The importance of pulsatile microcirculation in relation to hypertension

Jan, Ming-Yie; Hsiu, Hsin; Hsu, Tse-Lin; Wang, Yuh‐Ying Lin; Wang, Wei‐Kung

doi:10.1109/51.844389

Cited by 23 publications

(13 citation statements)

References 8 publications

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“…The time lag between pressure and LDF signals is calculated by using the pressure pulse as a self-trigger. We have previously demonstrated that blood pressure and microcirculation flux fluctuations are coherent, with the phase difference between these quantities being restricted to a small range (Jan et al 2000). Seki made similar observations in distal arterioles (Seki et al 2004).…”

Section: Introductionmentioning

confidence: 63%

Evaluating microcirculation by pulsatile laser Doppler signal

Chao¹,

Jan²,

Hsiu³

et al. 2006

Phys. Med. Biol.

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Laser Doppler flowmetry (LDF) is a popular method for monitoring the microcirculation, but it does not provide absolute measurements. Instead, the mean flux response or energy distribution in the frequency domain is generally compared before and after stimulus. Using the heartbeat as a trigger, we investigated whether the relation between pressure and flux can be used to discriminate different microcirculatory conditions. We propose the following three pulsatile indices for evaluating the microcirculation condition from the normalized pressure and flux segment with a synchronized-averaging method: peak delay time (PDT), pressure rise time and flux rise time (FRT). The abdominal aortic blood pressure and renal cortex flux (RCF) signals were measured in spontaneously hypertensive rats (SHR) and Wistar Kyoto rats (WKY). The mean value of the RCF did not differ between SHR and WKY. However, the PDT was longer in SHR (87.14 +/- 5.54 ms, mean +/- SD) than in WKY (76.92 +/- 2.62 ms; p < 0.001). The FRT was also longer in SHR (66.56 +/- 1.98 ms) than in WKY (58.02 +/- 1.77 ms; p < 0.001). We propose that a new dimension for comparing the LDF signals, which the results from the present study show, can be used to discriminate RCF signals that cannot be discriminated using traditional methods.

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Section: Introductionmentioning

confidence: 63%

Evaluating microcirculation by pulsatile laser Doppler signal

Chao¹,

Jan²,

Hsiu³

et al. 2006

Phys. Med. Biol.

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“…The pulse spectrum analysis method used in the present study was developed by Wang in accordance with organ resonance theory [6, 20]. Wang proposed that the pulse signals measured from the radial artery in the wrist are composed of several harmonics.…”

Section: Discussionmentioning

confidence: 99%

Applying Pulse Spectrum Analysis to Facilitate the Diagnosis of Coronary Artery Disease

Huang

Chang

Cheng

et al. 2019

Evidence-Based Complementary and Alternative Medicine

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Not all patients with angina pectoris have coronary artery stenosis. To facilitate the diagnosis of coronary artery disease (CAD), we sought to identify predictive factors of pulse spectrum analysis, which was developed by Wang and is one technique of modern pulse diagnosis. The patients suffered from chest pain and received cardiac catheterization to confirm the CAD diagnosis and Gensini score were recruited. Their pulse waves of radial artery were recorded. Then, by performing a fast Fourier transform, 10 amplitude values of frequency spectrum harmonics were obtained. Each harmonic amplitude was divided by the sum of all harmonic amplitude values, obtaining the relative percentages of 10 harmonics (C1-C10). Subsequently, multivariate logistic regression was conducted with two models and the areas under the receiver operating characteristic curves (ROC) of these 2 models were compared to see if combining the pulse diagnosis parameters with the risk factor of CAD can increase the prediction rate of CAD diagnosis. The predictive factors of CAD severity were analyzed by multivariate linear regression. A total of 83 participants were included; 63 were diagnosed CAD and 20 without CAD. In the CAD group, C1 was greater and C5 was lower than those of the non-CAD group. The CAD risk factors were put alone in Model 1 to perform the multivariate logistic regression analysis which had a prediction rate of 77.1%; while putting the C1 and C5 harmonics together with the risk factors into Model 2, the prediction rate increased to 80.7%. Finally, the area under ROC of Model 1 and Model 2 was 0.788 and 0.856, respectively. Furthermore, left C1, left C5, gender, and presence of hyperlipidemia were predictors of CAD severity. Therefore, pulse spectrum analysis may be a tool to facilitate CAD diagnosis before receiving cardiac catheterization. The harmonics C1 and C5 were favorable predictive indicators.

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“…It can be appreciated the peaks of LDF signal appeared synchronously correlated with the R-peaks of ECG signal after a certain phase delay. In order to determine the length of the LDF signal of each beat, the “beat-to-beat” algorithm was applied [15–17]. According to this algorithm, the “waveform” of LDF signals in heart-beat frequency was derived.…”

Section: Methodsmentioning

confidence: 99%

A Novel Assessment of Flexibility by Microcirculatory Signals

Bau

Chia

Chung

et al. 2013

Sensors

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Flexibility testing is one of the most important fitness assessments. It is generally evaluated by measuring the range of motion (RoM) of body segments around a joint center. This study presents a novel assessment of flexibility in the microcirculatory aspect. Eighteen college students were recruited for the flexibility assessment. The flexibility of the leg was defined according to the angle of active ankle dorsiflexion measured by goniometry. Six legs were excluded, and the remaining thirty legs were categorized into two groups, group H (n = 15 with higher flexibility) and group L (n = 15 with lower flexibility), according to their RoM. The microcirculatory signals of the gastrocnemius muscle on the belly were monitored by using Laser-Doppler Flowmetry (LDF) with a noninvasive skin probe. Three indices of nonpulsatile component (DC), pulsatile component (AC) and perfusion pulsatility (PP) were defined from the LDF signals after signal processing. The results revealed that both the DC and AC values of the group H that demonstrated higher stability underwent muscle stretching. In contrast, these indices of group L had interferences and became unstable during muscle stretching. The PP value of group H was a little higher than that of group L. These primary findings help us to understand the microcirculatory physiology of flexibility, and warrant further investigations for use of non-invasive LDF techniques in the assessment of flexibility.

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The importance of pulsatile microcirculation in relation to hypertension

Cited by 23 publications

References 8 publications

Evaluating microcirculation by pulsatile laser Doppler signal

Evaluating microcirculation by pulsatile laser Doppler signal

Applying Pulse Spectrum Analysis to Facilitate the Diagnosis of Coronary Artery Disease

A Novel Assessment of Flexibility by Microcirculatory Signals

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