Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks

Technological Vanguard: the outstanding performance of the LTY-CNN model for the early prediction of epileptic seizures

BACKGROUND

Epilepsy is a common neurological disorder that affects approximately 60 million people worldwide. Characterized by unpredictable neural electrical activity abnormalities, it results in seizures with varying intensity levels. Electroencephalography (EEG), as a crucial technology for monitoring and predicting epileptic seizures, plays an essential role in improving the quality of life for people with epilepsy.

METHOD

This study introduces an innovative deep learning model, a lightweight triscale yielding convolutional neural network" (LTY-CNN), that is specifically designed for EEG signal analysis. The model integrates a parallel convolutional structure with a multihead attention mechanism to capture complex EEG signal features across multiple scales and enhance the efficiency achieved when processing time series data. The lightweight design of the LTY-CNN enables it to maintain high performance in environments with limited computational resources while preserving the interpretability and maintainability of the model.

RESULTS

In tests conducted on the SWEC-ETHZ and CHB-MIT datasets, the LTY-CNN demonstrated outstanding performance. On the SWEC-ETHZ dataset, the LTY-CNN achieved an accuracy of 99.9%, an area under the receiver operating characteristic curve (AUROC) of 0.99, a sensitivity of 99.9%, and a specificity of 98.8%. Furthermore, on the CHB-MIT dataset, it recorded an accuracy of 99%, an AUROC of 0.932, a sensitivity of 99.1%, and a specificity of 93.2%. These results signify the remarkable ability of the LTY-CNN to distinguish between epileptic seizures and nonseizure events. Compared to other existing epilepsy detection classifiers, the LTY-CNN attained higher accuracy and sensitivity.

CONCLUSION

The high accuracy and sensitivity of the LTY-CNN model demonstrate its significant potential for epilepsy management, particularly in terms of predicting and mitigating epileptic seizures. Its value in personalized treatments and widespread clinical applications reflects the broad prospects of deep learning in the health care sector. This also highlights the crucial role of technological innovation in enhancing the quality of life experienced by patients.

Seizure-Cluster-Inception CNN (SciCNN): A Patient-Independent Epilepsy Tracking SoC With 0-Shot-Retraining

Epilepsy tracking System-on-Chips (SoC) usually perform patient-specific classification to deal with the patient-to-patient seizure pattern variation from a surface electroencephalogram (EEG). However, the patient-specific classifier training requires the EEG signals from the target patients a priori, which involves costly and time-consuming hospitalization for the inpatient data collection. To address this issue, this paper presents a patient-independent epilepsy tracking SoC that is trained with pre-existing databases and can be directly deployed to the target patients without collecting their data and performing cumbersome patient-specific training beforehand. The proposed SoC adopts a Seizure-Cluster-Inception Convolutional Neural Network (SciCNN) Neural Processor (SNP) to reduce SRAM access rate by 179.05× with the Kernel-Wise Pipeline (KWP). The 22-Ch. SoC achieves event-based sensitivity of 90.3%/90.4%/83.3% and specificity of 93.6%/95.7%/88.6% on unseen patients from CHB-MIT database/EU database/local hospital patient, respectively.

Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges

Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.