OBJECTIVES: As is true for all mechanical prostheses, bileaflet heart valves are prone to thrombus formation; reduced hemodynamic performance and embolic events can occur as a result. Prosthetic valve thrombosis affects the power spectra calculated from the phonocardiographic signals corresponding to prosthetic closing events. Artificial neural network-based classifiers are proposed for automatically and noninvasively assessing valve functionality and detecting thrombotic formations. Further studies will be directed toward an enlarging data set, extending the investigated frequency range, and applying the presented approach to other bileaflet mechanical valves. METHODS: Data were acquired for the normofunctioning St. Jude Regent valve mounted in the aortic position of a Sheffield Pulse Duplicator. Different pulsatile flow conditions were reproduced, changing heart rate and stroke volume. The case of a thrombus completely blocking 1 leaflet was also investigated. Power spectra were calculated from the phonocardiographic signals and used to train artificial neural networks of different topologies; neural networks were then tested with the spectra acquired in vivo from 33 patients, all recipients of the St. Jude Regent valve in the aortic position. RESULTS: The proposed classifier showed 100% correct classification in vitro and 97% when applied to in vivo data: 31 spectra were assigned to the right class, 1 received a false positive classification, and 1 was "not classifiable." CONCLUSION: Early malfunction detection is necessary to prevent thrombotic events in bileaflet mechanical heart valves. Following further clinical validation with an extended patient database, artificial neural network-based classifiers could be embedded in a portable device able to detect valvular thrombosis at early stages of formation: this would help clinicians make valvular dysfunction diagnoses before the appearance of critical symptoms.
OBJECTIVES: As is true for all mechanical prostheses, bileaflet heart valves are prone to thrombus formation; reduced hemodynamic performance and embolic events can occur as a result. Prosthetic valve thrombosis affects the power spectra calculated from the phonocardiographic signals corresponding to prosthetic closing events. Artificial neural network-based classifiers are proposed for automatically and noninvasively assessing valve functionality and detecting thrombotic formations. Further studies will be directed toward an enlarging data set, extending the investigated frequency range, and applying the presented approach to other bileaflet mechanical valves. METHODS: Data were acquired for the normofunctioning St. Jude Regent valve mounted in the aortic position of a Sheffield Pulse Duplicator. Different pulsatile flow conditions were reproduced, changing heart rate and stroke volume. The case of a thrombus completely blocking 1 leaflet was also investigated. Power spectra were calculated from the phonocardiographic signals and used to train artificial neural networks of different topologies; neural networks were then tested with the spectra acquired in vivo from 33 patients, all recipients of the St. Jude Regent valve in the aortic position. RESULTS: The proposed classifier showed 100% correct classification in vitro and 97% when applied to in vivo data: 31 spectra were assigned to the right class, 1 received a false positive classification, and 1 was "not classifiable." CONCLUSION: Early malfunction detection is necessary to prevent thrombotic events in bileaflet mechanical heart valves. Following further clinical validation with an extended patient database, artificial neural network-based classifiers could be embedded in a portable device able to detect valvular thrombosis at early stages of formation: this would help clinicians make valvular dysfunction diagnoses before the appearance of critical symptoms.