Neural network based algorithm for automatic identification of cough sounds.

Cough is the most common symptom of the several respiratory diseases containing diagnostic information. It is the best suitable candidate to develop a simplified screening technique for the management of respiratory diseases in timely manner, both in developing and developed countries, particularly in remote areas where medical facilities are limited. However, major issue hindering the development is the non-availability of reliable technique to automatically identify cough events. Medical practitioners still rely on manual counting, which is laborious and time consuming. In this paper we propose a novel method, based on the neural network to automatically identify cough segments, discarding other sounds such a speech, ambient noise etc. We achieved the accuracy of 98% in classifying 13395 segments into two classes, 'cough' and 'other sounds', with the sensitivity of 93.44% and specificity of 94.52%. Our preliminary results indicate that method can develop into a real-time cough identification technique in continuous cough monitoring systems.

Automatic identification of wet and dry cough in pediatric patients with respiratory diseases.

Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis especially in children. Wet coughs are more likely to be associated with lower respiratory track bacterial infections. At present during a typical consultation session, the wet/dry decision is based on the subjective judgment of a physician. It is not available for the non-trained person, long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop an automated technology to classify cough into 'wet' and 'dry' categories. We propose novel features and a Logistic regression model (LRM) for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric coughs (C = 536) recorded using a bed-side non-contact microphone from N = 78 patients. Results of the automatic classification were compared against two expert human scorers. The sensitivity and specificity of the LRM in picking wet coughs were between 87 and 88% with 95% confidence interval on training/validation dataset (310 cough events from 60 patients) and 84 and 76% respectively on prospective dataset (117 cough events from 18 patients). The kappa agreement with two expert human scorers on prospective dataset was 0.51. These results indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.

Automated algorithm for Wet/Dry cough sounds classification.

Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully automated technology to classify cough into 'Wet' and 'Dry' categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.