Cough event classification by pretrained deep neural network.

Area of Science:

• Medical Technology
• Artificial Intelligence
• Signal Processing

Background:

• Cough is a key symptom in respiratory diseases.
• Accurate and objective cough monitoring is crucial for patient care.
• Current monitoring methods require improvement for better disease management.

Purpose of the Study:

• To introduce a pretrained deep neural network (DNN) algorithm for cough classification.
• To enhance the accuracy of cough monitoring systems.
• To improve the objective measurement of cough severity in respiratory diseases.

Main Methods:

• Developed DNN models using unsupervised pretraining and fine-tuning.
• Integrated DNN with Hidden Markov Model (HMM) for temporal audio analysis.
• Utilized GMM-HMM for initial state alignment and Viterbi decoding for classification.

Main Results:

• DNN-based methods showed superior performance over traditional GMM-HMM.
• Maximal 14% error reduction in F1 score and 11% in micro average (Patient Dependent).
• Significant improvements in specificity, with up to 14% error reduction (PD and PI).

Conclusions:

• Pretrained DNN significantly enhances cough classification accuracy.
• The HMM-DNN framework offers better overall performance than conventional GMM-HMM.
• This approach advances objective cough monitoring for respiratory diseases.