Advancing Epilepsy Diagnosis: Insights from EEG Signal Analysis with SVM and CNN

Epilepsy is a common neurological disorder in elderly populations, often linked to age-related conditions such as stroke and neurodegeneration. Traditional EEG signal analysis for epilepsy diagnosis is time-consuming, subjective, and unsuitable for clinical use, emphasizing the necessity for automated approaches. This study evaluates the performance of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) in classifying EEG signals for epileptic seizure detection, utilizing the Bonn University EEG dataset. Time- and frequency-domain features were extracted, and 10-fold cross-validation was employed to validate the results. The findings reveal that SVM achieved 100% accuracy in distinguishing simple EEG states, such as healthy versus seizure conditions. Meanwhile, CNN outperformed SVM in processing more complex signals, achieving an average accuracy of over 98%. The results highlight the potential of integrating traditional machine learning with deep learning methods to enhance diagnostic accuracy and efficiency. These findings lay a strong foundation for developing advanced EEG-based diagnostic tools tailored to elderly epilepsy patients, facilitating more timely and effective clinical interventions

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Content Details:-

Corresponding author: Ting Wang*

Full Length Article: Advancing Epilepsy Diagnosis: Insights from EEG Signal Analysis with SVM and CNN

Journal: Gerontology & Geriatrics: Research

Disclaimer: This content is not owned or created by us. It has been sourced from the respective site, Austin Publishing Group, and is intended purely for study and educational purposes. All rights belong to the original authors and publishers.

Exploring Chest Disease Classification Methods Using X-ray Image Analysis

 

 

The World Health Organization has suffered from the limited diagnosis support systems and limited physicians. Especially in rural areas, almost all cases are handled by a single physician that is time consuming and tiring. Computer added diagnostic systems are being developed to solve this problem. The automated computer added diagnostic tools are of great significance for patient screening. The computer-aided detection based on Chest X-Ray Radiographs (CXR) play an important role in the diagnosis and treatment planning of the patients having lung diseases such as COVID-19, pneumonia etc. This review article presents a brief overview of all the available computer-aided systems to classify chest diseases using X-ray images. This review emphases the most common chest diseases such as Covid-19 and pneumonia along with different deep learning and machine learning techniques as available in the literature. This review paper can be useful for the researchers who are working in these areas for further improvements and advancements in the current technologies.

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Content Details:-

Corresponding author: Jyoti Gupta*

Full Length Article: Exploring Chest Disease Classification Methods UsingX-ray Image Analysis

Journal: Austin Journal of Radiology

Disclaimer: This content is not owned or created by us. It has been sourced from the respective site, Austin Publishing Group, and is intended purely for study and educational purposes. All rights belong to the original authors and publishers.