Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel

Ahmed, Rehan; Temko, Andriy; Marnane, William P.; Boylan, Geraldine B.; Lightbody, Gordon

doi:10.1016/j.compbiomed.2017.01.017

Cited by 25 publications

(23 citation statements)

References 37 publications

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“…These features have been used in different methods for neonatal seizure detection. 8,[15][16][17][18]21,50,[57][58][59] More information about the computation of these features can be found in the reference literature. 8,15,21,59 The classifier used in these algorithms is a bagged random forest.…”

Section: Time Domain Line (Curve) Length (1)mentioning

confidence: 99%

“…16 Temko and co-workers employed the same set of features with a support vector machines (SVMs) classifier, using a radial basis function (RBF) and a "Gaussian dynamic time warping" kernel. 17,18 A dictionary was created by Nagaraj et al using an atomic decomposition technique applied on the training data. The complexity of the atoms was then measured and aggregated to define seizures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Ansari

Cherian

Caicedo

et al. 2019

Int. J. Neur. Syst.

170

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Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

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Section: Time Domain Line (Curve) Length (1)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Ansari

Cherian

Caicedo

et al. 2019

Int. J. Neur. Syst.

170

View full text Add to dashboard Cite

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“…The SVM is known as a classical machine learning technique which has shown state of the art performance in EEG recognition. The basic principles of SVM can be found in literature [34], [35]. In this study, we adopt SVM configured with a kernel of radial basis function (RBF) in classification of epileptic seizure.…”

Section: Support Vector Machinementioning

confidence: 99%

Classification Epileptic Seizures in EEG Using Time-Frequency Image and Block Texture Features

Sun

Chen

et al. 2020

IEEE Access

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With the rapid development in technology, computer aided detection or diagnosis has become an indispensable part of the medical industry. Automatic detection of epileptic events is one of the important subjects that have aroused wide interest from more and more investigators. This paper proposes a new model in classification of multi-category electroencephalogram (EEG) signals using time-frequency image and block texture features. The one-dimensional EEG is first mapped to time-frequency domain by means of short-time Fourier transform (STFT), which is adapted to obtain a two-dimensional time-frequency image (2D-TFI). With the idea of multi-scale blocking, the obtained phase images and amplitude images are divided into several sub-blocks corresponding to different frequency ranges and time periods. Then the texture features are calculated to describe the behaviour of EEG signals. Particularly, a novel quadratic feature selection method based on kernel entropy component analysis (KECA) and Kruskal-Wallis test (KW) has been proposed for dimension reduction, by which the features that contained most distinctive information were provided. Eventually, the optimal KECA-based features are fed to support vector machine (SVM) for deciding the class of corresponding EEG. The proposed model is found to achieve at least 99.30% accuracy, 98.0% sensitivity and 100% specificity for each of the eight clinical problems. Our scheme is proven to be effective for seizure detection, which can help doctors optimize the diagnosis workflow, reduce workload and improve detection precision. INDEX TERMS Seizure, EEG, two-dimensional time-frequency image, multi-scale blocking, texture features, kernel entropy component analysis, quadratic feature selection.

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“…It should be noted that, except for exploring the time-varying characteristics in signal level or feature level, temporal information can be explored at the classifier-level. Although this line of thinking is relatively scarce in the field of automatic epileptic seizure detection, [ 60 ] provided an example. A mature technique, dynamic time wrapping (DTW), measuring the similarity between two feature/data sequences with variable length, was used to modify the kernel of support vector machine (SVM).…”

Section: Complexity In Epileptic Seizure Monitoringmentioning

confidence: 99%

Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review

Mei

Xian

Chen

et al. 2018

Sensors

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Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications.

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Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel

Cited by 25 publications

References 37 publications

Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Classification Epileptic Seizures in EEG Using Time-Frequency Image and Block Texture Features

Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review

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