On the Use of Hidden Markovian Model to Detect Cough from Respiratory Signals

This paper aims to explore the usefulness on hidden Markovian model for cough detection from abdominal and thoracic respiratory signals. Time and frequency domains based features are extracted and relevant ones are identified. Hidden Markovian Model (HMM) parameters (initial, transition and emission probabilities) are estimated. Classification is carried in parallel with frame duration optimization. Compared to some conventional machine learning methods, competitive performance were obtained when considering tradeoff between evaluation metrics.