An algorithm for the automatic analysis of signals from an oyster heart rate sensor

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.