生物医学工程系学术沙龙（BME Seminar）

时间：2024年12月17日，周二，下午2:30-3:30

Time: 14：30-15:30 on Tuesday, December 17.

地点：生物医学工程系讨论室（新行政楼Block A 3楼）

Location: Meeting Room of BME At Third Floor of Block A

报告人：Ijaz Ahmad

Speaker：Ijaz Ahmad

题目：EEG Epileptic Seizure Detection and Prediction Using Advanced AI Models

报告人简介：

Ijaz Ahmad is a researcher with extensive expertise in biomedical signal processing, neural engineering, and artificial intelligence applications in smart healthcare system. His work has focused on developing advanced deep learning and machine learning approaches for detecting and predicting epileptic seizures, multimodal signal analysis (EEG, ECG, EMG), and integrating IoT for clinical applications. His contributions also extend to advanced bio-signal analysis methods and medical imaging algorithms for various diseases like Alzheimer's, brain tumors, lung diseases, and skin cancer.

He completed his Ph.D. from Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China. His academic journey includes a Master's degree in Computer Engineering from Changchun University of Science and Technology and significant achievements in deep learning-driven clinical frameworks and signal analysis.

He published high quality papers such as IEEE JBHI, IEEE Transection, with a significant citation (>1150), total 32 SCI papers and 6 conferences. He is the reviewer of the international Journal such as IEEE TMI, IEEE TBME, Journal of Computer in Biology and Medicine, Journal of Neural Engineering, Journal of Biomedical Signal Processing, have various distinctions, including the Best International Excellence Award in 2023. Best Paper Award in 2020.

摘要：

Epilepsy is a neurological disorder affecting around 50 million people worldwide. Neurologists often spend significant time analyzing long-term EEG recordings to detect signs of epilepsy, while seizures can occur suddenly, posing risks such as injury, falls, and accidents. Therefore, an improved automated smart healthcare application using advanced AI models is urgently needed in clinical practice to support neurologists and patients with the earlier detection and prediction of seizures.

工学院

2024年12月16日