由AI驱动的心脏呼吸信号处理：分离、聚类和异常检测
AI-Driven Cardiorespiratory Signal Processing: Separation, Clustering, and Anomaly Detection
AI总结：
本文提出利用AI技术对心脏呼吸信号进行分离、聚类和异常检测，并探讨了新一代传感器在智能医疗诊断中的应用。
摘要：
本研究将人工智能（AI）应用于心脏呼吸声音的分离、聚类和分析。我们记录了一个新的数据集（HLS-CMDS），并开发了几种AI模型，包括基于大语言模型（LLMs）的生成AI方法用于引导分离，可解释AI（XAI）技术用于解释潜在表示，变分自编码器（VAEs）用于波形分离，一种受化学启发的非负矩阵分解（NMF）算法用于聚类，以及一种设计用于检测异常生理模式的量子卷积神经网络（QCNN）。这些AI模型的性能取决于记录信号的质量。因此，本论文还回顾了用于捕获生物医学数据的生物传感技术。它总结了微机电系统（MEMS）声学传感器和量子生物传感器的发展，如量子点和氮空位中心。它进一步概述了从电子集成电路（EICs）向光子集成电路（PICs）的过渡以及向芯片生物传感的集成量子光子学（IQP）的早期进展。这些研究展示了AI和下一代传感器如何支持未来医疗保健的更智能诊断系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
