Advances in Automated Neonatal Seizure Detection

Thomas, E. M.; Temko, Andrey; Lightbody, Gordon; Marnane, William P.; Boylan, Geraldine B.

doi:10.1007/978-3-642-11739-8_5

Cited by 5 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three decades ago, Jean Gotman [6,[38][39][40], analyzed and proposed the model for effective usage of EEG signals by applying different computational and statistical techniques for automatic seizure detection. Furthermore, the research has been carried out by different signal processing methods and data science methods to provide better outcomes [27,34,[41][42][43][44][45][46][47].…”

Section: Role Of Data Scientists In Epileptic Seizure Detectionmentioning

confidence: 99%

“…On the other hand, a combination of statistical features such as energy, kurtosis, line length, entropy, skewness, max, standard deviation, and min may produce promising outcomes. Most research [34,46,92,100,109,145] have achieved better results using these features. The novelty of [29,63,104,125] is the selected nine statistical features are able to assist in seizure detection with high accuracy, i.e., 100%.…”

Section: Overall Observation About Capable Classifiers and Statistical Featuresmentioning

confidence: 99%

See 1 more Smart Citation

A review of epileptic seizure detection using machine learning classifiers

et al. 2020

View full text Add to dashboard Buy / Rent full text

Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.

show abstract

Section: Role Of Data Scientists In Epileptic Seizure Detectionmentioning

confidence: 99%

Section: Overall Observation About Capable Classifiers and Statistical Featuresmentioning

confidence: 99%

A review of epileptic seizure detection using machine learning classifiers

et al. 2020

View full text Add to dashboard Buy / Rent full text

show abstract

“…Several computer methods have been developed for the detection of seizures in term infants. [8][9][10][11][12][13][14][15][16][17] Over the past 20 years term EEG seizure detection algorithms (T-SDAs) have passed several stages of development. 18 The early algorithms relied on signal processing routines to extract information from the EEG followed by hand-tuned rule-threshold decision making.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for EEG Seizure Detection in Preterm Infants

O'Shea

Ahmed

Lightbody³

et al. 2021

Int. J. Neur. Syst.

Self Cite

View full text Add to dashboard Buy / Rent full text

EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are reported to have a higher incidence of seizures compared to term infants. Preterm EEG morphology differs from that of term infants, which implies that seizure detection algorithms trained on term EEG may not be appropriate. The task of developing preterm specific algorithms becomes extra-challenging given the limited amount of annotated preterm EEG data available. This paper explores novel deep learning (DL) architectures for the task of neonatal seizure detection in preterm infants. The study tests and compares several approaches to address the problem: training on data from full-term infants; training on data from preterm infants; training on age-specific preterm data and transfer learning. The system performance is assessed on a large database of continuous EEG recordings of 575[Formula: see text]h in duration. It is shown that the accuracy of a validated term-trained EEG seizure detection algorithm, based on a support vector machine classifier, when tested on preterm infants falls well short of the performance achieved for full-term infants. An AUC of 88.3% was obtained when tested on preterm EEG as compared to 96.6% obtained when tested on term EEG. When re-trained on preterm EEG, the performance marginally increases to 89.7%. An alternative DL approach shows a more stable trend when tested on the preterm cohort, starting with an AUC of 93.3% for the term-trained algorithm and reaching 95.0% by transfer learning from the term model using available preterm data. The proposed DL approach avoids time-consuming explicit feature engineering and leverages the existence of the term seizure detection model, resulting in accurate predictions with a minimum amount of annotated preterm data.

show abstract

Advances in Automated Neonatal Seizure Detection

Cited by 5 publications

References 25 publications

A review of epileptic seizure detection using machine learning classifiers

A review of epileptic seizure detection using machine learning classifiers

Deep Learning for EEG Seizure Detection in Preterm Infants

Contact Info

Product

Resources

About