Epileptic Seizure Detection in EEG via Fusion of Multi-View Attention-Gated U-Net Deep Neural Networks

Chatzichristos, Christos; Dan, Jonathan; Narayanan, Abhijith Mundanad; Seeuws, Nick; Vandecasteele, Kaat; Vos, Maarten De; Bertrand, Alexander; Huffel, Sabine Van

doi:10.1109/spmb50085.2020.9353630

Cited by 22 publications

(16 citation statements)

References 26 publications

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“…Moreover, when the 2D models were trained and evaluated on the CHB-MIT dataset, we obtained the worst results thus far, with PRE lower than 5% for all cases. These numerical results are in line with many 2D models reported in the literature 20,28,29,[57][58][59] .…”

Section: Benefits Of Channel-level Seizure Detectionsupporting

confidence: 91%

Multi-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in Scalp and Intracranial EEG

Peh

Thangavel

Yao

et al. 2022

Preprint

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Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last several hours or even days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse morphologies and characteristics across different patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG (sEEG) and intracranial EEG (iEEG). First, we deploy a convolutional neural network (CNN) with transformers (TRF) and belief matching (BM) loss to detect seizures in single-channel EEG segments (channel-level detection). Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments (segment-level detection). At last, we apply postprocessing filters to the segment-level outputs to determine the start and end points of seizures in multi-channel EEGs (EEG-level detection). We introduce the minimum overlap evaluation scoring (MOES) as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) sEEG dataset and evaluated it on five other independent sEEG and iEEG datasets. On the TUH-SZ dataset, the proposed patient-independent seizure detector achieves a sensitivity (SEN), precision (PRE), average and median false positive rate per hour (aFPR/h and mFPR/h), and median offset of 0.772, 0.429, 4.425, 0, and -2.125s, respectively. Across all four adult datasets (excluding neonatal and paediatric datasets), we obtained SEN of 0.617-1.00, PRE of 0.534-1.00, aFPR/h of 0.425-2.002, and mFPR/h of 0-1.003. Meanwhile, on neonatal and paediatric datasets, we obtained SEN of 0.227-0.678, PRE of 0.377-0.818, aFPR/h of 0.253-0.421, and mFPR/h of 0.118-0.223. The proposed seizure detector can reliably detect seizures in adult EEGs (to less extent in neonatal EEGs) and takes less than 15s for a 30 minutes EEG. Hence, this system could potentially aid the clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.

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Section: Benefits Of Channel-level Seizure Detectionsupporting

confidence: 91%

Multi-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in Scalp and Intracranial EEG

Peh

Thangavel

Yao

et al. 2022

Preprint

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“…A recent approach saw the Neureka 2020 Epilepsy Challenge accounting for the number of channels in their scoring formula. Despite this, the winner of this challenge relied on a 16-channel EEG and still only managed to achieve 12.37% sensitivity (with one false alarm per 24 hours) [15]. 16-channels are clearly inappropriate for ambient use, and the state-of-the-art result highlights the challenges associated with developing a highperformance EEG-based seizure detection system using a constrained number of electrodes.…”

Section: Introductionmentioning

confidence: 99%

A multimodal AI system for out-of-distribution generalization of seizure detection

Yang

et al. 2021

Preprint

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Epilepsy is one of the most common severe neurological disorders worldwide. The International League Against Epilepsy (ILAE) define epilepsy as a brain disorder that generates (1) two unprovoked seizures more than 24 hrs apart, or (2) one unprovoked seizure with at least 60% risk of recurrence over the next ten years. Complete remission has been defined as ten years seizure free with the last five years medication free. This requires a cost-effective ambulatory ultra-long term out-patient monitoring solution. The common practice of self-reporting is inaccurate. Applying artificial intelligence (AI) to scalp electroencephalogram (EEG) interpretation is becoming increasingly common, but other data modalities such as electrocardiograms (ECGs) are simpler to collect and often recorded simultaneously with EEG. Both recordings contain biomarkers in the detection of seizures. Here, we propose a state-of-the-art performing AI system that combines EEG and ECG for seizure detection, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific inference-only tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 patients shortlisted by neurologists and 30 randomly selected). Across all datasets, our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for prior state-of-the-art approaches using EEG and ECG alone, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets can improve the accuracy and efficiency of epilepsy diagnoses.

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“…Furthermore, when the amount of the data collected allows, we will study approaches based on Neural Networks which have shown significant success in several seizure detection applications (Chatzichristos et al, 2020;Japaridze et al, 2022), very recently even with the detection of absence seizures with two-channel EEG (Hartmann et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…The feature set used in this study is similar to the one used in (Swinnen et al, 2021). The feature set was chosen among the features used in (Kjaer et al, 2017) and in (Vandecasteele et al, 2020) after performing Random Forest selection (Deviaene et al, 2019). Our final feature set is given in Table 1.…”

Section: Feature Calculationmentioning

confidence: 99%

Multimodal detection of typical absence seizures in home environment with wearable electrodes

Chatzichristos

Swinnen

Macea

et al. 2022

Front. Signal Process.

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Patients with absence epilepsy fail to report almost 90% of their seizures. The clinical gold standard to assess absence seizures is video-electroencephalography (vEEG) recorded in the hospital, an expensive and obtrusive procedure which requires also extended reviewing time. Wearable sensors, which allow the recording of electroencephalography (EEG), accelerometer and gyroscope have been used to monitor epileptic patients in their home environment for the first time. We developed a pipeline for accurate and robust absence seizure detection while reducing the review time of the long recordings. Our results show that multimodal analysis of absence seizures can improve the robustness to false alarms, while retaining a high sensitivity in seizure detection.

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Epileptic Seizure Detection in EEG via Fusion of Multi-View Attention-Gated U-Net Deep Neural Networks

Cited by 22 publications

References 26 publications

Multi-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in Scalp and Intracranial EEG

Multi-center Assessment of CNN-Transformer with Belief Matching Loss for Patient-independent Seizure Detection in Scalp and Intracranial EEG

A multimodal AI system for out-of-distribution generalization of seizure detection

Multimodal detection of typical absence seizures in home environment with wearable electrodes

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