Giacomo Melan1, Angela Bellato1, Francesca Maria Susin2, Tomaso Bottio3, Vincenzo Tarzia3, Vittorio Pengo4, Gino Gerosa3, Andrea Bagno1. 1. Department of Industrial Engineering, University of Padua, Padua, Italy. 2. Department of Civil, Environmental and Architectural Engineering, University of Padua, Padua. 3. Cardiovascular Surgery, University of Padua School of Medicine, Padua, Italy. 4. Clinical Cardiology, Department of Cardiologic, Thoracic and Vascular Sciences, University of Padua School of Medicine, Padua, Italy.
Abstract
BACKGROUND AND AIM OF THE STUDY: The main disadvantage of a mechanical heart valve (MHV) is thrombosis, a serious complication that is associated with high morbidity and mortality. The early detection of thrombotic formations is crucial for a prompt diagnosis and correct therapy before critical symptoms appear in patients. The present study describes the in-vitro assessment of thrombotic deposits by ultrasound phonocardiography on five commercially available bileaflet MHVs. METHODS: The closing sounds produced by bileaflet MHVs were acquired in the frequency range from 6 to 55 kHz. The corresponding power spectra were calculated and then analyzed by an artificial neural network (ANN) trained to classify the presence of simulated thrombotic formations of different weight and shape. Simulations were performed in a Sheffield pulse duplicator under different hydrodynamic regimes. RESULTS: Classification performances of the ANN depend on the range of frequency considered: better performances (up to 100% correct classification) are achieved when the entire spectrum is considered, rather than the audible (down to 87%) and ultrasound (down to 61%) regions, separately. CONCLUSION: Good and very good classification performances are achieved in vitro when phonocardiography is applied to detect and analyze the closing sounds produced by MHVs. Interestingly, extension of the analysis to the ultrasound region can improve classification efficiency. This finding allows the consideration of potential clinical applications of the proposed method to assign an MHV recipient to a risk class, thus enabling a prompt diagnosis.
BACKGROUND AND AIM OF THE STUDY: The main disadvantage of a mechanical heart valve (MHV) is thrombosis, a serious complication that is associated with high morbidity and mortality. The early detection of thrombotic formations is crucial for a prompt diagnosis and correct therapy before critical symptoms appear in patients. The present study describes the in-vitro assessment of thrombotic deposits by ultrasound phonocardiography on five commercially available bileaflet MHVs. METHODS: The closing sounds produced by bileaflet MHVs were acquired in the frequency range from 6 to 55 kHz. The corresponding power spectra were calculated and then analyzed by an artificial neural network (ANN) trained to classify the presence of simulated thrombotic formations of different weight and shape. Simulations were performed in a Sheffield pulse duplicator under different hydrodynamic regimes. RESULTS: Classification performances of the ANN depend on the range of frequency considered: better performances (up to 100% correct classification) are achieved when the entire spectrum is considered, rather than the audible (down to 87%) and ultrasound (down to 61%) regions, separately. CONCLUSION: Good and very good classification performances are achieved in vitro when phonocardiography is applied to detect and analyze the closing sounds produced by MHVs. Interestingly, extension of the analysis to the ultrasound region can improve classification efficiency. This finding allows the consideration of potential clinical applications of the proposed method to assign an MHV recipient to a risk class, thus enabling a prompt diagnosis.