Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks

Craley, Jeff; Johnson, Emily L.; Jouny, Christophe C.; Venkataraman, Archana

doi:10.1007/978-3-030-32251-9_28

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…However, we performed EEG source imaging with scalp detectors that are more applicable in practice [ 94 , 96 , 97 ]. Scalp EEG can localize the initial epileptic focus with high accuracy, e.g., the LORETA algorithm [ 98 ], exact LORETA kurtosis [ 99 , 100 , 101 ], exact LORETA current source density analysis [ 101 ], and switching Markov model [ 102 ]. A study reported a highly accurate detection of epileptic foci with multielectrode scalp EEGs (128-256 channels).…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

Statsenko,

Babushkin,

Talako

et al. 2023

Biomedicines

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Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95–100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

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Section: Discussionmentioning

confidence: 99%

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

Statsenko,

Babushkin,

Talako

et al. 2023

Biomedicines

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“…Other phenomenon of interest are auras and non-epileptic events, both of which can be detected using a similar training and evaluation strategy. Going one step further, EPViz supports channel-wise predictions, which makes it a natural tool for seizure localization studies [45,46], where the goal is to identify a specific area of onset (e.g., lobe and/ or hemisphere) and track the seizure activity as it propagates from that location.…”

Section: Application Domainsmentioning

confidence: 99%

“…The methods range from traditional machine learning algorithms applied to hand-crafted features, such as wavelet coefficients [5,[15][16][17][18][19][20][21], spectral power [6,7,[22][23][24][25][26], and non-linear measures [5,17,20,[27][28][29][30][31], to end-to-end deep neural networks based on convolutional and recurrent architectures [32][33][34][35][36][37][38][39][40][41][42][43][44]. Recent work in epilepsy has pivoted towards localizing the seizure onset from EEG, which adds a spatial component to the temporal predictions [23,45,46]. On the other hand, BCI systems try to decode user intent based on the EEG signals in order to control the environment [47].…”

Section: Introductionmentioning

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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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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DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data

M. Shama,

Jing,

Venkataraman

2023

Lecture Notes in Computer Science

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Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks

Cited by 3 publications

References 9 publications

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

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

DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data

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