PULSE WAVE ANALYSIS USING TONE-ENTROPY ALGORITHM IN PEOPLE WITH AND WITHOUT FOOT COMPLAINTS IN A RURAL DIABETES SCREENING CLINIC

2010 ◽  
Author(s):  
Herbert Franz Jelinek ◽  
Ahsan H Khandoker ◽  
Mohammed Hasan Imam
Keyword(s):  
Pulse Wave ◽  
Pulse Wave Analysis ◽  
Diabetes Screening ◽  
Wave Analysis ◽  
Screening Clinic

scholarly journals Pulse wave analysis and prevalent cardiovascular disease in type 1 diabetes

2010 ◽  
Vol 213 (2) ◽  
pp. 469-474 ◽  
Author(s):  
Catherine T. Prince ◽  
Aaron M. Secrest ◽  
Rachel H. Mackey ◽  
Vincent C. Arena ◽  
Lawrence A. Kingsley ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Type 1 Diabetes ◽  
Pulse Wave ◽  
Pulse Wave Analysis ◽  
Wave Analysis

Pulse wave analysis of the aortic pressure waveform in patients with vasovagal syncope

2014 ◽  
Vol 31 (1) ◽  
pp. 74-79 ◽  
Author(s):  
Simon Pecha ◽  
Samer Hakmi ◽  
Iris Wilke ◽  
Yalin Yildirim ◽  
Boris Hoffmann ◽  
...  
Keyword(s):  
Pulse Wave ◽  
Vasovagal Syncope ◽  
Aortic Pressure ◽  
Pulse Wave Analysis ◽  
Pressure Waveform ◽  
Wave Analysis

Photoplethysmography beyond perfusion and oxygenation monitoring: pulse wave analysis for hepatic graft monitoring

2014 ◽  
Author(s):  
Tony J. Akl ◽  
Mark A. Wilson ◽  
M. Nance Ericson ◽  
Gerard L. Coté
Keyword(s):  
Pulse Wave ◽  
Pulse Wave Analysis ◽  
Wave Analysis ◽  
Oxygenation Monitoring

Abstract P116: VARIATION IN MEAN ARTERIAL PRESSURE (MAP) AND ITS HEMODYNAMIC COMPONENTS DURING 24-HOUR AMBULATORY PULSE WAVE ANALYSIS: IMPACT OF AGE, RACE, GENDER AND BODY SIZE

Hypertension ◽  
2021 ◽  
Vol 78 (Suppl_1) ◽  
Author(s):  
Sagar Nagpal ◽  
Debduti Mukhopadhyay ◽  
Peter Osmond ◽  
Joseph E Schwartz ◽  
Joseph L Izzo
Keyword(s):  
Heart Rate ◽  
Pulse Wave ◽  
Beta Blockers ◽  
Demographic Characteristics ◽  
Pulse Wave Analysis ◽  
Clinical Implications ◽  
Wave Analysis ◽  
Contributing Factors ◽  
Clinical Associations ◽  
The Impact

BP is highly variable within and between individuals but the impact of variation in underlying hemodynamic components is unknown. We tested the feasibility and clinical associations of quantitated variances in MAP and its hemodynamic components [heart rate (HR), stroke volume (SV) and total vascular resistance (TVR)] obtained by 24-hr ambulatory pulse wave analysis (PWA, Mobil-O-Graph, IEM, Stolzberg, DE). BP and PWA were measured every 20 min for 24 hrs. Indexed to body surface area, MAP = HR*[SV index (SVI)]*[TVR index (TVRI)]; ln(MAP) = ln(HR) + ln(SVI) + ln(TVRI); and total MAP variability = var [ln(MAP)] = covariance (cov)[ln(HR), ln(MAP)] + cov[ln(SVI), ln(MAP)] + cov[ln(TVRI), ln(MAP)]. Relative contributions to var[ln(MAP)] for each hemodynamic component (as %) were calculated and associations with demographic characteristics were analyzed by correlations and t-tests. We studied 152 people (49% women, 23% black); mean(SD): # readings 57(11), age 59(16) yr, BMI 29.9(6.5) kg/m 2 , systolic BP 135(18) and MAP 106(14) mmHg. Mean(SD) 24-hr values were: ln(MAP) 4.64 (0.13), ln(HR) 4.20 (0.15), ln(SVI) -3.32 (0.15), and ln(TVRI) 3.75 (0.18). Relative contributions of hemodynamic components to total 24-hr ln(MAP) variation were: TVRI 54(36)%, HR 33(38)%, and SVI 13(40)%. The large SDs of these relative contributions led to analysis of potential contributing factors: TVRI contribution was correlated with 24-hr mean MAP (r=0.24, p=0.003) and was higher (>54%) in males (p=0.03) and blacks (p=0.04); HR contribution was inversely related to MAP (r=-0.26, p=0.001), age (r=-0.29, p=0.0003) and BMI (r=-0.173 p=0.05) and was lower (<33%) in blacks (p=0.008); SVI contribution was correlated with age (r=0.31, p<0.0001) and BMI (r=0.23, p=0.005) and was higher (>13%) in women (p=0.03). We conclude that 24-hr ambulatory PWA can identify components of MAP variation within individuals and their associations with demographic factors. The relative contributions of hemodynamic components (HR, SV, TVR) to 24-hr variability in ln(MAP) varies systematically with 24-hr mean MAP, age, race, gender, and BMI. Theoretical clinical implications may include therapeutic adjustments for extremes of variation in HR (beta-blockers), TVR (vasodilators) or SV (diuretics).

Pulse Wave Analysis to Estimate Cardiac Output: Comment

2021 ◽  
Author(s):  
Scott Hughey ◽  
Jacob Cole ◽  
Gregory Booth
Keyword(s):  
Cardiac Output ◽  
Pulse Wave ◽  
Pulse Wave Analysis ◽  
Wave Analysis

Pulse Wave Analysis During Sleep

2017 ◽  
pp. 1624-1632.e4 ◽  
Author(s):  
Ludger Grote ◽  
Ding Zou
Keyword(s):  
Pulse Wave ◽  
Pulse Wave Analysis ◽  
Wave Analysis

scholarly journals Associations between Intrinsic Heart Rate, P Wave and QT Interval Durations and Pulse Wave Analysis in Patients with Hypertension and High Normal Blood Pressure

2020 ◽  
Vol 17 (12) ◽  
pp. 4350
Author(s):  
Ioana Mozos ◽  
Cristina Gug ◽  
Costin Mozos ◽  
Dana Stoian ◽  
Marius Pricop ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Qt Interval ◽  
Pulse Wave ◽  
Pulse Wave Analysis ◽  
P Wave ◽  
Normal Blood Pressure ◽  
Wave Analysis ◽  
Wave Duration ◽  
P Wave Duration

The present study aimed to explore the relationship between electrocardiographic (ECG) and pulse wave analysis variables in patients with hypertension (HT) and high normal blood pressure (HNBP). A total of 56 consecutive, middle-aged hypertensive and HNBP patients underwent pulse wave analysis and standard 12-lead ECG. Pulse wave velocity (PWV), heart rate, intrinsic heart rate (IHR), P wave and QT interval durations were as follows: 7.26 ± 0.69 m/s, 69 ± 11 beats/minute, 91 ± 3 beats/minute, 105 ± 22 mm and 409 ± 64 mm, respectively. Significant correlations were obtained between PWV and IHR and P wave duration, respectively, between early vascular aging (EVA) and P wave and QT interval durations, respectively. Linear regression analysis revealed significant associations between ECG and pulse wave analysis variables but multiple regression analysis revealed only IHR as an independent predictor of PWV, even after adjusting for blood pressure variables and therapy. Receiver-operating characteristic (ROC) curve analysis revealed P wave duration (area under curve (AUC) = 0.731; 95% CI: 0.569–0.893) as a predictor of pathological PWV, and P wave and QT interval durations were found as sensitive and specific predictors of EVA. ECG provides information about PWV and EVA in patients with HT and HNBP. IHR and P wave durations are independent predictors of PWV, and P wave and QT interval may predict EVA.

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