Respiratory Disease Classification and Biometric Analysis Using Biosignals from Digital Stethoscopes

TL;DR

This paper introduces a machine learning-based method utilizing digital stethoscope biosignals for accurate respiratory disease classification, biometric analysis, and demographic estimation, demonstrating high accuracy and novel regression capabilities.

Contribution

The work presents a novel approach combining Empirical Mode Decomposition and spectral analysis on digital stethoscope data for disease classification and biometric estimation, including age, BMI, and sex.

Findings

89% accuracy in binary classification of healthy vs. diseased

72% accuracy in multi-class respiratory disease classification

First-time regression models for age, BMI, and sex based on acoustic data

Abstract

Respiratory diseases remain a leading cause of mortality worldwide, highlighting the need for faster and more accurate diagnostic tools. This work presents a novel approach leveraging digital stethoscope technology for automatic respiratory disease classification and biometric analysis. Our approach has the potential to significantly enhance traditional auscultation practices. By leveraging one of the largest publicly available medical database of respiratory sounds, we train machine learning models to classify various respiratory health conditions. Our method differs from conventional methods by using Empirical Mode Decomposition (EMD) and spectral analysis techniques to isolate clinically relevant biosignals embedded within acoustic data captured by digital stethoscopes. This approach focuses on information closely tied to cardiovascular and respiratory patterns within the acoustic data. Spectral analysis and filtering techniques isolate Intrinsic Mode Functions (IMFs) strongly correlated with these physiological phenomena. These biosignals undergo a comprehensive feature extraction process for predictive modeling. These features then serve as input to train several machine learning models for both classification and regression tasks. Our approach achieves high accuracy in both binary classification (89% balanced accuracy for healthy vs. diseased) and multi-class classification (72% balanced accuracy for specific diseases like pneumonia and COPD). For the first time, this work introduces regression models capable of estimating age and body mass index (BMI) based solely on acoustic data, as well as a model for sex classification. Our findings underscore the potential of intelligent digital stethoscopes to significantly enhance assistive and remote diagnostic capabilities, contributing to advancements in digital health, telehealth, and remote patient monitoring.