Abstract
Photoplethysmography (PPG) is a low cost, non-invasive optical technology to detect the volumetric changes of blood circulation at the surface of skin. While the medical indication of components of PPG signals in the form of pulse wave are not yet fully understood, it is vastly agreed that they carry valuable pathophysiological information related to the cardiovascular system. Going beyond just dealing with frequency and time domain features of the pulse wave, as well as the first and second derivatives of the wave commonly seen in many of the relevant work, we applied a K-MEANS improved algorithm for feature extraction based on selected time domain parameters: K1 (systolic area), K2 (diastolic area) and K (entire pulse wave area). The extracted characteristic waveforms under the same light intensity could achieve average confidence level of 90% or higher. The stationary wavelet transform was adopted to further analyze the characteristic waveform by calculating the wavelet entropy; We then trained a Probability Neural Network (PNN) model using the wavelet entropy and other time domain characteristic parameters. It is found that the trained PNN model performs well in analyzing characteristic waveform to distinguish between health condition and severe arterial stenosis.
Highlights
Going beyond just dealing with frequency and time domain features of the pulse wave, as well as the first and second derivatives of the wave commonly seen in many of the relevant work, we applied a K-MEANS improved algorithm for feature extraction based on selected time domain parameters: K1, K2 and K
It is found that the trained Probability Neural Network (PNN) model performs well in analyzing characteristic waveform to distinguish between health condition and severe arterial stenosis
It is believed that the second derivative of pulse wave contains essential health-related information, pulse wave analysis could be of significant value in evaluating cardiovascular diseases, facilitating early detection and recognition of illnesses, and continuous health monitoring
Summary
Photoplethysmography (PPG) is an electro-optic technology to generate cardiovascular pulse wave by measuring the volumetric changes of blood circulation. PPG measurement usually collects excessive data to average out noises for better signal quality. This inevitably could further raise difficulties for human reader of the PPG pulse wave. It is of practical use to extract feature waveform from vast PPG pulse wave data for the purpose of improving productivity of human readers. We propose in the first part of this paper clustering algorithms to extract PPG pulse waves characteristics using three time domain feature parameters: K1, K2 and K, where K1 represents the systolic area, K2 denotes the diastolic area, whereas K holds the entire pulse wave area. An improved K-MEANS algorithm is adopted to extract the feature waveforms out of the pulse wave sets given the same light intensity. The trained model is tested to show the effectiveness in classification of waveforms to distinguish between health condition and severe arterial stenosis
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