ABSTRACT
In Italy, artificial heart development was promoted by virtue of a special program on technological and industrial development in areas related to cardiology and cardiosurgery. A first prototype series of electromechanical total artificial hearts (TAHs) and ventricular assist devices (VADs), with ball-screw-based actuation technology, has been developed, and preliminary bench tests and short-term animal implant experiments were performed. The project started with analysis and development of existing TAH and VAD models, and it included a fill-sensor-free control scheme and anatomical fitting studies using a three-dimensional computer model of the chest cavity. Second-generation prototypes are currently being developed, and they are scheduled for medium-term bench and in vivo testing by early 1994.
Subject(s)
Heart, Artificial , Heart-Assist Devices , Animals , Cattle , Electric Power Supplies , Equipment Design , Italy , Materials Testing , Polyurethanes , Program DevelopmentABSTRACT
The use of the coherent averaging technique applied to the electrocardiographic signal implies the location of a fiducial point as a synchronisation reference. An algorithm easily adaptable to a personal computer, operable in real time, insensitive to mains and to ECG-baseline fluctuations, with a low jitter value and the capacity to trigger any ECG signal wave or complex, has been developed. The algorithm detects those waveforms which, within certain confidence intervals, are morphologically equal to a reference wave. This wave is chosen by the user as the repetitive waveform within which the fiducial point is to be located. A two-window template and differential parameters are used. The possibility of building the template permits the user to adapt the algorithm to each patient's ECG. To evaluate its accuracy objectively, a software simulation was built of a generator capable of producing test signals as the sum of the 'useful' signal plus 'noise'. A jitter standard deviation of 1.65 ms was obtained in the worst test (SNR = 10 dB; noiseband = 0-50 Hz), which shows the excellent recognition accuracy of the algorithm.
