scholarly journals Correction to: Pulse waveform analysis of optic nerve head circulation for predicting carotid atherosclerotic changes

Author(s):  
Rina Muramatsu ◽  
Tomoaki Shiba ◽  
Mao Takahashi ◽  
Yuichi Hori ◽  
Takatoshi Maeno
2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Tatsuhiko Kobayashi ◽  
Tomoaki Shiba ◽  
Yuji Nishiwaki ◽  
Ayako Kinoshita ◽  
Tadashi Matsumoto ◽  
...  

AbstractThe influences of age and gender differences on the pulse waveform in the optic nerve head (ONH) in healthy adults, using laser speckle flowgraphy (LSFG) were evaluated. We studied 908 healthy subjects (men = 701, age: 50.0 ± 9.1, women = 208, age: 49.8 ± 9.5, p = 0.76), evaluating these pulse waveform parameters: the blowout score (BOS), blowout time (BOT), acceleration time index (ATI), and the rising and falling rates. The parameters were analyzed separately for the tissue, vessels, and throughout the optic nerve head (All). All parameters were compared between genders. We investigated which independent factors for the pulse waveform in the ONH is most strongly correlated with age. All sections of the BOS, BOT, ATI, and falling rate showed a significant gender difference. A univariate regression analysis revealed that BOT-Tissue showed the strongest correlation with age (r = −0.51). The factors contributing independently to the BOT-Tissue were gender, age, heart rate, mean arterial blood pressure, pulse pressure, spherical refraction, and estimated glomerular filtration rate. Among the subjects aged >41 years, the chronological changes of BOT-Tissue in the women were significantly lower than those in the men. We concluded that the pulse waveform in the ONH has clear differences between the genders and shows chronological changes.


2003 ◽  
Vol 44 (11) ◽  
pp. 4864 ◽  
Author(s):  
Yasuhiro Tamaki ◽  
Makoto Araie ◽  
Yasuhiro Fukaya ◽  
Miyuki Nagahara ◽  
Asuka Imamura ◽  
...  

2009 ◽  
Vol 21 (02) ◽  
pp. 139-147 ◽  
Author(s):  
Shing-Hong Liu ◽  
Kang-Ming Chang ◽  
Chu-Chang Tyan

The purpose of this study is to build an automatic disease classification algorithm by pulse waveform analysis, based on a Fuzzy C-means clustering algorithm. A self designed three-axis mechanism was used to detect the optimal position to accurately measure the pressure pulse waveform (PPW). Considering the artery as a cylinder, the sensor should detect the PPW with the lowest possible distortion, and hence an analysis of the vascular geometry and an arterial model were used to design a standard positioning procedure based on the arterial diameter changed waveform for the X-axes (perpendicular to the forearm) and Z-axes (perpendicular to the radial artery). A fuzzy C-means algorithm was used to estimate the myocardial ischemia symptoms in 35 elderly subjects with the PPW of the radial artery. Two type parameters were used to make the features, one was a harmonic value of Fourier transfer, and the other was a form factor value. A receiver operating characteristics curve was used to determine the optimal decision function. The harmonic feature vector contain second, third and fourth harmonics ( H 2, H 3, H 4) performed at the level of 69% for sensitivity and 100% for specificity while the form factor feature vector derived from left hand (LFF) and right hand (RFF) performed at the level of 100% for sensitivity and 53% for specificity. The FCM- and ROC-based clustering approach may become an efficient alternative for distinguishing patients in the risk of myocardial ischemia, besides the traditional exercise ECG examination.


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