Neural network based algorithm for automatic identification of cough sounds

Abstract

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.

A Cough-Based Algorithm for Automatic Diagnosis of Pertussis

“…The cough detection part of the diagnosis algorithm presented here, if used on its own, achieves performance that is comparable to other methods proposed in literature. This is despite its lower complexity compared to other cough detection methods [ 8 , 10 , 11 , 30 ] that use HMM, SVM and neural networks for classification.…”

“…In both these methods the total number of coughs in the dataset were not reported. Swarnkar et al [ 8 ] used other spectral features such as formant frequencies, kurtosis, and B–score together with MFCC features for cough detection. These were fed into a neural network resulting in a sensitivity of 93% and a specificity of 94% for a test dataset consisting of 342 coughs from 3 subjects only.…”

Towards Device-Agnostic Mobile Cough Detection with Convolutional Neural Networks

“…They are based on artificial neural networks [29] and can be characterized as a model consisting of a cascade of multiple layers of nonlinear information processing [30]. Even though some of the most prominent cough detection algorithms, which have been employed to conventional condenser microphone signals [17], [21]- [23] exploit neural network architectures, they heavily focus on the engineering of features and still have rather shallow architectures consisting of 2-4 hidden layers. This may be explained by the difficulty of collecting large amounts of data from real subjects resulting in fewer data samples, which limited scaling to a deeper network.…”

Theory and Application of Audio-Based Assessment of Cough

“…The advantages and disadvantages of these algorithms cannot be determined because the results of different experiments are different [34,64]. However, MFCCs + SVM is used more widely [65,66], and the neural network has potential to model and achieve accurate identification [67,68].…”