Description: This study was aimed at determining whether the digital volume pulse waveform using the Pulse Sensor can be used to extract features related to arterial compliance. The Pulse Sensor, a low-cost photoplethysmograph, measures green light reflection in the finger and generates output, which is indicative of blood flow and can be read by the low-cost Arduino UNO™. The Pulse Sensor code was modified to increase the sampling frequency and to capture the data in a file, which is subsequently used for waveform analysis using programs written in the R system. Waveforms were obtained using the Pulse Sensor during two 30-s periods of seated rest, in each of 44 participants, who were between the ages of 20 and 80 years. For each cardiac cycle, the first four derivatives of the waveform were calculated and low-pass filtered by convolution before every differentiation step. The program was written to extract 19 features from the pulse waveform and its derivatives. These features were selected from those that have been reported to relate to the physiopathology of hemodynamics. Results indicate that subtle features of the pulse waveform can be calculated from the fourth derivative. Feature misidentification occurred in cases of saturation or low voltage and resulted in outliers; therefore, trimmed means of the features were calculated by automatically discarding the outliers. There was a high efficiency of extraction for most features. Significant relationships were found between several of the features and age, and systolic, diastolic, and mean arterial blood pressure, suggesting that these features might be employed to predict arterial compliance. Further improvements in experimental design could lead to a more detailed evaluation of the Pulse Sensor with respect to its capability to predict factors related to arterial compliance.
Date: May 2016
Creator: Smithers, Breana Gray
Item Type: Thesis or Dissertation