AI-Driven Cardiorespiratory Signal Processing: Separation, Clustering, and Anomaly Detection

2026-02-09

AI Summary

This study addresses the challenge of effectively disentangling and identifying cardiopulmonary abnormal patterns in complex physiological signals by integrating generative AI, interpretable AI, chemically inspired non-negative matrix factorization (NMF), variational autoencoders (VAE), and quantum convolutional neural networks (QCNN). The proposed multimodal intelligent diagnostic system synergistically combines MEMS acoustic sensors with photonic integrated circuits leveraging quantum dots and nitrogen-vacancy centers. A novel high-quality cardiopulmonary sound dataset, HLS-CMDS, is introduced to enable high-precision signal separation, clustering, and anomaly detection. Experimental results demonstrate the innovative potential and practical utility of deeply integrating advanced AI algorithms with quantum photonic biosensing for next-generation intelligent healthcare applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• cardiorespiratory signal processing
• anomaly detection
• biosensing technologies
• signal separation
• physiological pattern analysis

Innovation

• generative AI
• explainable AI (XAI)
• quantum convolutional neural network (QCNN)
• non-negative matrix factorization (NMF)
• integrated quantum photonics (IQP)