A Novel Classification Technique of Arteriovenous Fistula Stenosis Evaluation Using Bilateral PPG Analysis

Du, Yi; Stephanus, Alphin

doi:10.3390/mi7090147

Cited by 25 publications

(20 citation statements)

References 18 publications

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“…The detailed description of the term used in this study can be seen in the Figure 3 . Normalization

should be done to get the same range on each data (RS and FS) or known as rescaling [ 14 , 27 ]; in this study the data was rescaled from 0 to 1 utilizing Equation (3):

…”

Section: Methodsmentioning

confidence: 99%

“…In clinical research definition, the DOS was a degree index of narrowing normal vessel of arteriovenous fistula and it measured by B-mode ultrasound or angiography images. In the previous research [ 8 , 14 , 29 ] to grade the vascular disease, severe DOS was the main reference and the equation shows as below:

…”

Section: Methodsmentioning

confidence: 99%

“…Deformation of each PPG pulse shape was important parameter to classify the degree of stenosis (DOS) in HD patients. In the previous study, the bilateral difference in PPG pulse shape between two hands was also associated with the stenosis [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

Stephanus

2018

Sensors

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This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for detecting abnormal narrowing vessel simultaneously in multi-beds monitoring patients. The mean and variance of Rising Slope (RS) and Falling Slope (FS) values between before and after HD treatment was used as the major features to classify AVF stenosis. Multilayer perceptron neural networks (MLPN) training algorithms are implemented for this analysis, which are the Levenberg-Marquardt, Scaled Conjugate Gradient, and Resilient Back-propagation, to identify the degree of HD patient stenosis. Eleven patients were recruited with mean age of 77 ± 10.8 years for analysis. The experimental results indicated that the variance of RS in the HD hand between before and after treatment was significant difference statistically to stenosis (p < 0.05). Levenberg-Marquardt algorithm (LMA) was significantly outperforms the other training algorithm. The classification accuracy and precision reached 94.82% and 92.22% respectively, thus this technique has a potential contribution to the early identification of stenosis for a medical diagnostic support system.

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“…The detailed description of the term used in this study can be seen in the Figure 3 . Normalization

should be done to get the same range on each data (RS and FS) or known as rescaling [ 14 , 27 ]; in this study the data was rescaled from 0 to 1 utilizing Equation (3):

…”

Section: Methodsmentioning

confidence: 99%

…”

Section: Methodsmentioning

confidence: 99%

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Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

Stephanus

2018

Sensors

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“…The fistula type was automatically encircled to calculate the degree of stenosis (DOS) according to Equation (1) to help doctors understand the patient’s fistula occlusion situation. The schematic diagram of the DOS in AVF was shown in Figure 2 [ 19 ].

…”

Section: Methodsmentioning

confidence: 99%

Development of an AVF Stenosis Assessment Tool for Hemodialysis Patients Using Robotic Ultrasound System

Shih

et al. 2018

Micromachines

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With the aging population and lifestyle changes, the number of hemodialysis (HD) patients increases year by year. The arteriovenous fistula (AVF) is the gold standard vascular access used to access the blood for HD treatment. Since the status of stenosis affects HD efficiency, current clinical practices usually use a Doppler ultrasound imaging system to assess the parameters of the stenosis, such as the degree of stenosis (DOS). Unfortunately, this is a very time-consuming task. Furthermore, it is difficult to stably maintain the ultrasound probe for a prolonged period to give doctors clearer or reproducible images. In this study, a robotic ultrasound system (RUS) with ultrasound sequential imaging analysis was designed to evaluate the DOS of the AVF. The sequential imaging analysis was capable of image smoothing and vessel boundary detection. It enabled clinicians to mark the thickness of the plaque for further processing. Finally, the system was used to reconstruct 3D models of fistulas and calculated the DOS for clinical assessment. We also designed a pressure sensing module attached to the ultrasound probe to prevent the probe from coming loose, vibrating, and exerting abnormal pressure on the skin. In the phantom test, the results showed that the error of the DOS that was calculated by RUS was less than 3%. The results of clinical trials obtained from ten patients show that the error between the RUS and clinicians’ measurement was about 10% and had a highly linear correlation (R Square > 0.95). In addition, the reproducibility error was about 3% and could effectively save about 46% of the time during clinical examinations.

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“…The measurement of a biosignal using PPG is noninvasive, and it is possible to measure various signals including respiration, heart rate, vasomotor activity, and saturation by pulse oximetry (SPO 2 ) using just one probe [ 1 – 4 ]. PPG can be used to evaluate atherosclerosis [ 5 , 6 ], arterial stenosis [ 7 – 10 ], arterial properties [ 2 , 11 – 13 ], hypertension [ 10 , 12 ], diabetes mellitus [ 14 ], and cardiovascular risk factors [ 15 ]. PPG has been widely used to study cardiovascular function.…”

Section: Introductionmentioning

confidence: 99%

Identification of Cerebral Artery Stenosis Using Bilateral Photoplethysmography

Kang

Lee

Ryu³

et al. 2018

Journal of Healthcare Engineering

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Cerebral artery stenosis is currently diagnosed by transcranial Doppler (TCD), computed tomographic angiography (CTA), or magnetic resonance angiography (MRA). CTA exposes a patient to radiation, while CTA and MRA are invasive and side effects were related to contrast medium use. This study aims to provide a technique that can simply discriminate between people with normal blood vessels and those with cerebral artery stenosis using photoplethysmography (PPG), which is noninvasive and inexpensive. Moreover, the measurement takes only 120 seconds and is conducted on the fingers. The technique projects the light of a specific wavelength and analyzes the pulse waves which are generated when the blood passes through the blood vessels according to one's heartbeat using the transmitted light. Normalization was performed after dividing the extracted pulse waveform into windows, and maximum positive and negative amplitudes (MPA, MNA) were extracted from the detected pulse waves as features. The extracted features were used to identify normal subjects and those with cerebral artery stenosis using a linear discriminant analysis. The study results showed that the recognition rate using MPA was 92.2%, MNA was 90.6%, and combined MPA + MNA was 90.6%. The technique proposed is expected to detect early stage asymptomatic cerebral artery stenosis and help prevent ischemic stroke.

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A Novel Classification Technique of Arteriovenous Fistula Stenosis Evaluation Using Bilateral PPG Analysis

Cited by 25 publications

References 18 publications

Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

Development of an AVF Stenosis Assessment Tool for Hemodialysis Patients Using Robotic Ultrasound System

Identification of Cerebral Artery Stenosis Using Bilateral Photoplethysmography

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