Cross-site Epileptic Seizure Detection Using Convolutional Neural Networks

Currey, Danielle; Hsu, David; Ahmed, Raheel; Venkataraman, Archana; Craley, Jeff

doi:10.1109/ciss50987.2021.9400222

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Specifically, EPViz allows the user to generate and overlay predictions on top of the EEG signals, thus providing a mechanism to interpret the model output with respect to the data. EPViz can also generate high-quality images of the predictive modeling outputs to aid in scientific reporting [62]. EPViz is completely open-source and uses Python, which is the fastest-growing programming language for machine learning.…”

Section: Discussionmentioning

confidence: 99%

EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG

et al. 2023

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Scalp Electroencephalography (EEG) is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in computational neuroscience towards spatio-temporal predictive analyses. We introduce a novel open-source viewer, the EEG Prediction Visualizer (EPViz), to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to EEG features, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. These results can be saved as high-resolution images for use in manuscripts and presentations. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic data statistics, and annotation editing. Finally, we have included a built-in EDF anonymization module to facilitate sharing of clinical data. Taken together, EPViz fills a much needed gap in EEG visualization. Our user-friendly interface and rich collection of features may also help to promote collaboration between engineers and clinicians.

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Section: Discussionmentioning

confidence: 99%

EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG

et al. 2023

Self Cite

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“…Currey et al (2021) trained the three CNN models, that is, 1D CNN, 2D CNN‐Spectrogram, and CNN‐combined on CHB MIT and tested it on the University of Wisconsin dataset. It is one of the first research articles to study inter‐hospital generalized performance.…”

Section: Software Developmentsmentioning

confidence: 99%

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

et al. 2023

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Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state-of-the-art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start-ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA

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“…Recent works have also focused on the decomposition of EEG signals to images that act as an input to 2D networks. Currey et al (2021) developed a short-time spectral representation of CHB-MIT EEG scalp database and University of Wisconsin EEG data which acted as their training and testing set respectively. The spectral representation acted as an input to three generic CNN pipelines for cross-dataset analysis for epileptic seizure detection.…”

Section: Rajendramentioning

confidence: 99%

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

Handa,

Gupta,

Gupta

et al. 2023

Expert Systems

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Precise monitoring and diagnosis of epilepsy by manual analysis of EEG signals are challenging due to the low doctor‐to‐patient ratio, and shortage of medical resources. To automate this diagnosis in real‐time, EEG based Brain–Computer Interface (BCI) system with integration of artificial intelligence techniques will prove to be propitious. This work proposes an end‐to‐end, one‐dimensional atrous conv‐net‐based architecture for automatic epilepsy diagnosis using EEG signals with a conceptual framework of the EEG‐BCI system for routine monitoring and clinical use. The proposed architecture has a robust backbone of six blocks of atrous convolutional layers activated with exponential linear unit functions. The six blocks are followed by the addition of a long short‐term memory layer for automatic feature extraction and sequential EEG data analysis. The efficacy of the proposed architecture has been verified on three publicly available EEG datasets using various evaluation metrics, feature maps, test set evaluation, and ablation studies. An average training and validation accuracy of 96.16% and 90.80% has been achieved upon multiple runs for the three datasets. Ablation experiments indicate that the addition of each block contributed to increasing 17%–25% accuracy scores during the classification of epileptic and non‐epileptic EEG signals. The real‐time EEG‐BCI has been analyzed using weight optimization of the proposed architecture through the NVIDIA Tensor RT framework on a 40 GB DGX A100 NVIDIA workstation. The proposed architecture has generalized well in comparison with the existing techniques for the three EEG datasets and achieved a low training and validation loss with optimum evaluation metrics. This makes the proposed architecture suitable for future EEG‐BCI system deployment in the automatic diagnosis of epilepsy.

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Cross-site Epileptic Seizure Detection Using Convolutional Neural Networks

Cited by 4 publications

References 17 publications

EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG

EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

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