Acoustic cough analyses
cough, Cough
All recorded cough and throat-clearing signals will be analysed with a software developed for the purpose of this study and written in the Python programming language.Segmentation: Cough samples are segmented manually into single coughs leaving silent intervals before and after each signal. Subsequent automatic segmentation is performed via the signal contour by assigning to the onset the first contour sample and to the offset the last contour sample the value of which is larger than − 30 dB with regard to the signal contour maximum. Prior to analysis, the segmented cough signals are normalized so that the average energy of the signal equals 1.Spectral analysis: Cough signals are transient signals (average duration of 0.3 s). As a consequence, conventional spectrograms are an unsatisfactory representation. Spectrograms are suitable for locally stable sounds such as speech signals that comprise quasi-stable targets connected by transitions. Also, features available in standard analysis tools are suitable for sustained sounds only. The cough signal changes quickly and incessantly over a brief period of time. Given the transient nature of the cough signal, our software focus on a limited number of isolated frequency bands, whose spectral energies are reported band by band. By lowering the number of frequency bands (lower frequency resolution), we are able to examine the temporal evolution of the signal band by band (higher temporal resolution). A filter bank based on the discrete cosine transform (DCT) divides the signals into frequency bands, the boundaries of which are 0–400 Hz, 400–800 Hz, 800–1600 Hz and 1600–3200 Hz as well as the interval between 3200 Hz and half the sampling frequency (22,050 kHz). The DCT periodically extends the signal by pivoting it with respect to its onset and offset so that the extended signal is even. The difference between the discrete cosine transform and the discrete Fourier transform is that the former avoids spectral artefacts that would be caused by concatenating rapid high-amplitude onsets (bursts) with gradual low-amplitude offsets. The cough signal can be accurately decomposed using a DCT, meaning that the energy of the original cough signal and the sum of the energies of the band-filtered signals are identical [Temporal analysis: The temporal analysis involves the evolution with time of the contours of the cough signal amplitude, the sample entropy and the kurtosis.The The The The 