Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds.

Area of Science:

• Artificial Intelligence in Healthcare
• Pediatric Respiratory Medicine
• Signal Processing for Medical Diagnosis

Background:

• Intelligent systems are increasingly integrated into healthcare.
• Accurate diagnosis of pediatric respiratory conditions is crucial.
• Cough sound analysis offers a non-invasive diagnostic approach.

Purpose of the Study:

• To develop and evaluate a deep learning model for classifying pediatric cough sounds.
• To differentiate between healthy and pathological coughs (asthma, URTI, LRTI).
• To assess the model's accuracy in identifying specific respiratory pathologies.

Main Methods:

• A novel dataset of clinician-diagnosed cough sounds was collected.
• A bidirectional long-short-term memory (BiLSTM) network utilizing Mel-Frequency Cepstral Coefficients (MFCCs) was employed.
• Model performance was evaluated for binary (healthy vs. pathological) and multi-class (four types) cough classification.

Main Results:

• The BiLSTM model achieved over 84% accuracy in distinguishing healthy from pathological coughs.
• Combining multiple cough epochs per subject improved prediction accuracy to over 91% for specific pathologies.
• The four-class classification model showed reduced accuracy, with pathological coughs often misclassified.
• However, the four-class model achieved >84% accuracy when classifying coughs as either healthy or pathological.

Conclusions:

• Deep learning, specifically BiLSTM with MFCCs, demonstrates significant potential for automated pediatric cough sound classification.
• The model shows high accuracy in identifying general pathology and specific conditions when data is aggregated per subject.
• Further research is needed to refine differentiation among distinct pathological cough types due to feature space overlap.