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This approach has been tried before. Bill Softky describes a startup he'd worked for using a similar sound-transduction continuous non-invasive blood-pressure monitoring technique. It ... had problems: Our non-invasive device was supposed to measure blood pressure just as accurately [as an arterial line], but without the cutting, using specially-sculpted sonic vibrations and fancy algorithmic analysis, which was my job. The overall challenge was like measuring the pressure inside a bottle without opening it. Our device worked fine, in that our algorithmically-estimated blood pressure moved up and down, beat to beat, in lockstep with the actual blood pressure. The problem was that our estimate also moved up and down at other times as well, say when the patient moved her fingers, rotated her arm, or took vaso-constricting drugs like nicotine. I spent most of a year understanding these problems, and understanding they couldn’t be solved before our funding ran out. That was when an old-timer taught me an important lesson of measurement: it’s fairly easy to calculate a signal which correlates with what you want to measure, the way our vibration-estimate correlated with actual blood pressure. It’s much harder, though, to calculate a signal which does NOT correlate with what you DON’T want to measure, like arm motion. <https://www.linkedin.com/pulse/monster-monetization-bill-sof...> I'd be exceedingly curious as to how the CalTech team have solved that non-correlation problem. |