%0 Journal Article %1 TIAMKAPEN2020101983 %A Tiam Kapen, Pascalin %A Youssoufa, Mohamadou %A Kouam Kouam, Serge Urbain %A Foutse, Momo %A Tchamda, André Rodrigue %A Tchuen, Ghislain %D 2020 %J Biomedical Signal Processing and Control %K audio ecg machine-learning pcg tiny-hearts %P 101983 %R https://doi.org/10.1016/j.bspc.2020.101983 %T Phonocardiogram: A robust algorithm for generating synthetic signals and comparison with real life ones %U https://www.sciencedirect.com/science/article/pii/S1746809420301397 %V 60 %X The work presented in this paper aims to develop and implement a robust algorithm for generating synthetic phonocardiogram (PCG) signals based on characteristics obtained from real life signals. The purpose of this work is to identify a few set of parameters that can be linked to the physiological state of the heart. The mathematical model used in the synthesis of the signal was based on three coupled ordinary differential equations (ODE) proposed by Jabloun et al. (2013). The amplitude, S2S1 duration, and heart rate of the real life PCG signals were extracted and used in the synthesis of the signals. The real life PCG signals were obtained from the MITHSDB and AADHSDB databases from Massachusetts Institute of Technology and Aalborg University respectively. The signals were preprocessed using a second order Butterworth low pass filter of cutoff frequency of 500 Hz to obtain cleaner versions. The system of equations was numerically solved with the fourth-order Runge-Kutta method. These synthetic PCG signals were then compared with the real life PCG signals. The time domain correlations between the two sets of signals showed a good agreement between the two with values above 0.8 for all tested cases. The percentage differences in heart rate (beats per minutes), amplitude and S2S1 durations between the real life and synthesized PCG signals also showed a close match with percentage values as small as 1.61%, 2.27% and 4.51% for heart rate, amplitude and S2S1 duration respectively.

@article{TIAMKAPEN2020101983, abstract = {The work presented in this paper aims to develop and implement a robust algorithm for generating synthetic phonocardiogram (PCG) signals based on characteristics obtained from real life signals. The purpose of this work is to identify a few set of parameters that can be linked to the physiological state of the heart. The mathematical model used in the synthesis of the signal was based on three coupled ordinary differential equations (ODE) proposed by Jabloun et al. (2013). The amplitude, S2S1 duration, and heart rate of the real life PCG signals were extracted and used in the synthesis of the signals. The real life PCG signals were obtained from the MITHSDB and AADHSDB databases from Massachusetts Institute of Technology and Aalborg University respectively. The signals were preprocessed using a second order Butterworth low pass filter of cutoff frequency of 500 Hz to obtain cleaner versions. The system of equations was numerically solved with the fourth-order Runge-Kutta method. These synthetic PCG signals were then compared with the real life PCG signals. The time domain correlations between the two sets of signals showed a good agreement between the two with values above 0.8 for all tested cases. The percentage differences in heart rate (beats per minutes), amplitude and S2S1 durations between the real life and synthesized PCG signals also showed a close match with percentage values as small as 1.61%, 2.27% and 4.51% for heart rate, amplitude and S2S1 duration respectively.}, added-at = {2021-07-03T10:57:09.000+0200}, author = {{Tiam Kapen}, Pascalin and Youssoufa, Mohamadou and {Kouam Kouam}, Serge Urbain and Foutse, Momo and Tchamda, André Rodrigue and Tchuen, Ghislain}, biburl = {https://www.bibsonomy.org/bibtex/2f05dfe859e5cb86633968f37064a33ed/gameovercite}, description = {Phonocardiogram: A robust algorithm for generating synthetic signals and comparison with real life ones - ScienceDirect}, doi = {https://doi.org/10.1016/j.bspc.2020.101983}, interhash = {bb37b460d0d1482c879d01cc14a25993}, intrahash = {f05dfe859e5cb86633968f37064a33ed}, issn = {1746-8094}, journal = {Biomedical Signal Processing and Control}, keywords = {audio ecg machine-learning pcg tiny-hearts}, pages = 101983, timestamp = {2021-07-28T06:52:02.000+0200}, title = {Phonocardiogram: A robust algorithm for generating synthetic signals and comparison with real life ones}, url = {https://www.sciencedirect.com/science/article/pii/S1746809420301397}, volume = 60, year = 2020 }