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The wavelet transform, which is the decomposition of a signal into a set of independent frequency channels, is shown to be a useful diagnostic tool in the analysis of heartbeat sounds. In particular, the wavelet transform enables the experimentalist to obtain qualitative and quantitative measurements of time-frequency characteristics of phonocardiogram (PCG) signals.

The wavelet transform, which is a new time-scale technique suitable for transient signal detection is applied to phonocardiogram signals taken from group of patients. It has been shown that using this new technique the separation between normal and abnormal subject could easily be obtained. Moreover, a separation between the high frequency systolic and diastolic murmurs, and the low frequency systolic and diastolic murmurs can be achieved. Furthermore, the temporal location of the auscultatory gap can be detected, which is usually very difficult to dicem even to the trained ear.

This paper investigates the optimal design of analog active filters using the symbiotic organisms search (SOS) algorithm. Symbiotic organisms search is a newly proposed global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the 3 common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other methods, SOS is free of tuning parameters, which makes it an attractive optimization method. Here, SOS is used to determine the values of the passive components (resistors and capacitors) used in active filters. Two active filter topologies, fourth‐order Butterworth low‐pass filter and second‐order state variable low‐pass filter, are considered. The components values are chosen from several standard industrial series (E12, E24, and E96). The results obtained using SOS are compared with those obtained using several optimization methods, like particle swarm optimization, seeker optimization algorithm, differential evolution, and other ones. It is shown that the SOS is a robust straightforward evolutionary algorithm that can outperform other well‐known methods.

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