Research on Teaching Methodologies for Key Computational Techniques in Medical Signal Processing: A Case-Driven Medical-Engineering Integration Approach

Le résumé fourni par la source

Against the backdrop of rapid intelligent healthcare development, core computational techniques in medical signal processing—such as signal preprocessing, feature extraction, and classification modeling—constitute critical competencies for cultivating talent in intelligent medical engineering. However, traditional teaching models suffer from disconnects between theory and practice and insufficient integration of medicine and computer science, failing to meet the talent cultivation demands of the intelligent healthcare field. This paper proposes a case-driven instructional design integrating computer modeling with medical signal processing. The core of this approach lies in constructing a teaching framework focused on medical signal processing based on key computational techniques: using electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals as research subjects, it systematically designs and implements teaching content and experimental projects covering core components including signal preprocessing, feature extraction, and classification modeling. Driven by authentic medical case data, feature extraction algorithm competitions and classification model optimization tasks are conducted using MATLAB or Python, focusing on training students in understanding core algorithms, implementation capabilities, model parameter adjustment, and performance optimization strategies. This integrated teaching framework centered on computer simulation and modeling effectively enhances students’ practical competence and innovation capabilities in key medical signal processing technologies, providing a cultivation pathway for interdisciplinary talent mastering core computational techniques in intelligent healthcare.

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