Jiann-Shing Shieh received the B.S. and M.S. degrees in chemical engineering from National Cheng Kung University, Tainan, Taiwan, in 1983 and 1986, respectively, and the Ph.D. degree in automatic control and systems engineering from the University of Sheffield, Sheffield, U.K., in 1995. He is currently a Professor with the Department of Mechanical Engineering, a Joint Professor with the Graduate School of Biotechnology and Bioengineering, and also serves as the Provost of Yuan Ze University, Taoyuan, Taiwan. He has published more than 150 papers in peer-reviewed international journals. His research interests include biomedical engineering, particularly in bio-signal processing of ECG, BP, EEG, SPO2, center of pressure position signals, artificial intelligent analysis and control, medical automation, pain model and control, critical care medicine monitoring and control, dynamic cerebral autoregulation research, and brain death index research. Dr. Shieh also serves a Section Editor of the Journal of Clinical Medicine and an Academic Editor of the Journal of Healthcare Engineering. He is also a principal investigator for Taiwan Experience Education Program for nearly 4 years as the following topic: Industry 4.0 - Research on Smart Production and Management.

Two topics for this IIPP project:

1. One of the most challenging predictive data analysis efforts is an accurate prediction of depth of anesthesia (DOA) indicators which has attracted growing attention since it provides patients a safe surgical environment in case of secondary damage caused by intraoperative awareness or brain injury. However, many researchers put heavily handcraft feature extraction or carefully tailored feature engineering to each patient to achieve very high sensitivity and low false prediction rate for a particular dataset. This limits the benefit of the proposed approaches if a different dataset is used. Recently, representations learned using the deep convolutional neural network (CNN) for object recognition are becoming a widely used model of the processing hierarchy in the human visual system. The correspondence between models and brain signals that holds the acquired activity at high temporal resolution has been explored less exhaustively. In this topic, deep learning CNN with a range of different architectures is designed for identifying related activities from raw electroencephalography (EEG). Specifically, an improved short-time Fourier transform is used to stand for the time-frequency information after extracting the spectral images of the original EEG as input to CNN. Then CNN models are designed and trained to predict the DOA levels from EEG spectrum without handcrafted features, which presents an intuitive mapping process with high efficiency and reliability.

2. This topic introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level assessment through ECG signal analysis. The primary focus lies in developing a robust, privacy-preserving system that accurately classifies pain levels (low, medium, and high) by leveraging the intricate relationship between pain perception and autonomic nervous system responses captured in ECG signals. At the heart of our methodology lies a signal processing approach that transforms one-dimensional ECG signals into rich, two-dimensional Continuous Wavelet Transform (CWT) images. These transformations capture both temporal and frequency characteristics of pain-induced cardiac variations, providing a comprehensive representation of autonomic nervous system responses to different pain intensities.  In this project, special attention is given to the AI and quantum technologies that are utilized in signal processing and diagnosis in order to reduce the computationally intensive and less memory hardware.