Deep Neural Networks for Identifying Cough Sounds

Area of Science:

• Artificial Intelligence
• Biomedical Signal Processing
• Machine Learning for Healthcare

Background:

• Cough detection is crucial for diagnosing respiratory conditions.
• Traditional methods for cough sound analysis have limitations.
• Deep learning offers potential for improved cough detection accuracy.

Purpose of the Study:

• To evaluate deep neural network approaches for cough detection.
• To compare convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for this task.
• To analyze the impact of network parameters on classifier performance.

Main Methods:

• Cough detection framed as visual recognition using CNNs.
• Cough detection framed as sequence-to-sequence labeling using RNNs.
• Performance evaluation against conventional methods and analysis of network size and signal dependencies.

Main Results:

• Both CNN and RNN architectures significantly outperformed traditional methods.
• The CNN achieved a specificity of 92.7%.
• The RNN achieved a sensitivity of 87.7%.

Conclusions:

• Deep neural networks are effective for cough detection.
• CNNs offer higher specificity, while RNNs offer higher sensitivity.
• Further exploration of network parameters can optimize cough classifier performance.