Crise d'épilepsie

Résumé

Une crise d'épilepsie ou crise épileptique est caractérisée par divers symptômes dus à une activité neuronale anormalement excessive ou synchrone dans le cerveau. Les effets extérieurs varient de mouvements de tremblement incontrôlés impliquant une grande partie du corps avec perte de connaissance (crise d'épilepsie généralisée tonico-clonique), de mouvements de tremblement n'impliquant qu'une partie du corps avec des niveaux de conscience variables (crise épileptique focale) ou à une perte de conscience momentanée subtile (absence épileptique). Durée La plupart du temps, ces épisodes durent moins de deux minutes et le retour à la normale prend un certain temps. Une crise qui dure plus longtemps qu’une brève période est une urgence médicale. Causes Les crises peuvent être provoquées ou non provoquées. Les crises provoquées sont dues à un événement temporaire tel que l'hypoglycémie, le sevrage alcoolique, l'hyponatrémie, la fièvre, une infection cérébrale o

Source officielle

https://fr.wikipedia.org/wiki/Crise_d'%C3%A9pilepsie

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The course covers the fundaments of bioelectronics and integrated microelectronics for biomedical and implantable systems. Issues and trade-offs at the circuit and systems levels of invasive microelectronic systems as well as their eluding designs, methods and classical implementations are discussed

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Séances de cours associées

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Epileptic Seizure Detection With Patient-Specific Feature and Channel Selection for Low-power Applications

Keyvan Farhang Razi, Alexandre Schmid

An accurate epileptic seizure detector using intracranial electroencephalography (iEEG) recordings, suitable for low-power wearable/implantable applications, is presented. Eleven time-domain features with low hardware complexity are employed in the feature pool of the seizure detector. A novel two-step feature ranking algorithm based on maximum discrimination minimum redundancy (MDMR) is proposed to identify the most discriminating features in a patient-specific manner during a training phase. Subsequently, the top-ranked features are extracted in a two-stage energy-efficient architecture in which the second stage is activated by a controller to avoid unnecessary energy consumption imposed by multiple feature extraction during the long period of non-seizure states. The effective number of features which are continuously extracted is reduced in this work. Moreover, a patient-specific data reduction technique which selects the most informative and discriminating iEEG channels is also proposed. The presented channel selection technique reaches 68% data reduction on average for the tested patients and reduces the computational complexity. The proposed algorithm is implemented on Cyclone V FPGA of Terasic DE10-standard board. It is tested on twelve patients with short-term and long-term seizures from the Bern Inselspital dataset. The FPGA implementation results reveal an excellent sensitivity of 100% for all patients and remarkable specificity and detection delay improvement compared to the state-of-the-art. The average dynamic power consumption is 0.49 mW which is in the acceptable range for low-power wearable/implantable applications. In addition, a new figure-of-merit (FoM_SD) is defined to consider the major parameters of the seizure detectors. The outstanding FoM_SD of this work is 0.464 which outperforms the state-of-the-art.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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Interpreting deep learning models for epileptic seizure detection on EEG signals

David Atienza Alonso, Leila Cammoun, Valentin Alexandre Guy Gabeff, Tomas Teijeiro Campo, Marina Zapater Sancho

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neural network models. We have tackled this issue by developing interpretable DL models in the context of online detection of epileptic seizure, based on EEG signal. This has conditioned the preparation of the input signals, the network architecture, and the post-processing of the output in line with the domain knowledge. Specifically, we focused the discussion on three main aspects: 1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; 2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and 3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method. Results show that the kernel size in the first layer determines the interpretability of the extracted features and the sensitivity of the trained models, even though the final performance is very similar after post-processing. Also, we found that amplitude is the main feature leading to an ictal prediction, suggesting that a larger patient population would be required to learn more complex frequency patterns. Still, our methodology was successfully able to generalize patient inter-variability for the majority of the studied population with a classification F1-score of 0.873 and detecting 90% of the seizures.

2021

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, ,

Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epileptic seizure detection. Unfortunately, due to differences in data preparation, segmentation, encoding strategies, and performance metrics, results are hard to compare, which makes building upon that knowledge difficult. Thus, the main goal of this work is to perform a systematic assessment of the HD computing framework for the detection of epileptic seizures, comparing different feature approaches mapped to HD vectors. More precisely, we test two previously implemented features as well as several novel approaches with HD computing on epileptic seizure detection. We evaluate them in a comparable way, i.e., with the same preprocessing setup, and with the identical performance measures. We use two different datasets in order to assess the generalizability of our conclusions. The systematic assessment involved three primary aspects relevant for potential wearable implementations: 1) detection performance, 2) memory requirements, and 3) computational complexity. Our analysis shows a significant difference in detection performance between approaches, but also that the ones with the highest performance might not be ideal for wearable applications due to their high memory or computational requirements. Furthermore, we evaluate a post-processing strategy to adjust the predictions to the dynamics of epileptic seizures, showing that performance is significantly improved in all the approaches and also that after post-processing, differences in performance are much smaller between approaches.

2021

Chargement

Interpreting deep learning models for epileptic seizure detection on EEG signals

David Atienza Alonso, Leila Cammoun, Valentin Alexandre Guy Gabeff, Tomas Teijeiro Campo, Marina Zapater Sancho

2021

Epileptic Seizure Detection With Patient-Specific Feature and Channel Selection for Low-power Applications

Keyvan Farhang Razi, Alexandre Schmid

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022