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

TL;DR

This paper explores AI techniques for analyzing cardiorespiratory sounds, introducing new datasets, models, and sensor technologies to improve diagnostics and understanding of physiological patterns.

Contribution

It presents novel AI models for sound separation, clustering, and anomaly detection, along with a new dataset and review of advanced biosensing technologies.

Findings

AI models' performance depends on signal quality

Generative AI aids in guided separation of sounds

Quantum sensors and integrated photonics advance biosensing

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.