Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device

Chiang, Pei Yu; Chao, Paul C.-P.; Tu, Tse Yi; Kao, Yung Hua; Yang, Chi‐Rei; Tarng, Der Cherng; Wey, Chin‐Long

doi:10.3390/s19153422

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…The study results showed that SVM offered good effectiveness. Moreover, Chiang et al [25] used the data of a novel PPG and the classification method of SVM to evaluate the quality of dialysis patients’ AV fistula, involving the blood flow volume and degree of stenosis. The experimental data showed that with PPG data, the SVM-based prediction of fistula obstruction could reach a precision of 87.84% and the prediction of blood flow volume could reach a precision of 88.61%.…”

Section: Related Workmentioning

confidence: 99%

Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records

Hsieh

Hwang

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Intelligent models for predicting hemodialysis-related complications, i.e., hypotension and the deterioration of the quality or obstruction of the AV fistula, based on machine learning (ML) methods were established to offer early warnings to medical staff and give them enough time to provide pre-emptive treatment. Methods: A novel integration platform collected data from the Internet of Medical Things (IoMT) at a dialysis center and inspection results from electronic medical records (EMR) to train ML algorithms and build models. The selection of the feature parameters was implemented using Pearson's correlation method. Then, the eXtreme Gradient Boost (XGBoost) algorithm was chosen to create the predictive models and optimize the feature choice. 75% of collected data are used as a training dataset and the other 25% are used as a testing dataset. Results: We adopted the prediction precision and recall rate of hypotension and AV fistula obstruction to measure the effectiveness of the predictive models. These rates were sufficiently high at approximately 71%-90%. Conclusion: In the context of hemodialysis, hypotension and the deterioration of the quality or obstruction of the arteriovenous (AV) fistula affect treatment quality and patient safety and may lead to a poor prognosis. Our prediction models with high accuracies can provide excellent references and signals for clinical healthcare service providers. Clinical and Translational Impact Statement: With the integrated dataset collected from IoMT and EMR, the superior predictive results of our models for complications of hemodialysis patients are demonstrated. We believe, after enough clinical tests are implemented as planned, these models can assist the healthcare team in making appropriate preparations in advance or adjusting the medical procedures to avoid these adverse events.

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Section: Related Workmentioning

confidence: 99%

Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records

Hsieh

Hwang

et al. 2023

IEEE J. Transl. Eng. Health Med.

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“…The PPG signals are composed of two components: DC and alternating component (AC) as shown in Figure 2 [15]. The AC corresponds to the variation of blood volume in synchronization with heartbeat pulse, and it is used to measure heart rate.…”

Section: Photoplethysmographymentioning

confidence: 99%

Non-Contact Heart Rate Monitoring from Face Video Utilizing Color Intensity

Sahin¹,

Deng²,

Castelo³

et al. 2021

J Multimed Inf Syst

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Heart Rate is a crucial physiological parameter that provides basic information about the state of the human body in the cardiovascular system, as well as in medical diagnostics and fitness assessments. At present day, it has been demonstrated that facial video-based photoplethysmographic signal captured using a low-cost RGB camera is possible to retrieve remote heart rate. Traditional heart rate measurement is mostly obtained by direct contact with the human body, therefore, it can result inconvenient for long-term measurement due to the discomfort that it causes to the subject. In this paper, we propose a non-contact-based remote heart rate measuring approach of the subject which depends on the color intensity variation of the subject's facial skin. The proposed method is applied in two regions of the subject's face, forehead and cheeks. For this, three different algorithms are used to measure the heart rate. i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA). The average accuracy for the three algorithms utilizing the proposed method was 89.25% in both regions. It is also noteworthy that the FastICA algorithm showed a higher average accuracy of more than 92% in both regions. The proposed method obtained 1.94% higher average accuracy than the traditional method based on average color value.

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“…The AC pulse signal is superimposed on the DC signal, where more than 90% of the pulse amplitude is contributed by the DC component [58]. Both the AC and DC waveforms are extracted using suitable filters and amplifiers, and later the AC waveforms are used for subsequent pulse analysis in a software tool like Matlab.…”

Section: Working Principle Of the Ppg Sensormentioning

confidence: 99%

A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications

Kumar

Buckley

Barton

et al. 2020

Sensors

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A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented. The paper describes the platform in detail, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications. An example application using the developed platform is discussed for arterial oxygen saturation (SpO2) and heart rate measurement using optical photoplethysmography (PPG). A comparison of the wireless performance in the 868 MHz and the 2.45 GHz bands is performed. Another contribution of this work is the development of a highly integrated 868 MHz antenna. The antenna structure is printed on the surface of a wristwatch enclosure using laser direct structuring (LDS) technology. At 868 MHz, a low specific absorption rate (SAR) of less than 0.1% of the maximum permissible limit in the simulation is demonstrated. The measured on-body prototype antenna exhibits a −10 dB impedance bandwidth of 36 MHz, a peak realized gain of −4.86 dBi and a radiation efficiency of 14.53% at 868 MHz. To evaluate the performance of the developed 868 MHz sensor platform, the wireless communication range measurements are performed in an indoor environment and compared with a commercial Bluetooth wristwatch device.

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Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device

Cited by 12 publications

References 29 publications

Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records

Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records

Non-Contact Heart Rate Monitoring from Face Video Utilizing Color Intensity

A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications

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