A dataset of neonatal EEG recordings with seizure annotations

Stevenson, Nathan J.; Tapani, Karoliina; Lauronen, Leena; Vanhatalo, Sampsa

doi:10.1038/sdata.2019.39

Cited by 118 publications

(118 citation statements)

References 30 publications

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“…It is worth stressing here that the border between the concepts of "state" and "layer" is blurry; viewing a state, or, observations over a time-window as a layer, for example, often facilitates learning tasks, e.g., [9,14]. Nevertheless, a subtle distinction is that a layer is usually well defined and known to the user a-priori, e.g., subjects in a clinical study or frequency bands, whereas states may not be provided beforehand and may need to be learned from the brain-network time-series; an epileptic seizure state, for example, may need to be separated from non-seizure ones through EEG time-series observations [15]. The following discussion is developed having in mind those delicate distinctions between states and layers.…”

Section: A Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

Ye¹,

Slavakis²,

Nakuci³

et al. 2020

Preprint

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This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds according to Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: feature points are first sampled in a non-random way to reveal the underlying geometric information, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information on tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework is validated by extensive numerical tests against several state-of-the-art manifold-learning and brain-network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electro-encephalogram<br> (EEG) data.

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Section: A Problem Statementmentioning

confidence: 99%

“…The open-source EEG data [15] were used. EEGs were selected from an archive of neonatal EEG recordings, requested from the clinical team due to suspicion of seizures.…”

Section: Real Eeg Datamentioning

confidence: 99%

Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

Ye¹,

Slavakis²,

Nakuci³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The open-source EEG data [37] were used. Data comprise neonatal EEG recordings, annotated for seizure and non-seizure states (= classes) by experts.…”

Section: Real Datamentioning

confidence: 99%

Online Classification of Dynamic Multilayer-Network Time Series in Riemannian Manifolds

Ye¹,

Slavakis²,

Nakuci³

et al. 2020

Preprint

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<div>This work exploits Riemannian manifolds to introduce a geometric framework for online state and community classification in dynamic multilayer networks where nodes are annotated with time series. A bottom-up approach is followed, starting from the extraction of Riemannian features from nodal time series, and reaching up to online/sequential classification of features via geodesic distances and angular information in the tangent spaces of a Riemannian manifold. As a case study, features in the Grassmann manifold are generated by fitting a kernel autoregressive-moving-average model to the nodal time series of the multilayer network. The paper highlights also numerical tests on synthetic and real brain-network data, where it is shown that the proposed geometric framework outperforms state-of-the-art deep-learning models in classification accuracy, especially in cases where the number of training data is small with respect to the number of the testing ones.</div><div><br></div><div>-------</div><div><br></div><div>© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.<br></div><div><br></div>

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“…The real data applied is a dataset of neonatal EEG recordings and seizure annotations [ 45 ]. Neonatal epilepsy is a common emergency in neonatal intensive care unit.…”

Section: Performancementioning

confidence: 99%

“…In the data set, each expert commented on an average of 460 epileptic seizures, including 39 neonatal seizures and 22 non-epileptic seizures by consensus. Detailed data set information can be referred to [ 45 ].…”

Section: Performancementioning

confidence: 99%

The Entropy-Based Time Domain Feature Extraction for Online Concept Drift Detection

Ding

Luo

2019

Entropy

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Most of time series deriving from complex systems in real life is non-stationary, where the data distribution would be influenced by various internal/external factors such that the contexts are persistently changing. Therefore, the concept drift detection of time series has practical significance. In this paper, a novel method called online entropy-based time domain feature extraction (ETFE) for concept drift detection is proposed. Firstly, the empirical mode decomposition based on extrema symmetric extension is used to decompose time series, where features in various time scales can be adaptively extracted. Meanwhile, the end point effect caused by traditional empirical mode decomposition can be avoided. Secondly, by using the entropy calculation, the time-domain features are coarse-grained to quantify the structure and complexity of the time series, among which six kinds of entropy are used for discussion. Finally, a statistical process control method based on generalized likelihood ratio is used to monitor the change of the entropy, which can effectively track the mean and amplitude of the time series. Therefore, the early alarm of concept drift can be given. Synthetic data sets and neonatal electroencephalogram (EEG) recordings with seizures annotations data sets are used to validate the effectiveness and accuracy of the proposed method.

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A dataset of neonatal EEG recordings with seizure annotations

Cited by 118 publications

References 30 publications

Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

Online Classification of Dynamic Multilayer-Network Time Series in Riemannian Manifolds

The Entropy-Based Time Domain Feature Extraction for Online Concept Drift Detection

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