本課程聚焦於生醫訊號處理的基礎理論與實務應用，適合大學高年級與碩士班初階學生修習。課程將涵蓋心電圖（ECG）、腦波（EEG）、肌電圖（EMG）等常見生醫訊號的前處理、特徵提取與統計分析方法。學生將學習常用的濾波器設計、頻譜分析、時頻轉換（如STFT、Wavelet）、與非線性分析（如熵與複雜度指標）等技術，並使用Python、MATLAB或其他開源工具進行實作。課程目標為培養學生能理解各種訊號處理方法背後的數學與生理意涵，並具備實作能力以應用於專題或個人研究題目，建立跨領域的問題解決與資料分析能力，為日後從事研究或進入生醫產業奠定基礎。

This course focuses on basic theories and practical applications of medical number processing, and is suitable for junior college students in college and junior college students. The course will cover pre-treatment, characteristic extraction and statistical analysis methods for common medical signals such as cardiac electrocardiogram (ECG), brain wave (EEG), and myocardial electrocardiogram (EMG). Students will learn commonly used filter design, spectrum analysis, time-frequency conversion (such as STFT, Wavelet), and nonlinear analysis (such as entropy and complexity indicators), and use Python, MATLAB or other source tools to perform the work. The course objectives are to cultivate students to understand the mathematical and physiological implications behind various information processing methods, and to have practical skills to apply them to topics or personal research topics, establish cross-domain problem solving and data analysis capabilities, laying the foundation for future research or entry into the biomedical industry.

主要理論部分Biomedical Signal Analysis, 3rd Edition
搭配自編投影片

Main theories Biomedical Signal Analysis, 3rd Edition
With self-edited projection film