抄録
The activity sensing rate-responsive pacer incorporates a sensor as a piezo-cristal or accelerometer, bounded to the pacemakers. Body vibration which (is produced by physical activity effects the sensor. Rate regulation system cannot detect physiological parameters, because the system is open loop. Therefore, rate responsibility is to depend upon sensor geometry and system algorithm. Rate regulation of activity sensing pacer is linearly regulated by signal strength of the sensor, because the signal strength is associated with strength of activity. However the relationship of workload and sinus rate isn't linear, especially on intermittent or decreasing load. This isone of activity sensing pacer limitation in physiological pacing. Neural network system has a superior capacity of pattern recognition. In this article, we propose a new rate regulation algorithm using neural network system which learned patterns of relationship between sinus rate and workload. The Kohonen's Feature Map was chosen as a network model and the extended Learning Vector Quantization (LVQ) was adopted as a learning algorithm. Relationship between sinus rate and workload was examined using multiple exercise circuit test.
