On arithmetic misconceptions of spectral analysis of biological signals, in particular respiratory sounds.

Spectral analysis is one of the most common methods in sound signal analysis for approximating sound power. However, since the sound power is usually presented in logarithmic scale, it is important to consider the non-linearity effects of logarithm function. In this study, the misconceptions and implementation issues regarding noise power reduction and average power calculation are described. Respiratory sound analysis is utilized as an example to show these issues in a practical application. The results indicate that most of the errors happen during noise power reduction; they can be either due to substituting noise reduction by sound detection concept or/and representing the noise power in the very low frequency components instead of the signal power. Also, if the average powers of the signals are calculated in the wrong scale, the results do not represent the acoustical characteristics of the sounds; this is shown by considering the flow-sound relationship at different flow rates.