Not only does erectile dysfunction (ED) reflect penile vascular disorder in the majority of patients, but it also implicates their high systemic cardiovascular risk. Based on the principle of reactive hyperemia after a brief period of penile ischemia, in this study, we tested the validity of a new Penile Arterial Waveform Analyzer (PAWA) in assessing the relative increase in post-ischemic penile perfusion. Twenty young adult males (mean age 24.24 ± 2.45) without known history of cardiovascular diseases were recruited, whose anthropometric characteristics were recorded and their serum testosterone levels as well as biochemical profiles were determined. A penile cuff was applied to each subject, with cuff pressure being increased from 80 to 250 mmHg, each for 4 min, followed by reperfusion for 7 min. By dividing the area under waveform contour of hyperemic and baseline signals after Ensemble Empirical Mode Decomposition (EEMD), a Penile Perfusion Index (PPI) was calculated. Penile Brachial Index (PBI) was also obtained for comparison. The results not only showed a significant agreement between PPI and serum testosterone levels, but also a superiority of PPI to PBI in distinguishing the high- and low-risk groups for potential ED (PPI: p = 0.039 vs. PBI: p = 0.147). PPI was also demonstrated to show significant correlations with waist circumference (p < 0.001), body mass index (p = 0.005), body weight, total triglyceride, high-density lipoprotein, and systolic and diastolic pressures (all p < 0.05). In conclusion, we proposed a portable and easy-to-operate system in assessing the relative increase in penile perfusion after brief ischemia. The PPI thus obtained correlated significantly with serum testosterone levels as well as key anthropometric and serum biochemical parameters even in apparently healthy young adults, suggesting its potential as a sensitive tool in monitoring penile vascular function and risk for ED.
This is a preview of subscription content, log in via an institution to check access.
Access this article Subscribe and saveSpringer+ Basic
€34.99 /Month
Price includes VAT (Germany)
Instant access to the full article PDF.
Similar content being viewed by others Explore related subjectsDiscover the latest articles and news from researchers in related subjects, suggested using machine learning. ReferencesAlberti, K. G., and P. Z. Zimmet. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet. Med. 15(7):539–553, 1998.
Ambegaonkar, B., D. Chirovsky, W. Wu, H. Colclough, G. Milligan, and V. Sazonov. The effects of isolated versus multiple lipid disorders on resource utilization among metabolic syndrome patients with lipid abnormalities despite lipid-modifying treatment. Cardiology 117(2):96–104, 2010.
Balacco, G. C., F. Regine, E. M. Vingolo, E. Rispoli, and A. Isidori. Acute electroretinographic changes during Sildenafil (Viagra) treatment for erectile dysfunction. Doc. Ophthalmol. 107(2):111–114, 2003.
Basar, M. M., A. Atan, and U. Y. Tekdogan. New concept parameters of RigiScan in differentiation of vascular erectile dysfunction: is it a useful test? Int. J. Urol. 8(12):686–691, 2001.
Billups, K. L. Endothelial dysfunction as a common link between erectile dysfunction and cardiovascular disease. Curr. Sex. Health Rep. 1(4):137–141, 2004.
Billups, K. L. Erectile dysfunction as an early sign of cardiovascular disease. Int. J. Impot. Res. 17(1):19–24, 2005.
Bonetti, P. O., G. W. Barsness, P. C. Keelan, T. I. Schnell, G. M. Pumper, J. T. Kuvin, R. P. Schnall, D. R. Holmes, S. T. Higano, and A. Lerman. Enhanced external counterpulsation improves endothelial function in patients with symptomatic coronary artery disease. J. Am. Coll. Cardiol. 41(10):1761–1768, 2003.
Bonetti, P. O., G. M. Pumper, S. T. Higano, D. R. Holmes, Jr., J. T. Kuvin, and A. Lerman. Noninvasive identification of patients with early coronary atherosclerosis by assessment of digital reactive hyperemia. J. Am. Coll. Cardiol. 44(11):2137–2141, 2004.
Bradley, W. E., G. W. Timm, J. M. Gallagher, and B. K. Johnson. New method for continuous measurements of nocturnal penile tumescence and rigidity. Urology 26(1):4–9, 1985.
Britt, D. B., W. T. Kemmerer, and J. R. Bobison. Penile blood flow determination by mercury strain gauge plethysmogrphy. Invest. Urol. 8:673–678, 1971.
Chen, J. Y., W. C. Tsai, M. S. Wu, C. H. Hsu, C. C. Lin, H. T. Wu, L. J. Lin, and J. H. Chen. Novel Compliance Index derived from digital volume pulse associated with risk factors and exercise capacity in patients undergoing treadmill exercise tests. J. Hypertens. 25(9):1894–1899, 2007.
Christ, F., I. Nehring, J. Abicht, V. Baranov, A. Kotov, I. Gartside, J. Gamble, and K. Messmer. Changes in the arteriolar volume pulse of the finger during various degrees of tilt using near infra-red and red photoplethysmography. Eur. J. Med. Res. 3(5):249–255, 1998.
Dean, J. Characterisation, prevalence, and consultation rates of erectile dysfunction. Clin. Cornerstone 7(1):5–11, 2005.
Ellenberg, M. Impotence in diabetes: the neurologic factor. Ann. Intern. Med. 75(2):213–219, 1971.
Ellenberg, M. Sexual function in diabetic patients. Ann. Intern. Med. 92(2 Pt 2):331–333, 1980.
Elliott, B. M., G. J. Collins, Jr., J. R. Youkey, J. M. Salander, and N. M. Rich. The noninvasive diagnosis of vasculogenic impotence. J. Vasc. Surg. 3(3):493–497, 1986.
Halls, J., G. Bydawell, and U. Patel. Erectile dysfunction: the role of penile Doppler ultrasound in diagnosis. Abdom. Imaging 34(6):712–725, 2009.
Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. R. Soc. Lond. 454:903–995, 1998.
Huang, N. E., Z. Shen, and S. R. Long. A new view of nonlinear water waves: the Hilbert spectrum. Annu. Rev. Fluid Mech. 31:417–457, 1999.
Huang, N. E., M. C. Wu, S. R. Long, S. P. Shen, W. Qu, P. Gloersen, and K. L. Fan. A confidence limit for the empirical mode decomposition and the Hilbert spectral analysis. Proc. R. Soc. Lond. A 459:2317–2345, 2003.
Jankowski, J. T., A. D. Seftel, and K. P. Strohl. Erectile dysfunction and sleep related disorders. J. Urol. 179(3):837–841, 2008.
Klingler, H. C., C. Kratzik, A. Pycha, and M. Marberger. Value of power Doppler sonography in the investigation of erectile dysfunction. Eur. Urol. 36(4):320–326, 1999.
Kubin, M., G. Wagner, and A. R. Fugl-Meyer. Epidemiology of erectile dysfunction. Int. J. Impot. Res. 15(1):63–71, 2003.
Levine, L. A., and R. A. Carroll. Nocturnal penile tumescence and rigidity in men without complaints of erectile dysfunction using a new quantitative analysis software. J. Urol. 152(4):1103–1107, 1994.
Licht, M. R. Use of oral sildenafil (Viagra) in the treatment of erectile dysfunction. Compr. Ther. 25(2):90–94, 1999.
Lo, M. T., P. H. Tsai, P. F. Lin, C. Lin, and Y. L. Hsin. The nonlinear and nonstationary properties in EEG signals: probing the complex fluctuations by Hilbert–Huang Transform. Adv. Adapt. Data Anal. 1(3):461–482, 2009.
Niklas, M., U. Moser, A. Buehrer, R. Valentin, J. Abicht, H. Baschnegger, and F. Christ. Attenuation of the near-infrared and red photoplethysmographic signal by different depths of tissues. Eur. J. Med. Res. 3(5):241–248, 1998.
Roumeguère, T., E. Wespes, Y. Carpentier, P. Hoffmann, and C. C. Schulman. Erectile dysfunction is associated with a high prevalence of hyperlipidemia and coronary heart disease risk. Eur. Urol. 44(3):355–359, 2003.
Ryder, R. E., C. F. Close, K. T. Moriarty, K. T. Moore, and C. A. Hardisty. Impotence in diabetes: aetiology, implications for treatment and preferred vacuum device. Diabet. Med. 9(10):893–898, 1992.
Solomon, H., J. W. Man, and G. Jackson. Erectile dysfunction and the cardiovascular patient: endothelial dysfunction is the common denominator. Heart 89(3):251–254, 2003.
Sullivan, M. E., S. R. Keoghane, and M. A. W. Miller. Vascular risk factors and erectile dysfunction. Br. J. Urol. Int. 87(9):838–845, 2001.
Tardy, Y., J. J. Meister, F. Perret, H. R. Brunner, and M. Arditi. Non-invasive estimate of the mechanical properties of peripheral arteries from ultrasonic and photoplethysmographic measurements. Clin. Phys. Physiol. Meas. 12(1):39–54, 1991.
Toth, P. P. High-density lipoprotein and cardiovascular risk. Circulation 109(15):1809–1812, 2004.
Unno, N., K. Inuzuka, H. Mitsuoka, K. Ishimaru, D. Sagara, and H. Konno. Automated bedside measurement of penile blood flow using pulse-volume plethysmography. Surg. Today 36(3):257–261, 2006.
Wespes, E. The ageing penis. World J. Urol. 20(1):36–39, 2002.
Wu, Z., and N. E. Huang. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1:1–41, 2009.
Wu, H. T., C. H. Lee, and A. B. Liu. Assessment of endothelial function using arterial pressure signals. J. Signal Process. Syst. 64(2):223–232, 2011.
Wu, H. T., C. H. Lee, A. B. Liu, W. S. Chung, C. J. Tang, C. K. Sun, and H. K. Yip. Arterial stiffness using radial arterial waveforms measured at the wrist as an indicator of diabetic control in the elderly. IEEE Trans. Biomed. Eng. 58(2):243–252, 2011.
The authors would like to thank the Associate Editor, Professor Ioannis A. Kakadiaris, and the anonymous reviewers for their insightful comments and suggestions which have significantly contributed to the improvement of this study. This research was supported in part by grants from the National Science Council (NSC 98-2221-E-259-017 and NSC 99-2221-E-259-001), Taiwan, Republic of China. The authors would also like to thank Miss Shu-Mei Wen, who worked as Acting Head Nurse in the Outpatient Department of Hualien Hospital for her clinical support, and the volunteers involved in this study for allowing us to collect and analyze their data. The authors are also grateful to Texas Instruments, Taiwan, for sponsoring the low-power instrumentation amplifiers and ADC.
Author information Authors and AffiliationsDepartment of Electrical Engineering, National Dong Hwa University, No. 1, Sec. 2, Da-Hsueh Rd., Shoufeng, Hualien, 97401, Taiwan
Hsien-Tsai Wu & Chun-Ho Lee
Department of Urology, Hualien Hospital, Department of Health Executive Yuan, Hualien, 97061, Taiwan
Chin-Jung Chen
Department of Emergency Medicine, E-Da Hospital, I-Shou University, Kaohsiung, 82445, Taiwan
Cheuk-Kwan Sun
Correspondence to Hsien-Tsai Wu.
Additional informationAssociate Editor Ioannis A. Kakadiaris oversaw the review of this article.
Appendix Appendix Ensemble Empirical Mode Decomposition (EEMD)In general,
$$ x\left( t \right) = s\left( t \right) + n\left( t \right) $$
(A1)
where x(t) is the recorded data, and s(t) and n(t) are the true signal and white noises, respectively.
Step 1: Identify local extrema in the experimental data x(t). All the local maxima are connected by a cubic spline line x up(t), which forms the upper envelope of the data. Repeat the same procedure for the local minima to produce the lower envelope x low(t). Both envelopes will cover all the data between them. The mean of upper envelope and lower envelope m 1(t) is given by
$$ m_{1} \left( t \right) = \frac{{\left( {x_{\text{up}} \left( t \right) + x_{\text{low}} \left( t \right)} \right)}}{2}. $$
(A2)
Subtracting the running mean m 1(t) from the original time series x(t), we get the first component h 1(t);
$$ h_{1} \left( t \right) = x\left( t \right) - m_{1} \left( t \right). $$
(A3)
If h 1(t) is not an IMF, then the sifting process has to be repeated as many times as required to reduce the extracted signal to an IMF. Subsequently
$$ h_{11} \left( t \right) = h_{1} \left( t \right) - m_{11} \left( t \right). $$
(A4)
Through the iteration process (for a total of k times), the difference within the signal and the mean envelope values, which is denoted as h 1k (t), is obtained as
$$ h_{1k} \left( t \right) = h_{{1\left( {k - 1} \right)}} \left( t \right) - m_{1k} \left( t \right). $$
(A5)
Step 2: If the resulting time series is an IMF, then it is designated as c 1 = h 1k (t). The first IMF is then subtracted from the original data, and the difference r 1 given by
$$ r_{1} \left( t \right) = x\left( t \right) - c_{1} \left( t \right). $$
(A6)
The residue r 1(t) is taken as the original data, and we apply to it again the sifting process of Step 1. Adopting the same procedures for Step 1 and Step 2, we continue the process to find more intrinsic modes c i until the last one. The final residue will be a constant or a monotonic function which represents the general trend of the time series. Finally, we obtain
$$ x\left( t \right) = \sum\limits_{i = 1}^{n} {c_{i} \left( t \right) + r_{n} } $$
(A7)
$$ x\left( t \right) = IMF1 + IMF2 + \cdots + IMFn + r_{n}, $$
(A8)
$$ r_{i - 1} \left( t \right) - c_{i} \left( t \right) = r_{i} \left( t \right),\quad i = 2, \ldots ,n. $$
(A9)
The result of EEMD is obtained when the number in the ensemble approaches infinity:
$$ c_{i} \left( t \right) = \mathop {\lim }\limits_{N \to \infty } \frac{1}{N}\sum\limits_{k = 1}^{n} {\left\{ {c_{i} \left( t \right) + \alpha r_{k} \left( t \right)} \right\}} , $$
(A10)
where
$$ c_{i} \left( t \right) + \alpha r_{k} \left( t \right) $$
(A11)
is the kth realization of the ith IMF in the noise-added signal, α is the standard deviation of the added noise, and r k (t) is the residual after extracting the first k IMF components. The iteration time in the ensemble, N, has to be large. In this study, α is set to be 0.2, and N is equal to 200 for fast computing.20,36,38
The Hilbert SpectrumHaving obtained the IMF components, it is easy to apply the Hilbert transform to each component, and the instantaneous frequency can be computed. Therefore, the Hilbert spectrum of the signal x(t) can be expressed in the following form:
$$ H\left( {\omega ,t} \right) = \text{Re} \sum\limits_{i = 1}^{n} {a\left( t \right)e^{{j\int {\omega \left( t \right)dt} }} } $$
(A12)
where Re means taking the real part of the sum, a(t) indicates the ith instantaneous amplitude of the analysis signal, and ω(t) represents the ith instantaneous frequency. With the Hilbert spectrum defined, we can also define the marginal spectrum, h(ω), as
$$ h\left( \omega \right) = \int\limits_{0}^{T} {H\left( {\omega ,t} \right)dt.} $$
(A13)
About this article Cite this articleWu, HT., Lee, CH., Chen, CJ. et al. Penile Arterial Waveform Analyzer for Assessing Penile Vascular Function in Young Adults. Ann Biomed Eng 39, 2857–2868 (2011). https://doi.org/10.1007/s10439-011-0342-1
Received: 23 April 2011
Accepted: 16 June 2011
Published: 16 July 2011
Issue Date: November 2011
DOI: https://doi.org/10.1007/s10439-011-0342-1
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4