Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG

Craley, Jeff; Johnson, Emily L.; Venkataraman, Archana

doi:10.1007/978-3-030-20351-1_22

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…Their sensitivity and specificity were approximately 90% and 95%, respectively. Craley et al [8] exploited a hybrid Probabilistic Graphical Model CNN (PGM-CNN) for seizure tracking. They used an engineered feature called a Coupled Hidden Markov Model (CHMM) that is an extension of conventional Hidden Markov Models where the current state is not only dependent on the states of its own chain but also depends on the neighboring chain at the previous time-step.…”

Section: Introductionmentioning

confidence: 99%

Low Latency Real-Time Seizure Detection Using Transfer Deep Learning

Khalkhali

Shawki

Shah

et al. 2021

2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

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Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be exploited to achieve accurate detection of seizure events. Most popular approaches to seizure detection using deep learning do not jointly model this information or require multiple passes over the signal, which makes the systems inherently non-causal. In this paper, we exploit both simultaneously by converting the multichannel signal to a grayscale image and using transfer learning to achieve high performance. The proposed system is trained end-to-end with only very simple pre-and postprocessing operations which are computationally lightweight and have low latency, making them conducive to clinical applications that require real-time processing. We have achieved a performance of 42.05% sensitivity with 5.78 false alarm per 24 hours on the development dataset of v1.5.2 of the Temple University Hospital Seizure Detection Corpus. On a single core CPU operating at 1.7 GHz, the system runs faster than real-time (0.58 xRT), uses 16 Gbytes of memory, and has a latency of 300 msec.

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Section: Introductionmentioning

confidence: 99%

Low Latency Real-Time Seizure Detection Using Transfer Deep Learning

Khalkhali

Shawki

Shah

et al. 2021

2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

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“…• MLP-XXX: These three methods rely on hand-crafted features extracted channel-wise from the one-second windows as described in [45]. The "time" features consist of sample entropy, signal energy, line length, and largest Lyapunov exponent.…”

Section: Methodsmentioning

confidence: 99%

“…of distances between consecutive time points of the signal; it is a particularly useful metric in EEG analysis [29]. Beyond these time-domain features, EPViz computes the power within the standard EEG frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-14 Hz), beta (14-30 Hz), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). As shown in Fig 3, the user can control the channel and time interval over which the statistics are computed by moving the red rectangle.…”

Section: Plos Onementioning

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%

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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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“…Combining CNNs with conventional feature extraction methods was explored in [ 82 ]; they used the empirical mode decomposition (EMD) method for feature extraction, and CNN was used to acquire high accuracy in the multiclass classification tasks. In [ 83 ], a framework for the diagnosis of epileptic seizures is presented that combined the capability of interpreting probabilistic graphical models (PGMs) with advances in DL. The authors in [ 84 ] submitted a 1D-CNN architecture-defined CNN-BP (standing for CNN bipolar).…”

Section: Epileptic Seizures Detection Based On DL Techniquesmentioning

confidence: 99%

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

289

109

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A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

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Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG

Cited by 13 publications

References 6 publications

Low Latency Real-Time Seizure Detection Using Transfer Deep Learning

Low Latency Real-Time Seizure Detection Using Transfer Deep Learning

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

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

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