Respiratory waveform pattern recognition using digital techniques.

An algorithm for detection of ventilatory events using digital signal processing techniques is described. Start and end of inspiration of each breath are detected using a combination of first derivative peak detection and second derivative analysis for edge detection. A convolution algorithm for computation of smoothed first and second derivatives is employed. Ventilatory data obtained automatically were compared to those calculated manually. Our results show that the difference in mean values for inspiratory time, expiratory time and total respiratory cycle time are very small (less than 6%).

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