Compressive asynchronous decomposition of heart sounds

Because of the need to control power consumption, in many biomedical applications asynchronous processing of the data is more appropriate. In this paper, we present a scale-based decomposition algorithm for analog signals similar to the wavelet decomposition. Our procedure uses asynchronous sigma delta modulators (ASDMs) to represent the amplitude of a signal using the zero-crossing times of a binary signal. Changing the zero-crossing times into random sequences of pulse widths, it can be shown to be equivalent to an optimal level-crossing sampler using local averages as the quantization levels. Applying the generation of multi-level signals from the output of ASDMs for different scale parameters we are able to obtain a decomposer that in a few stages provides a close representation of the signal. To illustrate the performance of the proposed decomposition, we consider its application to the representation of heart sounds.