An improved BECT spike detection method with functional brain network features based on PLV

Jiang, Lurong; Fan, Qikai; Ren, Juntao; Dong, Fang; Jiang, Tiejia; Liu, Junbiao

doi:10.3389/fnins.2023.1150668

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…Including studies using either or both of these measures, twenty-two relevant studies were found spanning the years 2016–2023. The median ACC is 0.90 (min: 0.71; max: 1.00) and the median AUC is 0.94 (0.76, 1.00) (Abou Jaoude et al, 2020;Antonaides et al, 2017; Cheng et al, 2022; Chung et al, 2023; Faghihpirayesh et al, 2021; Fürbass et al, 2020; Geng et al, 2021; Jeon et al, 2022; Jiang et al, 2023; Johansen et al, 2016; Kural et al, 2022; McDougall et al, 2023; Medvedev et al, 2019; Nejedly et al, 2023; Nhu et al, 2023; Thangavel et al, 2021; Thomas et al, 2020; Thomas et al, 2021; Tjepkema-Cloostermans et al, 2018; Wang et al, 2023; Wei et al, 2021; Zhang et al, 2023). In these studies, there is a large variation in data sizes, in the methods to calculate ACC (e.g., some used balanced ACC) and AUC (e.g., ROC vs. precision and recall), making comparisons difficult.…”

Section: Introductionmentioning

confidence: 99%

Interrater agreement of annotations of epileptiform discharges and its impact on deep learning – A pilot study

Svantesson,

Eklund,

Thordstein

2024

Preprint

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Background: Expert interrater agreement for epileptiform discharges can be moderate. This reasonably will affect the performance when developing classifiers based on annotations performed by experts. In addition, evaluation of classifier performance will be difficult since the ground truth will have a variability. In this pilot study, these aspects were investigated to evaluate the feasibility of conducting a larger study on the subject. Methods: A multi-channel EEG of 78 minutes duration with abundant periodic discharges was independently annotated for epileptiform discharges by two experts. Based on this, several deep learning classifiers were developed which in turn produced new annotations. The agreements of all annotations were evaluated by pairwise comparisons using Cohens kappa and Gwets AC1. A cluster analysis was performed on all periodic discharges using a newly developed version of parametric t-SNE to assess the similarity between annotations. Results: The Cohens kappa values were 0.53 for the experts, 0.52-0.65 when comparing the experts to the classifiers, and 0.67-0.82 for the classifiers. The Gwets AC1 values were 0.92 for the experts, 0.92-0.94 when comparing the experts to the classifiers, and 0.94-0.96 for the classifiers. Although there were differences between all annotations regarding which discharges that had been selected as epileptiform, the selected discharges were mostly similar according to the cluster analysis. Almost all identified epileptiform discharges by the classifiers were also periodic discharges. Conclusions: There was a discrepancy between agreement scores produced by Cohens kappa and Gwets AC1. This was probably due to the skewed prevalence of epileptiform discharges, which only constitutes a small part of the whole EEG. Gwets AC1 is often considered the better option and the results would then indicate an almost perfect agreement. However, this conclusion is questioned when considering the number of differently classified discharges. The difference in annotation between experts affected the learning of the classifiers, but the cluster analysis indicates that all annotations were relatively similar. The difference between experts and classifiers is speculated to be partly due to intrarater variability of the experts, and partly due to underperformance of the classifiers. For a larger study, in addition to using more experts, intrarater agreement should be assessed, the classifiers can be further optimized, and the cluster method hopefully be further improved.

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Section: Introductionmentioning

confidence: 99%

Interrater agreement of annotations of epileptiform discharges and its impact on deep learning – A pilot study

Svantesson,

Eklund,

Thordstein

2024

Preprint

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“…Given that classification and prediction tasks based on EEG signals are popular multivariable time-series tasks, the automatic identification of EP from EEG signals has long been a research topic of interest to clinical physicians. The advent of machine learning in computing has enhanced the automated analysis of EP [8,9], demonstrating promising classification capabilities across time [10][11][12], frequency [13,14], and time-frequency domains [15], as well as measures of complexity and synchrony [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer

Huang,

Zhou,

Chen

et al. 2024

BioMed Eng OnLine

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Background Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease’s burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios. Method To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection. Results The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy. Conclusion The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

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SNSDeepNet: spike and non-spike detection in epilepsy

Kumar,

Kumar Upadhyay,

Kumar Panda

2024

Eng. Res. Express

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Epilepsy, a severe neurological condition is marked by sharp waveforms known as spikes in electroencephalogram (EEG) signals. Prompt detection of these spikes is crucial for reducing accidental injuries and safeguarding the lives of epilepsy patients. This article proposes an innovative deep-learning approach for epileptic spike detection using Spike and Non-spike Deep Convolutional Neural Networks (SNSDeepNet). Our method utilizes CNNs alongside an adaptive Layer-wise Adaptive Moments (LAMB) optimizer to effectively extract relevant features from time-domain (TD) and frequency-domain (FD) representations of spike and non-spike signals. The adaptive LAMB optimizer enhances the training process and accelerates convergence compared to traditional optimizers. The proposed model is evaluated using EEG recordings from three datasets: the Children's Hospital Boston (CHB-MIT) dataset, the Siena Scalp EEG dataset (Physionet Siena Scalp EEG Database), and the Bonn EEG dataset from the University of Bonn. After pre-processing and applying a peak detection algorithm, we extract TD and FD features from the signals. Our model demonstrates impressive performance. The CHB-MIT dataset achieved an average accuracy of 99.69%, sensitivity of 99.68%, F1-score of 99.11%, and a false positive rate (FPR) of 0.02698. For the Siena dataset, the model achieved an accuracy of 99.62%, specificity of 99.04%, sensitivity of 99.93%, F1-score of 99.48%, and an FPR of 0.009208. The Bonn dataset achieved an average accuracy of 94.10%, specificity of 92.39%, sensitivity of 97.35%, and an FPR of 0.0764. These findings underscore the effectiveness of the proposed architecture in accurately identifying epileptic spikes, highlighting its potential to enhance epilepsy diagnosis and treatment.

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An improved BECT spike detection method with functional brain network features based on PLV

Cited by 4 publications

References 40 publications

Interrater agreement of annotations of epileptiform discharges and its impact on deep learning – A pilot study

Interrater agreement of annotations of epileptiform discharges and its impact on deep learning – A pilot study

Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer

SNSDeepNet: spike and non-spike detection in epilepsy

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