AI融合新型传感器，实现心肺信号分离与异常检测
AI-Driven Cardiorespiratory Signal Processing: Separation, Clustering, and Anomaly Detection
- 用大模型引导分离、变分自编码器分波形、化学启发聚类
- 量子卷积网络在异常模式识别上表现优于传统方法
- 结合先进传感技术，推动智能医疗诊断系统发展
本研究将人工智能应用于心肺声音的分离、聚类与分析。构建了新数据集HLS-CMDS，开发了多种AI模型：基于大语言模型（LLMs）的生成式方法用于引导信号分离，可解释AI（XAI）技术解析潜在表征，变分自编码器（VAEs）实现波形分离，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设计的量子卷积神经网络（QCNN）。模型性能依赖于信号质量，因此论文还综述了生物传感技术，包括微机电系统（MEMS）声学传感器和量子生物传感器（如量子点、氮空位中心），并探讨了从电子集成电路（EICs）向光子集成电路（PICs）及早期集成量子光子学（IQP）芯片级生物传感的演进。这些成果展示了AI与下一代传感器协同支持未来智能诊断系统的潜力。
原文摘要 · Abstract (English)
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.
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