Photoplethysmography (PPG) is a noninvasive method suitable for extracting the HR when monitoring the physiological condition. The PPG waveform is readily contaminated by various mechanisms, such as raising or lowering the hand, breathing, changes in stroke volume, the presence of double dicrotic notches, and cardiovascular diseases. These made heart rate extraction a difficult problem. The purpose of this study was to propose a heart rate extraction method that has better performance than the wavelet and correlation methods. A fuzzy logic discriminator was used to discriminate the truth of each peak of the slope of the PPG signal based on weights. A determining algorithm used these weights to extract the heart rate, and also to insert an interpolated peak near the time of a missing peak. This allowed the physiologic condition of a patient during the rapid heart rate change to be reliably monitored. The study applied six different PPG waveforms to test our desired method. The root-mean-square error values relative to the reference showed that the desired method for Case I is 5.15 beats/min, and for Case IV (1) it is 0
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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
