Optimization of epilepsy detection method based on dynamic EEG channel screening

A systematic review of artificial intelligence techniques based on electroencephalography analysis in the diagnosis of epilepsy disorders: A clinical perspective

In recent years, Artificial Intelligence (AI), with a specific emphasis on attention mechanisms instead of conventional Deep Learning (DL) or Machine Learning (ML), has demonstrated significant applicability across diverse medical domains. This paper redirects its focus from general brain mapping techniques to specifically investigate the impact of AI in the field of epilepsy diagnosis, concentrating exclusively on Electroencephalography (EEG) data. While earlier studies have predominantly centered on the automatic identification and prediction of seizures using EEG records, an emerging body of research delves into the potential of AI techniques to enhance the analysis of EEG data. This systematic review offers a comprehensive overview, commencing with a concise theoretical exposition on Artificial Neural Networks (ANNs) and attention mechanisms. Subsequent sections explore the applications of AI in EEG analysis for epilepsy, covering aspects such as diagnosis, lateralization, automated lesion detection, presurgical evaluation, and the prediction of postsurgical outcomes. The discussion not only highlights the promising aspects of AI in refining clinical practices but also underscores its potential in tailoring individualized treatments for epilepsy, considering it as a network disorder. The paper concludes by addressing limitations, challenges, and proposing future directions for the application of AI in epilepsy research. While acknowledging the transformative potential of this approach, it emphasizes the necessity for greater multicenter collaboration to amass high-quality data and ensure the open accessibility of developed codes and tools. Moreover, the application of AI models in Computer-Aided Diagnosis (CAD) has exhibited significant promise in enhancing the accuracy and efficiency of epilepsy and seizure diagnosis. This integration of advanced technologies contributes to the development of robust tools for clinical decision-making and underscores the potential for AI-driven solutions in neurological healthcare.

Unlocking the potential of EEG in Alzheimer's disease research: Current status and pathways to precision detection

Alzheimer's disease (AD) affects millions of individuals worldwide and is considered a serious global health issue due to its gradual neuro-degenerative effects on cognitive abilities such as memory, thinking, and behavior. There is no cure for this disease but early detection along with a supportive care plan may aid in improving the quality of life for patients. Automated detection of AD is challenging because its symptoms vary in patients due to genetic, environmental, or other co-existing health conditions. In recent years, multiple researchers have proposed automated detection methods for AD using MRI and fMRI. These approaches are expensive, have poor temporal resolution, do not offer real-time insights, and have not proven to be very accurate. In contrast, only a limited number of studies have explored the potential of Electroencephalogram (EEG) signals for AD detection. In contrast, Electroencephalogram (EEG) signals present a cost-effective, non-invasive, and high-temporal-resolution alternative for AD detection. Despite their potential, the application of EEG signals in AD research remains under-explored. This study reviews publicly available EEG datasets, the variety of machine learning models developed for automated AD detection, and the performance metrics achieved by these methods. It provides a critical analysis of existing approaches, highlights challenges, and identifies key areas requiring further investigation. Key findings include a detailed evaluation of current methodologies, prevailing trends, and potential gaps in the field. What sets this work apart is its in-depth analysis of EEG signals for Alzheimer's Disease detection, providing a stronger and more reliable foundation for understanding the potential role of EEG in this area.

Design of a hybrid AI network circuit for epilepsy detection with 97.5% accuracy and low cost-latency

Epilepsy detection using artificial intelligence (AI) networks has gained significant attention. However, existing methods face challenges in accuracy, computational cost, and speed. CNN excel in feature extraction but suffer from high computational latency and power consumption, while SVM rely heavily on feature quality and expensive kernel computations, limiting real-time performance. Additionally, most CNN-SVM hybrid model lack hardware optimization, leading to inefficient implementations with poor accuracy-latency trade-offs. To address these issues, this paper designs a hybrid AI network-based method for epilepsy detection using electroencephalography (EEG) signals. First, a hybrid AI network was constructed using three convolutional layers, three pooling layers, and a Gaussian kernel SVM to achieve EEG epilepsy detection. Then, the design of the multiply-accumulate circuit was completed using a parallel-style row computation method, and a pipelined convolutional computation circuit was used to accelerate the convolutional computation and reduce the computational overhead and delay. Finally, a single-precision floating-point exponential and logarithmic computation circuit was designed to improve the speed and accuracy of data computation. The digital back-end of the hardware circuit was realized under the TSMC 65 nm process. Experimental results show that the circuit occupies an area of 3.20 mm2, consumes 4.28 mW of power, operates at a frequency of 10 MHz, and has an epilepsy detection latency of 0.008 s, which represents a 32% reduction in latency compared to those reported in the relevant literature. The database test results showed an epilepsy detection accuracy of 97.5%, a sensitivity of 97.6%, and a specificity of 97.2%.

Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00-99.00%) and specificity (89.63-99.00%) across all tasks highlighted the model's robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life.

A novel method for optimizing epilepsy detection features through multi-domain feature fusion and selection

Background

The methods used to detect epileptic seizures using electroencephalogram (EEG) signals suffer from poor accuracy in feature selection and high redundancy. This problem is addressed through the use of a novel multi-domain feature fusion and selection method (PMPSO).

Method

Discrete Wavelet Transforms (DWT) and Welch are used initially to extract features from different domains, including frequency domain, time-frequency domain, and non-linear domain. The first step in the detection process is to extract important features from different domains, such as frequency domain, time-frequency domain, and non-linear domain, using methods such as Discrete Wavelet Transform (DWT) and Welch. To extract features strongly correlated with epileptic classification detection, an improved particle swarm optimization (PSO) algorithm and Pearson correlation analysis are combined. Finally, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forest (RF) and XGBoost classifiers are used to construct epileptic seizure detection models based on the optimized detection features.

Result

According to experimental results, the proposed method achieves 99.32% accuracy, 99.64% specificity, 99.29% sensitivity, and 99.32% score, respectively.

Conclusion

The detection performance of the three classifiers is compared using 10-fold cross-validation. Surpassing other methods in detection accuracy. Consequently, this optimized method for epilepsy seizure detection enhances the diagnostic accuracy of epilepsy seizures.

Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset

Introduction

Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial seizure characteristics of individual patients.

Methods

We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient's distinctive seizure locations with seizure re-annotation.

Results

Our multi- and single-channel detectors achieved an average sensitivity of 97.05-100%, false alarm rate of 0.22-0.40/h, and latency of 2.1-3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones.

Discussion

We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.

Recent Advances in Wearable Healthcare Devices: From Material to Application

In recent years, the proliferation of wearable healthcare devices has marked a revolutionary shift in the personal health monitoring and management paradigm. These devices, ranging from fitness trackers to advanced biosensors, have not only made healthcare more accessible, but have also transformed the way individuals engage with their health data. By continuously monitoring health signs, from physical-based to biochemical-based such as heart rate and blood glucose levels, wearable technology offers insights into human health, enabling a proactive rather than a reactive approach to healthcare. This shift towards personalized health monitoring empowers individuals with the knowledge and tools to make informed decisions about their lifestyle and medical care, potentially leading to the earlier detection of health issues and more tailored treatment plans. This review presents the fabrication methods of flexible wearable healthcare devices and their applications in medical care. The potential challenges and future prospectives are also discussed.