Analysis of arterial pulse waveforms is important for non-invasive diagnosis of cardiovascular functions. Large samples of IPG signal records of radial arterial pulse show presence of eight different types of shapes (...Analysis of arterial pulse waveforms is important for non-invasive diagnosis of cardiovascular functions. Large samples of IPG signal records of radial arterial pulse show presence of eight different types of shapes (morphological patterns) in the pulse waveforms. In this paper we present an efficient computational method for automatic identification of these morphological patterns. Our algorithm uses likelihood ratio of cumulative periodogram of pulse signals and some geometrical criteria. The algorithm is presented with necessary details on signal processing aspects. Results for a large sample of pulse records of adult Indian subjects show high accuracy of our algorithm in detecting pulse-morphology patterns. Variation of pulse-morphology with respect to time is also analyzed using this algorithm. We have identified some characteristic features of pulse-morphology variation in patients of certain cardiac problems, hypertension, and diabetes. These are found relevant and significant in terms of physiological interpretation of the associated shapes of pulse waveforms. Importance of these findings is highlighted along with discussion on overall scope of our study in automatic analysis of heart rate variability and in other applications for non-invasive prognosis/diagnosis.展开更多
文摘Analysis of arterial pulse waveforms is important for non-invasive diagnosis of cardiovascular functions. Large samples of IPG signal records of radial arterial pulse show presence of eight different types of shapes (morphological patterns) in the pulse waveforms. In this paper we present an efficient computational method for automatic identification of these morphological patterns. Our algorithm uses likelihood ratio of cumulative periodogram of pulse signals and some geometrical criteria. The algorithm is presented with necessary details on signal processing aspects. Results for a large sample of pulse records of adult Indian subjects show high accuracy of our algorithm in detecting pulse-morphology patterns. Variation of pulse-morphology with respect to time is also analyzed using this algorithm. We have identified some characteristic features of pulse-morphology variation in patients of certain cardiac problems, hypertension, and diabetes. These are found relevant and significant in terms of physiological interpretation of the associated shapes of pulse waveforms. Importance of these findings is highlighted along with discussion on overall scope of our study in automatic analysis of heart rate variability and in other applications for non-invasive prognosis/diagnosis.
