This research applies artificial intelligence (AI) to separate, cluster, and analyze cardiorespiratory sounds. We recorded a new dataset (HLS-CMDS) and developed several AI models, including generative AI methods based on large language models (LLMs) for guided separation, explainable AI (XAI) techniques to interpret latent representations, variational autoencoders (VAEs) for waveform separation, a chemistry-inspired non-negative matrix factorization (NMF) algorithm for clustering, and a quantum convolutional neural network (QCNN) designed to detect abnormal physiological patterns. The performance of these AI models depends on the quality of the recorded signals. Therefore, this thesis also reviews the biosensing technologies used to capture biomedical data. It summarizes developments in microelectromechanical systems (MEMS) acoustic sensors and quantum biosensors, such as quantum dots and nitrogen-vacancy centers. It further outlines the transition from electronic integrated circuits (EICs) to photonic integrated circuits (PICs) and early progress toward integrated quantum photonics (IQP) for chip-based biosensing. Together, these studies show how AI and next-generation sensors can support more intelligent diagnostic systems for future healthcare.

翻译：本研究应用人工智能（AI）技术对心肺音信号进行分离、聚类与分析。我们采集了一个新的数据集（HLS-CMDS），并开发了多种AI模型，包括基于大语言模型（LLMs）的生成式AI方法用于引导分离、可解释AI（XAI）技术用于解释潜在表征、变分自编码器（VAEs）用于波形分离、受化学启发的非负矩阵分解（NMF）算法用于聚类，以及专为检测异常生理模式而设计的量子卷积神经网络（QCNN）。这些AI模型的性能取决于所记录信号的质量。因此，本论文还综述了用于捕获生物医学数据的生物传感技术，总结了微机电系统（MEMS）声学传感器和量子生物传感器（如量子点和氮空位中心）的发展，进一步概述了从电子集成电路（EICs）到光子集成电路（PICs）的转变，以及面向芯片生物传感的集成量子光子学（IQP）的早期进展。这些研究共同展示了AI与下一代传感器如何为未来医疗保健提供更智能的诊断系统支持。