Cough classification of MEMS microphone signal using time-series and tabular machine learning algorithms

Cough is a protective mechanism of the proximal respiratory tract. The frequency and severity cough provide useful information for diagnosis upper diseases evaluation their treatments. Manual classification subjective, labor-intensive, time-consuming. In this study, technique based on microelectromechanical system microphone proposed. For process, various tabular time-series machine learning algorithms were applied, results compared. With respect to algorithms, random interval decision tree, spectral forest, convolution kernel transform (ROCKET) methods used. neural network (CNN) with 40 Mel-frequency cepstral coefficients (MFCCs) recurrent MFCCs Voluntary noncough (throat clearing, expiration, speaking, rest) signals recorded from 10 healthy subjects. ROCKET method showed best accuracy (98.40%). addition, while its training took longest time (1628.80 s), algorithm reasonably short prediction (0.27 s). CNN second-best (97.81%) (454.13 s) (0.40 times. Thus, given time, recommended type over algorithm. To validate application proposed methods, two applied public Coswara data set. classify one class three non-cough classes, reasonable accuracies 90.33% 89.16%, respectively.