Introduction to Complex Systems Analysis with Wavelets

Vanello, Nicola; Landini, Luigi

doi:10.1007/978-3-319-58709-7_5

Cited by 1 publication

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, 20-cycle digital volume pulse waveforms with ensemble averaging were adopted in Table 3 for DTFS coefficient (C 0 -C 10 ) computation. More importantly, HA [6,7], wavelet transformation [4,35] (both linear models), and ensemble empirical mode decomposition [5] (a non-linear model) are three popular signal decomposition methods for biomedical signal analysis, which result in orthogonal harmonic components, orthogonal wavelet details, and orthogonal intrinsic mode function outputs, respectively. A reasonable inference would be that more stable and shorter peripheral volume pulses suggest significant improvements in HA computation for assessing the averaged total pulse energy in elderly and diabetic subjects, which is a potentially important issue when using an ensemble averaging process based on the R-wave peak location (as in Step 1 in Section 2.…”

Section: Discussionmentioning

confidence: 99%

A First Step towards a Comprehensive Approach to Harmonic Analysis of Synchronous Peripheral Volume Pulses: A Proof-of-Concept Study

Haryadi

Chen

2021

JPM

View full text Add to dashboard Cite

The harmonic analysis (HA) of arterial radial pulses in humans has been widely investigated in recent years for clinical applications of traditional Chinese medicine. This study aimed at establishing the validity of carrying out HA on synchronous peripheral volume pulses for predicting diabetes-induced subtle changes in heart energy. In this study, 141 subjects (Group 1: 63 healthy elderly subjects; Group 2: 78 diabetic subjects) were enrolled at the same hospital. After routine blood sampling, all synchronous electrocardiogram (ECG) and photoplethysmography (PPG) measurements (i.e., at the six locations) were acquired in the morning. HA of synchronous peripheral volume pulses and radial pulse waves was performed and analyzed after a short period of an ensemble averaging process based on the R-wave peak location. This study utilized HA for the peripheral volume pulses and found that the averaged total pulse energy (i.e., the C0 of the DTFS) was identical in the same subject. A logistic regression model with C0 and a waist circumference variable showed a graded association with the risk of developing type 2 diabetes. The adjusted odds ratio for C0 and the waist circumference were 0.986 (95% confidence interval: 0.977, 0.994) and 1.130 (95% confidence interval: 1.045, 1.222), respectively. C0 also showed significant negative correlations with risk factors for type 2 diabetes mellitus, including glycosylated hemoglobin and fasting plasma glucose (r = −0.438, p < 0.001; r = −0.358, p < 0.001, respectively). This study established a new application of harmonic analysis in synchronous peripheral volume pulses for clinical applications. The findings showed that the C0 could be used as a prognostic indicator of a protective factor for predicting type 2 diabetes.

show abstract