Biosignal Feature Extraction with Deep Learning: Applications in Clinical Diagnostics and Therapeutics

Biological signals (such as electrocardiogram and electroencephalogram) hold significant value in disease diagnosis and treatment.However, traditional methods of processing these signals suffer from low efficiency and vulnerability to noise interference.With the integrated development of AI and communication technologies, such as 5G and the Internet of Things, a new path has been opened for precision medicine.This study focuses on exploring how AI can leverage the extraction of biological signal features and communication optimisation to assist in disease treatment.By conducting a literature review to summarise the latest theories and analysing successful treatment cases, the research results show that AI can significantly improve the accuracy of biological signal feature extraction, and communication technologies can effectively ensure real-time treatment responses.Moreover, the fusion of multi-modal signals and lightweight AI will become a key direction for future development in this field, providing support for the advancement of disease identification and treatment towards greater precision and efficiency.