Use of a linear predictive analysis method to detect gas bubbles generated in the bloodstream during decompression

This paper discusses an algorithm for detecting bubbles in a diver's bloodstream during decompression from submergence. A widely employed method at present for detecting bubbles is the ultrasonic Doppler method, which is based on aural sensation. However, a problem here is that the Doppler signal contains various kinds of noises such as cardiac motion signals. Thus, the observer is required to concentrate to detect the bubble sound from the noise when the number of bubbles are few, and to count the number when there are many. This is not an easy task, even for an expert. The authors discovered the features of the typical bubble sound with respect to the time waveform and the frequency spectrum, and developed a new bubble-detection algorithm based on linear predictive signal processing. An experiment for bubble detection is executed, using a material to demonstrate the bubble-detection Doppler method and data recorded in the simulated 300-m manned submergence. It is verified that the proposed method has nearly the same performance as that of an expert. Finally, this paper discusses the phenomenon where the frequency of the bubble decreases with the increase in bubble generation.