A novel online method for identifying motion artifact and photoplethysmography signal reconstruction using artificial neural networks and adaptive neuro-fuzzy inference system

Tarvirdizadeh, Bahram; Golgouneh, Alireza; Tajdari, Farzam; Khodabakhshi, Erfan

doi:10.1007/s00521-018-3767-8

Cited by 26 publications

(10 citation statements)

References 41 publications

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“…Deep and machine learning and neural networks have also been used to classify the PPG signal. However, methods based on neural networks and fuzzy systems require training or self-tuning of adaptive parameters [127][128][129][130][131][132]. For example, a combined ECG/PPG signal within a nonlinear system, based on a reaction-diffusion mathematical model implemented using the cellular neural network (CNN) methodology, was employed to filter the PPG signal by assigning a recognition score to the waveforms in the time series [132].…”

Section: Motion Artefactsmentioning

confidence: 99%

Current progress of photoplethysmography and SPO2 for health monitoring

Tamura¹

2019

Biomed. Eng. Lett.

182

133

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A photoplethysmograph (PPG) is a simple medical device for monitoring blood flow and transportation of substances in the blood. It consists of a light source and a photodetector for measuring transmitted and reflected light signals. Clinically, PPGs are used to monitor the pulse rate, oxygen saturation, blood pressure, and blood vessel stiffness. Wearable unobtrusive PPG monitors are commercially available. Here, we review the principle issues and clinical applications of PPG for monitoring oxygen saturation.

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Section: Motion Artefactsmentioning

confidence: 99%

Current progress of photoplethysmography and SPO2 for health monitoring

Tamura¹

2019

Biomed. Eng. Lett.

182

133

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“…In this method, after eigen-decomposition is performed to extract the eigen components of PPG, PPG is restored only with the main components from which the noise components are removed. When most of the waveform information is lost because of severe distortion of PPG, detecting the damaged part and estimating the waveform of the corresponding part to restore it using a machine learning technique, such as recurrent neural network, have been reported ( Tarvirdizadeh et al, 2018 ; Roy et al, 2019 ). In addition to restoring distorted parts, reconstruction of the PPG waveform can be performed to enhance the waveform.…”

Section: Resultsmentioning

confidence: 99%

Photoplethysmogram Analysis and Applications: An Integrative Review

et al. 2022

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Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

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“…The functions are selected based on Tarvirdizadeh et al (2018). Therefore, in this problem, density functions of error state and torques are appropriate functions to describe the changes, which are elaborated as followswhere ρe¯ is density of state errors and ρ T is the torques’ density.…”

Section: Controller Designmentioning

confidence: 99%

“…Actually, equations (56) and (57) are sensitive when truee¯(k) and T ( k ) are sufficiently greater than average of the variables with window size ℜ. These criteria explain a smooth and logical stimulation to changes of the variables (Tarvirdizadeh et al, 2018). Consequently, the inputs of the intelligent estimator are ρe¯1×6 and ρT1×6, and the outputs are wQ1×1, and wR1×1 as shown in Figure 4.…”

Section: Controller Designmentioning

confidence: 99%

Implementation and intelligent gain tuning feedback–based optimal torque control of a rotary parallel robot

Tajdari

Toulkani

2021

Journal of Vibration and Control

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Aiming at operating optimally minimizing error of tracking and designing control effort, this study presents a novel generalizable methodology of an optimal torque control for a 6-degree-of-freedom Stewart platform with rotary actuators. In the proposed approach, a linear quadratic integral regulator with the least sensitivity to controller parameter choices is designed, associated with an online artificial neural network gain tuning. The nonlinear system is implemented in ADAMS, and the controller is formulated in MATLAB to minimize the real-time tracking error robustly. To validate the controller performance, MATLAB and ADAMS are linked together and the performance of the controller on the simulated system is validated as real time. Practically, the Stewart robot is fabricated and the proposed controller is implemented. The method is assessed by simulation experiments, exhibiting the viability of the developed methodology and highlighting an improvement of 45% averagely, from the optimum and zero-error convergence points of view. Consequently, the experiment results allow demonstrating the robustness of the controller method, in the presence of the motor torque saturation, the uncertainties, and unknown disturbances such as intrinsic properties of the real test bed.

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A novel online method for identifying motion artifact and photoplethysmography signal reconstruction using artificial neural networks and adaptive neuro-fuzzy inference system

Cited by 26 publications

References 41 publications

Current progress of photoplethysmography and SPO2 for health monitoring

Current progress of photoplethysmography and SPO2 for health monitoring

Photoplethysmogram Analysis and Applications: An Integrative Review

Implementation and intelligent gain tuning feedback–based optimal torque control of a rotary parallel robot

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