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In this paper, an efficient procedure to identify vital bio-signals from the Fetal Phonocardiogram (FPCG) signal is presented. FPCG signal is rich in physiological bio-signals. However, extracting these bio-signals with fidelity is a challenging task since they overlap in time and frequency and as well as corrupted by noise induced from the maternal and fetal movements. While current approaches use a two-step process of de-noising the signal and then extract the vital bio-signals, Empirical Mode Decomposition (EMD) algorithm can efficiently de-noise and extract the maternal and fetal heart sounds in a single step. Frequency range obtained from the Short Time Fourier Transform (STFT) is used to validate the algorithm. It is observed that the bandwidths of extracted FHS and MHS signals are consistent with the existing gold standards.

This study presents a cluster analysis framework for acoustic signals of the novel catheter stethoscope. The objective of the current study is to collect the blood flow sounds from a body site of a Yorkshire pig using the novel acoustic catheter stethoscope and further recognize any changes in the sinus rhythm patterns. Initially the collected blood flow sounds are preprocessed with noise cancellation and wavelet source separation to result in acoustic heart and respiratory pulses. Then, the extracted acoustic heart pulses are post processed to recognize the patterns of the sinus rhythm based on a novel feature extraction technique and cluster analysis. Finally, the developed framework is qualitatively and quantitatively validated by providing visual results of the clustering and by computing the clustering accuracy, sensitivity and specificity. The cross validation results show that the developed framework consistently recognizes the patterns of the sinus rhythms with an accuracy rate of 94.37%.