A graph convolutional neural network for the automated detection of seizures in the neonatal EEG

Raeisi, Khadijeh; Khazaei, Mohammad Reza; Croce, Pierpaolo; Tamburro, Gabriella; Comani, Silvia; Zappasodi, Filippo

doi:10.1016/j.cmpb.2022.106950

Cited by 38 publications

(20 citation statements)

References 37 publications

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“…Thus, in addition to retaining the advantages of CNN, GCNN can deal with homogeneous and heterogeneous data (Such et al, 2017). In particular, it is capable of extracting features from unstructured data, such as graph representations, by performing convolutions on graph signals (Raeisi et al, 2022). Meanwhile, using graph as the input, GCNN provides a useful tool for processing signals from multiple channels simultaneously.…”

Section: Graph Convolutional Neural Network (Gcnn)mentioning

confidence: 99%

Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages

Zheng,

Zhang,

Song

et al. 2023

Front. Comput. Neurosci.

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IntroductionThe automatic precision detection technology based on electroencephalography (EEG) is essential in epilepsy studies. It can provide objective proof for epilepsy diagnosis, treatment, and evaluation, thus helping doctors improve treatment efficiency. At present, the normal and acute phases of epilepsy can be well identified through EEG analysis, but distinguishing between the normal and chronic phases is still tricky.MethodsIn this paper, five popular complexity indicators of EEG signal, including approximate entropy, sample entropy, permutation entropy, fuzzy entropy and Kolmogorov complexity, are computed from rat hippocampi to characterize the normal, acute, and chronic phases during epileptogenesis. Results of one-way ANOVA and principal component analysis both show that utilizing complexity features, we are able to easily identify differences between normal, acute, and chronic phases. We also propose an innovative framework for epilepsy detection based on graph convolutional neural network (GCNN) using multi-channel EEG complexity as input.ResultsCombining information of five complexity measures at eight channels, our GCNN model demonstrate superior ability in recognizing the normal, acute, and chronic phases. Experiments results show that our GCNN model reached the high prediction accuracy above 98% and F1 score above 97% among these three phases for each individual rat.DiscussionOur research practice based on real data shows that EEG complexity characteristics are of great significance for recognizing different stages of epilepsy.

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Section: Graph Convolutional Neural Network (Gcnn)mentioning

confidence: 99%

Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages

Zheng,

Zhang,

Song

et al. 2023

Front. Comput. Neurosci.

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“…Khadijeh Raeisi et al [ 14 ] presented a DL-based Graph Convolutional Neural Network (GCNN) for automatic seizure detection. Their findings demonstrate that functional connectivity measures derived from EEG graph representations can effectively take advantage of the dependencies between EEG data and result in the accurate diagnosis of newborn seizures.…”

Section: Literature Reviewmentioning

confidence: 99%

A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates

Mumenin

Biswas

Khan

et al. 2023

Sensors

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Electroencephalography (EEG) is increasingly being used in pediatric neurology and provides opportunities to diagnose various brain illnesses more accurately and precisely. It is thought to be one of the most effective tools for identifying newborn seizures, especially in Neonatal Intensive Care Units (NICUs). However, EEG interpretation is time-consuming and requires specialists with extensive training. It can be challenging and time-consuming to distinguish between seizures since they might have a wide range of clinical characteristics and etiologies. Technological advancements such as the Machine Learning (ML) approach for the rapid and automated diagnosis of newborn seizures have increased in recent years. This work proposes a novel optimized ML framework to eradicate the constraints of conventional seizure detection techniques. Moreover, we modified a novel meta-heuristic optimization algorithm (MHOA), named Aquila Optimization (AO), to develop an optimized model to make our proposed framework more efficient and robust. To conduct a comparison-based study, we also examined the performance of our optimized model with that of other classifiers, including the Decision Tree (DT), Random Forest (RF), and Gradient Boosting Classifier (GBC). This framework was validated on a public dataset of Helsinki University Hospital, where EEG signals were collected from 79 neonates. Our proposed model acquired encouraging results showing a 93.38% Accuracy Score, 93.9% Area Under the Curve (AUC), 92.72% F1 score, 65.17% Kappa, 93.38% sensitivity, and 77.52% specificity. Thus, it outperforms most of the present shallow ML architectures by showing improvements in accuracy and AUC scores. We believe that these results indicate a major advance in the detection of newborn seizures, which will benefit the medical community by increasing the reliability of the detection process.

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“…In der Neugeborenenüberwachung z. B. spielt die qEEG nicht-invasiver Langzeitableitungen eine besondere Rolle für die Erkennung epileptischer (subklinischer) Anfälle 10 11 . Die höhere Auflösung digitaler EEGs erlaubt die Erkennung schneller Frequenzen (high Frequency Oscillation = HFO), die z.…”

Section: Epilepsieunclassified

EEG-Update

Noachtar¹,

Rémi²,

Kaufmann³

2022

Klinische Neurophysiologie

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Durch die rasante Entwicklung digitaler Computertechniken und neuer Analysemethoden hat sich ein neuer Ansatz zur Analyse der Hirnströme (quantitatives EEG) ergeben, die in verschiedenen klinischen Bereichen der Neurologie und Psychiatrie bereits Ergebnisse zeigen. Die neuen Möglichkeiten der Analyse des EEG durch Einsatz künstlicher Intelligenz (Deep Learning) und großer Datenmengen (Big Data) sowie telemedizinischer Datenübermittlung und Interaktion wird den Einsatz der Methode vermutlich in den nächsten Jahren erweitern.

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A graph convolutional neural network for the automated detection of seizures in the neonatal EEG

Cited by 38 publications

References 37 publications

Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages

Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages

A Modified Aquila-Based Optimized XGBoost Framework for Detecting Probable Seizure Status in Neonates

EEG-Update

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