Neural Memory Networks for Seizure Type Classification

Ahmedt-Aristizabal, David; Fernando, Tharindu; Denman, Simon; Aburn, Matthew J.; Fookes, Clinton

doi:10.1109/embc44109.2020.9175641

Cited by 42 publications

(30 citation statements)

References 31 publications

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“…Furthermore, we compare our model's performance to results of two other CNNs reported in the literature 35,36 . Following standards established by recent studies on seizure detection 24,28,29 and seizure type classification 33,35,37 , we compare the AUROC scores on the test set between the baselines and our DCRNN models for seizure detection, and weighted F1-scores for seizure type classification. As a fair comparison to the CNN-based ensemble model SeizureNet 35 , we trained and evaluated our DCRNN using a new train and test split: the same 3-fold patient-wise split reported by Asif et al 35 .…”

Section: Graph Neural Network Performancementioning

confidence: 99%

“…Based upon the results so far [32][33][34][35][36] , the seizure type classification problem appears to be substantially more difficult than seizure detection alone. Investigators have used classical machine learning methods 32,33 , deep-learning-based CNNs [34][35][36] , or hybrid CNN-RNN models 37 . In most of these prior studies on seizure detection or seizure type classification, the same patients' EEGs were used for both training and evaluating the algorithms, which does not demonstrate the model's ability to generalize to unseen patients.…”

Section: Introductionmentioning

confidence: 99%

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Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis

Tang¹,

Dunnmon²,

Saab³

et al. 2021

Preprint

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Automated seizure detection and classification from electroencephalography (EEG) can greatly improve the diagnosis and treatment of seizures. While prior studies mainly used convolutional neural networks (CNNs) that assume image-like structure in EEG signals or spectrograms, this modeling choice does not reflect the natural geometry of or connectivity between EEG electrodes. In this study, identifying seizure regions compared to an existing CNN. In summary, our graph-based modeling approach integrates domain knowledge about EEG, sets a new state-of-the-art for seizure detection and classification on a large public dataset (5,499 EEG files), and provides better ability to identify seizure regions.

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Section: Graph Neural Network Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis

Tang¹,

Dunnmon²,

Saab³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Specifically, in [1] the authors couple an NMN together with neural plasticity framework to effectively identify tumors in MRI scans and abnormalities in EEGs. Furthermore, in [51] the same architecture is used to identify different seizure types in EEGs.…”

Section: G D Zmentioning

confidence: 99%

Deep Learning for Medical Anomaly Detection -- A Survey

Fernando¹,

Gammulle²,

Denman³

et al. 2020

Preprint

Self Cite

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Machine learning-based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. Furthermore, we provide a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.

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“…For seizure type classification experiments, we exclude only myoclonic seizures because of the small number of seizures recorded (three seizure events). The seven types of seizure selected for analysis are focal non-specific seizures (FNSZ), generalized non-specific seizures (GNSZ), simple partial seizures (SPSZ), complex partial seizures (CPSZ), absence seizures (ABSZ), tonic seizures (TNSZ), and tonic clonic seizures (TCSZ) [30]. Clinically SPSZ and CPSZ are more specific subclasses of FNSZ, while ABSZ, TNSZ, and TCSZ are more specific subclasses of GNSZ.…”

Section: B Experimental Subjectsmentioning

confidence: 99%

Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Chen

Li³

et al. 2020

IEEE Access

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Important physiological information is hidden in electroencephalography (EEG), which can reflect the human brain's activity. EEG, which is a kind of complicated signal, can be used for epileptic seizure detection and epilepsy diagnosis via machine learning. A large amount of effort, including raw signal preprocessing and data preprocessing for machine learning, is required for constructing high-quality training datasets because the classification performance highly depends on high-quality data. Feature extraction has been widely used in EEG-based early epileptic seizure detection. Due to the complexity of data collection and labeling, some of the training instances are inevitably mislabeled. That means some similar instances have different labels. This is called the issue of class overlap, which leads to a poor class boundary for classification models and makes constructing a high-quality classification model more difficult. However, the previous studies investigating the impact of the class overlap for EEG data is quite limited. Our goal is to investigate the impact of the class overlap on EEG-based early epileptic seizure detection. We propose a special neighborhood cleaning rule (SNCR) to solve the class overlap issue. To alleviate the class overlap issue, we conduct large-scale experiments on two widely-used EEG datasets and compare our proposed SNCR strategy with a state-of-the-art data clean strategy, i.e., the improved k-means clustering cleaning approach (IKMCCA). The experimental results show that the classification model can achieve significantly better performance in terms of AUC, recall, and F1 metrics when using our proposed SNCR strategy. Therefore, for EEG-based early epileptic seizure detection, we recommend researchers to apply the SNCR strategy to mitigate the class overlap issue and use the SNCR strategy to perform data preprocessing in a future related study.

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Neural Memory Networks for Seizure Type Classification

Cited by 42 publications

References 31 publications

Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis

Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis

Deep Learning for Medical Anomaly Detection -- A Survey

Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

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