Detection of neonatal seizures through computerized EEG analysis

doi:10.1016/0013-4694(92)90179-L

Electroencephalography and Clinical Neurophysiology

Volume 82, Issue 1, January 1992, Pages 30-37

https://doi.org/10.1016/0013-4694(92)90179-L Get rights and content

Abstract

Neonatal seizures are a symptom of central nervous system disturbances. Neonatal seizures may be identified by direct clinical observation but the majority of electrographic seizures are clinically silent or subtle. Electrographic seizures in the newborn consist of periodic or rhythmic discharges that are distinctively different from normal background cerebral activity. Utilizing these differences, we have developed a technique to identify electrographic seizure activity. In this study, autocorrelation analysis was used to distinguish seizures from background electrocerebral activity. Autocorrelation data were scored to quantify the periodicity using a newly developed scoring system. This method, Scored Autocorrelation Moment (SAM) analysis, successfully distinguished epochs of EEGs with seizures from those without (N = 117 epochs, 58 with seizure and 59 without). SAM analysis showed a sensitivity of 84% and a specificity of 98%. SAM analysis of EEG may provide a method for monitoring electrographic seizures in high-risk newborns.

References (18)

A. Legido et al.
Recent advances in the diagnosis, treatment, and prognosis of neonatal seizures
Pediat. Neurol.
(1988)
J. Rowe et al.
Prognostic value of the electroencephalogram in term and preterm infants following neonatal seizures
Electroenceph. clin. Neurophysiol.
(1985)
M. Andre et al.
Neonatal seizures — recent aspects
Neuropediatrics
(1988)
S. Aziz et al.
Cotside EEG monitoring using computerized spectral analysis
Arch. Dis. Child.
(1986)
A. Babloyantz et al.
Low-dimensional chaos in an instance of epilepsy
G. Calciolari et al.
Seizures in the neonatal intensive care unit of the 1980s: types, etiologies, timing
Clin. Pediat.
(1988)
R. Clancy
Interictal sharp EEG transients in neonatal seizures
J. Child Neurol.
(1989)
R. Coen et al.
Neonatal electroencephalography
G.M. Fenichel
Difficulty in clinical identification of neonatal seizures: an EEG monitor study
Yale J. Biol. Med.
(1987)

There are more references available in the full text version of this article.

Cited by (197)

Neonatal EEG seizure detection using a new signal structural complexity measure based on matching pursuit decomposition with nonstationary dictionary
2022, Computer Methods and Programs in Biomedicine
In newborns, it is often difficult to accurately differentiate between seizure and non-seizure based solely on clinical manifestations. This highlights the importance of electroencephalogram (EEG) in the recognition and management of neonatal seizures. This paper proposes an effective algorithm for the detection of neonatal seizure using multichannel EEG.
Neonatal EEG changes morphology as it alternates between seizure and non-seizure states. A new signal complexity measure based on matching pursuit (MP) decomposition is proposed and used to detect transitions between these two states. The new measure, referred to as weighted structural complexity (WSC), was used for the detection of seizures in 30 newborn EEG records. Multiple IIR filters and an MP-based filter were designed and used to remove artifacts from the EEG data. Geometrical correlation between the EEG data channels was applied to reduce the number of false detections caused by remnant artifacts. The seizure detector's performance was assessed using several epoch-based (e.g., accuracy) and event-based (GDR = good detection rate and FD/h = false detections per hour) metrics.
Compared to the neurologist marking, the proposed detector was able to detect EEG seizures with 94% accuracy, 90.9% GDR, and 0.14 FD/h (95% CI: [0.06, 0.34]).
The high performance of the MP-based detector may have significant implications for the accurate diagnosis of neonatal seizures and the appropriate use of anticonvulsants and ongoing clinical assessment and care of the newborn.
A graph convolutional neural network for the automated detection of seizures in the neonatal EEG
2022, Computer Methods and Programs in Biomedicine
Neonatal seizures are the most common clinical presentation of neurological conditions and can have adverse effects on the neurodevelopment of the neonatal brain. Visual detection of these events from continuous EEG recordings is a laborious and time-consuming task. We propose a novel algorithm for the automated detection of neonatal seizures.
In this study, we propose a novel deep learning model based on Graph Convolutional Neural Networks for the automated detection of neonatal seizures. Unlike other methods exploiting mainly the temporal information contained in EEG signals, our method also considers long-range spatial information, i.e., the interdependencies across EEG signals. The temporal information is embedded as graph signals in the graph representation of the EEG recordings and includes EEG features extracted from the EEG signals in the time and frequency domains. The spatial information is represented as functional connections among the EEG channels (calculated by the phase-locking value and the mean squared coherence) or as maps of Euclidean distances. These different spatial representations were evaluated to assess their efficiency in providing more discriminative features for an effective detection of neonatal seizures. The model performance was assessed on a publicly available dataset of continuous EEG signals recorded from 39 neonates by means of the area under the curve (AUC) and the AUC for specificity values greater than 90% (AUC90).
After applying post-processing, consisting in smoothing the output of the classifiers, the models based on the mean squared coherence, the phase-locking value, and the Euclidean distance respectively reached a median AUC of 99.1% (IQR: 96.8%-99.6%), 99% (IQR: 95.2%-99.7%), and 97.3% (IQR: 86.3%-99.6%), and a median AUC90 of 96%, 95.7%, and 94.9%. These values are superior or comparable to those reached by methods considered as state-of-the-art in this field.
Our results show that the EEG graph representations drawn from functional connectivity measures can effectively leverage interdependencies among EEG signals and lead to reliable detection of neonatal seizures. Furthermore, our model has the advantage of requiring only temporal annotations on seizures for the training phase, making it more appealing for clinical applications.
Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy
2022, Computers in Biology and Medicine
Neonatal seizure detection algorithms (SDA) are approaching the benchmark of human expert annotation. Measures of algorithm generalizability and non-inferiority as well as measures of clinical efficacy are needed to assess the full scope of neonatal SDA performance. We validated our neonatal SDA on an independent data set of 28 neonates. Generalizability was tested by comparing the performance of the original training set (cross-validation) to its performance on the validation set. Non-inferiority was tested by assessing inter-observer agreement between combinations of SDA and two human expert annotations. Clinical efficacy was tested by comparing how the SDA and human experts quantified seizure burden and identified clinically significant periods of seizure activity in the EEG. Algorithm performance was consistent between training and validation sets with no significant worsening in AUC (p > 0.05, n = 28). SDA output was inferior to the annotation of the human expert, however, re-training with an increased diversity of data resulted in non-inferior performance (Δκ = 0.077, 95% CI: −0.002-0.232, n = 18). The SDA assessment of seizure burden had an accuracy ranging from 89 to 93%, and 87% for identifying periods of clinical interest. The proposed SDA is approaching human equivalence and provides a clinically relevant interpretation of the EEG.
Convolutional neural networks ensemble model for neonatal seizure detection
2021, Journal of Neuroscience Methods
Neonatal seizures are a common occurrence in clinical settings, requiring immediate attention and detection. Previous studies have proposed using manual feature extraction coupled with machine learning, or deep learning to classify between seizure and non-seizure states.
In this paper a deep learning based approach is used for neonatal seizure classification using electroencephalogram (EEG) signals. The architecture detects seizure activity in raw EEG signals as opposed to common state-of-art, where manual feature extraction with machine learning algorithms is used. The architecture is a two-dimensional (2D) convolutional neural network (CNN) to classify between seizure/non-seizure states.
The dataset used for this study is annotated by three experts and as such three separate models are trained on individual annotations, resulting in average accuracies (ACC) of 95.6 %, 94.8 % and 90.1 % respectively, and average area under the receiver operating characteristic curve (AUC) of 99.2 %, 98.4 % and 96.7 % respectively. The testing was done using 10-cross fold validation, so that the performance can be an accurate representation of the architectures classification capability in a clinical setting. After training/testing of the three individual models, a final ensemble model is made consisting of the three models. The ensemble model gives an average ACC and AUC of 96.3 % and 99.3 % respectively.
This study outperforms previous studies, with increased ACC and AUC results coupled with use of small time windows (1 s) used for evaluation.
The proposed approach is promising for detecting seizure activity in unseen neonate data in a clinical setting.
A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial
2020, The Lancet Child and Adolescent Health
Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could improve detection. We aimed to assess the diagnostic accuracy of an automated seizure detection algorithm called Algorithm for Neonatal Seizure Recognition (ANSeR).
This multicentre, randomised, two-arm, parallel, controlled trial was done in eight neonatal centres across Ireland, the Netherlands, Sweden, and the UK. Neonates with a corrected gestational age between 36 and 44 weeks with, or at significant risk of, seizures requiring EEG monitoring, received cEEG plus ANSeR linked to the EEG monitor displaying a seizure probability trend in real time (algorithm group) or cEEG monitoring alone (non-algorithm group). The primary outcome was diagnostic accuracy (sensitivity, specificity, and false detection rate) of health-care professionals to identify neonates with electrographic seizures and seizure hours with and without the support of the ANSeR algorithm. Neonates with data on the outcome of interest were included in the analysis. This study is registered with ClinicalTrials.gov, NCT02431780.
Between Feb 13, 2015, and Feb 7, 2017, 132 neonates were randomly assigned to the algorithm group and 132 to the non-algorithm group. Six neonates were excluded (four from the algorithm group and two from the non-algorithm group). Electrographic seizures were present in 32 (25·0%) of 128 neonates in the algorithm group and 38 (29·2%) of 130 neonates in the non-algorithm group. For recognition of neonates with electrographic seizures, sensitivity was 81·3% (95% CI 66·7–93·3) in the algorithm group and 89·5% (78·4–97·5) in the non-algorithm group; specificity was 84·4% (95% CI 76·9–91·0) in the algorithm group and 89·1% (82·5–94·7) in the non-algorithm group; and the false detection rate was 36·6% (95% CI 22·7–52·1) in the algorithm group and 22·7% (11·6–35·9) in the non-algorithm group. We identified 659 h in which seizures occurred (seizure hours): 268 h in the algorithm versus 391 h in the non-algorithm group. The percentage of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (177 [66·0%; 95% CI 53·8–77·3] of 268 h vs 177 [45·3%; 34·5–58·3] of 391 h; difference 20·8% [3·6–37·1]). No significant differences were seen in the percentage of neonates with seizures given at least one inappropriate antiseizure medication (37·5% [95% CI 25·0 to 56·3] vs 31·6% [21·1 to 47·4]; difference 5·9% [–14·0 to 26·3]).
ANSeR, a machine-learning algorithm, is safe and able to accurately detect neonatal seizures. Although the algorithm did not enhance identification of individual neonates with seizures beyond conventional EEG, recognition of seizure hours was improved with use of ANSeR. The benefit might be greater in less experienced centres, but further study is required.
Wellcome Trust, Science Foundation Ireland, and Nihon Kohden.
Acute symptomatic seizures in newborns: a narrative review
2024, Acta Epileptologica

View all citing articles on Scopus

^☆: Sponsored in part by grants from the National Sudden Infant Death Syndrome Foundation, Southern California Chapter.

¹: We would like to thank Ms. Mara Gallo for technical assistance with the EEGs and Ms. Roz Aronzon for editorial assistance. This work was submitted in part for fulfillment of the USD medical degree requirements (A.L.).

View full text

Detection of neonatal seizures through computerized EEG analysis☆

Abstract

Pediat. Neurol.

Electroenceph. clin. Neurophysiol.

Neonatal seizures — recent aspects

Neuropediatrics

Cotside EEG monitoring using computerized spectral analysis

Arch. Dis. Child.

Low-dimensional chaos in an instance of epilepsy

Seizures in the neonatal intensive care unit of the 1980s: types, etiologies, timing

Clin. Pediat.

Interictal sharp EEG transients in neonatal seizures

J. Child Neurol.

Neonatal electroencephalography

Difficulty in clinical identification of neonatal seizures: an EEG monitor study

Yale J. Biol. Med.