[Cough sound detection bases on EMD analysis and HMM recognition]
Weiping Hu1, Kefang Lai, Minghui Du
1College of Electronics and Communication Engineering, South China University of Technology, Guangzhou 510640, China. huwp@mailbox.gxnu.edu.cn
Summary
This study introduces a new method using Empirical Mode Decomposition (EMD) and Hidden Markov Models (HMM) to accurately detect cough sounds. This approach enhances the identification of coughs, crucial for diagnosing respiratory diseases.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Computational Linguistics
Context:
- Cough is a prevalent symptom in numerous respiratory conditions, providing vital clinical information.
- Differentiating cough sounds from non-cough sounds like speech or throat clearing is essential for accurate analysis.
Purpose:
- To develop and validate a novel method for analyzing and detecting cough sounds.
- To improve the accuracy of cough sound detection by distinguishing it from other vocalizations.
Summary:
- A new method combining Empirical Mode Decomposition (EMD) and Hidden Markov Models (HMM) is proposed for cough sound analysis.
- EMD's adaptive filter banks are utilized to obtain mean energy distribution in the frequency domain.
- Optimal characteristics for cough recognition are identified using HMM, based on statistical analysis of cough and non-cough sounds.
Impact:
- The proposed method significantly improves the detection rate of cough sounds in clinical settings.
- Enhanced cough sound detection can lead to more accurate diagnoses and better management of respiratory diseases.
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01:31Hearing
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Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
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