due to the limited number of points, which limits the number of negative eigenvalues that can be computed numerically. The proposed method is compared to the existing spikes detection approaches using MEG signal reported in this work [6]. Table II shows that the SCSA-based features improve the detection sensitivity and gets higher specificity compared to two of the existing methods.…”

Section: Resultsmentioning

confidence: 99%

“…Different methods were proposed for epileptic spikes detection using EEG/MEG signals [3]. However, to the best of the authors knowledge, few methods exists for spike detection using the MEG signal [4], [5], [6]. For example, in [4], an independent component analysis (ICA) method has been proposed with a multi-channel MEG spikes localization method, which decomposes spike-like and background components into separate spatial topographies and associated time series.…”

Section: Introductionmentioning

confidence: 99%

“…The latter is similar to the method in [4] except in the classification stage where the LDA classifier is used for the detection. Moreover, an Amplitude Thresholding and Dynamic Time Warping (AT-DTW) approach has been proposed in [6]. This method uses amplitude thresholding to localize abnormal activities,F to specify the region in the brain where the abnormalities happen and to select the affected channels.…”

Section: Introductionmentioning

confidence: 99%

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Feature Generation and Dimensionality Reduction using the Discrete Spectrum of the Schrödinger Operator for Epileptic Spikes Detection

Chahid

Alotaiby

Alshebeili

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Epilepsy is a neurological disorder classified as the second most serious neurological disease known to humanity, after stroke. Magnetoencephalography (MEG) is performed to localize the epileptogenic zone in the brain. However, the detection of epileptic spikes requires the visual assessment of long MEG recordings. This task is time-consuming and might lead to wrong decisions. Therefore, the introduction of effective machine learning algorithms for the quick and accurate epileptic spikes detection from MEG recordings would improve the clinical diagnosis of the disease. The efficiency of machine learning based algorithms requires a good characterization of the signal by extracting pertinent and discriminative features. In this paper, we propose new sets of features for MEG signals. These features are based on a Semi-Classical Signal Analysis (SCSA) method, which allows a good characterization of peak shaped signals. Moreover, this method improves the spike detection accuracy and reduces the feature vector size. We could achieve up to 92.52% and 89.10% in average sensitivity and specificity, respectively. We use the 5-folds cross-validation applied to a balanced dataset of 3104 frames, extracted from eight healthy and eight epileptic subjects with a frame size of 100 samples with a step size of 2 samples, using Support Vector Machine (SVM) classifier.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature Generation and Dimensionality Reduction using the Discrete Spectrum of the Schrödinger Operator for Epileptic Spikes Detection

Chahid

Alotaiby

Alshebeili

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…From the aspect of physical sense, DDTW is more proper to describe the similarity between two time series. The basic principle of DTW is delivered as follows [21]. Given two time series of length N and M, 1 2 , , , , ,…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

“…The warping path must satisfy the following three constraints [18,20]: The basic principle of DTW is delivered as follows [21]. Given two time series of length N and M,…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

Fault Diagnosis of Mine Shaft Guide Rails Using Vibration Signal Analysis Based on Dynamic Time Warping

Jiang

2018

Symmetry

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Guide rails are amongst the most important components of mine hoist systems, and faults in them must be detected as early as possible to avoid fatal breakdowns in mine production. Presently, guide rail inspection is performed visually in most situations. In this paper, we examine a more efficient approach based on multi-time scale and dynamic time warping (DTW) to diagnose guide rail faults including embossment, bumps, and clearance. Firstly, vibration signals collected from operational conveyance under different fault categories are analyzed and the corresponding characteristic waveforms (CWs) are extracted. Embossment faults are identified with priority according to visible disparities in CW patterns on a large time scale. Then, templates for bump and clearance faults are established through processing the CWs on a small time scale. Subsequently, the distances of DTW (DDTWs) between test samples and the selected templates are calculated. Finally, the remaining fault conditions are classified according to the DDTW results since the same fault category has the smallest distance. Experiments are conducted on a guide rail fault simulator to demonstrate the reliability of the proposed method. The resultant diagnosis accuracies are 100%, 90.40%, and 84.53%, respectively, for embossment, bump, and clearance faults, which indicates that the proposed approach is feasible and effective for diagnosing guide rail faults under variable operating conditions and different fault severities.

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Landscape of epilepsy research: Analysis and future trajectory

Sharma,

Anand,

Pourush

2024

Interdisciplinary Neurosurgery

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Epileptic MEG Spikes Detection Using Amplitude Thresholding and Dynamic Time Warping

Cited by 15 publications

References 49 publications

Feature Generation and Dimensionality Reduction using the Discrete Spectrum of the Schrödinger Operator for Epileptic Spikes Detection

Feature Generation and Dimensionality Reduction using the Discrete Spectrum of the Schrödinger Operator for Epileptic Spikes Detection

Fault Diagnosis of Mine Shaft Guide Rails Using Vibration Signal Analysis Based on Dynamic Time Warping

Landscape of epilepsy research: Analysis and future trajectory

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