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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Automatic Non-Invasive Cough Detection based on Accelerometer and Audio Signals.

Madhurananda Pahar1, Igor Miranda2, Andreas Diacon3

  • 1Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, 7600 Western Cape South Africa.

Journal of Signal Processing Systems
|March 28, 2022
PubMed
Summary

This study introduces a non-invasive cough detection method using smartphone accelerometers and microphones. Deep learning models, particularly Resnet50, achieved high accuracy, with accelerometer-based detection offering a privacy-preserving alternative for long-term monitoring.

Keywords:
AccelerometerAudioCNNCough detectionLRLSTMMLPResnet50SVM

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Continuous cough monitoring is crucial for diagnosing and managing respiratory conditions.
  • Existing methods often require invasive sensors or are limited by privacy concerns.
  • Developing non-invasive, accurate, and convenient cough detection systems is a significant challenge.

Purpose of the Study:

  • To develop and evaluate an automatic, non-invasive method for detecting cough events using smartphone-based accelerometer and audio signals.
  • To compare the performance of traditional machine learning classifiers with deep learning architectures for cough detection.
  • To assess the feasibility of using accelerometer data alone for accurate and privacy-preserving cough monitoring.

Main Methods:

  • A dataset of simultaneous accelerometer and audio signals was collected from 14 adult male patients, comprising approximately 6000 cough and 68000 non-cough events.
  • Traditional classifiers (Logistic Regression, SVM, MLP) and deep learning models (CNN, LSTM, Resnet50) were trained and evaluated using a leave-one-out cross-validation scheme.
  • Performance was assessed by distinguishing coughs from other activities like sneezing, throat-clearing, and movement.

Main Results:

  • Deep neural networks significantly outperformed shallow classifiers in cough detection accuracy.
  • Resnet50 achieved the highest performance, with an Area Under the ROC Curve (AUC) exceeding 0.98 for acceleration and 0.99 for audio signals.
  • While audio-based detection showed slightly better performance, accelerometer-based detection achieved comparable accuracy with enhanced privacy and convenience.

Conclusions:

  • Accurate non-invasive cough detection is achievable using either accelerometer or audio signals from a smartphone.
  • Deep learning models, especially Resnet50, demonstrate superior performance compared to traditional methods.
  • Accelerometer-based cough detection presents a promising, convenient, and privacy-securing solution for long-term cough monitoring.