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Both machine learning and median filtered autocorrelation approaches can accurately estimate heart rates from oyster optical sensors, with the latter requiring less computational power.
An in situ optical oyster heart rate sensor generates signals requiring frequency estimation with properties different to human ECG and speech signals. We discuss the method of signal generation and highlight a number of these signal properties. An optimal heart rate estimation approach was identified by application of a variety of frequency estimation techniques and comparing results to manually acquired values. Although a machine learning approach achieved the best performance, accurately estimating 96.8% of the heart rates correctly, a median filtered autocorrelation approach achieved 93.7% with significantly less computational requirement. A method for estimating heart rate variation is also presented.
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Hellicar et al. (2015) studied this question.
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