Patient-specific seizure detection in long-term EEG using signal-derived empirical mode decomposition (EMD)-based dictionary approach

Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

doi:10.1088/1741-2552/aaceb1

Cited by 35 publications

(14 citation statements)

References 38 publications

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“…These recordings share 23 common channels for each recording, which are, according to the International 10-20 system of electrode positions and nomenclature, FP1-F7, F7-T7, T7-P7, P7-O1, FP1-F3, F3-C3, C3-P3, P3-O1, FP2-F4, F4-C4, C4-P4, P4-O2, FP2-F8, F8-T8, T8-P8, P8-O2, FZ-CZ, CZ-PZ, P7-T7, T7-FT9, FT9-FT10, FT10-T8, and T8-P8. As is the case with previous studies [e.g., ( 7 , 11 , 28 )], recordings with at least one seizure event are used. The total duration of seizure and non-seizure recordings that have been used is 2.9 and 171 h, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The proposed methodology does not use any pre-processing, nor is limited to particular frequency bands. In this study we used 4-s long segments based on our previous work using the EMD-based dictionary approach ( 28 ), where we had compared 1, 2, 3, and 4 s segments and found 4-s segments to be most suitable. This choice was based on the best results for seizure detection having been obtained using 4 s segments.…”

Section: Methodsmentioning

confidence: 99%

“…A signal derived empirical dictionary learning approach for automatic seizure detection has been presented in Kaleem et al ( 28 ). Based on empirical mode decomposition (EMD), this approach, called the EMD-based dictionary approach, is a methodology inspired by traditional methods of dictionary learning.…”

Section: Introductionmentioning

confidence: 99%

“…The EMD-based dictionary approach learns a dictionary composed for atoms formed using intrinsic mode functions obtained after decomposing a signal using EMD, and can be used for seizure detection using the projection coefficients as features, obtained after projecting the testing signals against the trained EMD-based dictionary. Seizure detection can be performed using a classifier, whereby a support vector machine classifier was used in Kaleem et al ( 28 ).…”

Section: Introductionmentioning

confidence: 99%

“…The methodology proposed in this paper falls in the category of patient-specific seizure detection approaches. For automatic seizure detection, patient-specific approaches are most common [e.g., ( 7 , 13 , 28 , 30 , 31 )]. Patient-specific approaches allow more flexible application to different patients, each with their unique EEG patterns.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Empirical Mode Decomposition, Wavelets, and Different Machine Learning Approaches for Patient-Specific Seizure Detection Using Signal-Derived Empirical Dictionary Approach

Kaleem¹,

Guergachi²,

Krishnan³

2021

Front. Digit. Health

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%