A pulse oximetry system with motion artifact reduction based on Fourier analysis

Yang, Yi-Hsiang; Tang, Kea-Tiong

doi:10.1109/isbb.2014.6820902

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…A contaminated PPG signal is first trimmed by a preprocessor such as a bandpass filter to decrease noise. Then, the fundamental frequency of PPG is determined by perioddetection algorithms, and Fourier coefficients of the fundamental and representative harmonics are calculated (Yang and Tang, 2014;Sadhukhan et al, 2018). By applying inverse Fourier transform to these coefficients, a clean PPG signal is reconstructed.…”

Section: Independence-based Compensationmentioning

confidence: 99%

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

et al. 2021

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Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.

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Section: Independence-based Compensationmentioning

confidence: 99%

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

et al. 2021

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“…Photoplethysmography (PPG) is a non-invasive measuring method aimed at sensing the pulsation of the blood vessel by acquiring the reflective lights from a light-emitted diode (LED) toward a photodiode (PD). Photoplethysmography sensors are known for their non-invasive, small-sized, inexpensive, easy-to-use measurements, and it is widely used in hospitals for monitoring heart rate [14], heart rate variability [15], blood pressure [16], blood oxygen level [17], etc. A typical PPG signal is shown in Figure 1, where the PPG signal is composed of a very large direct component (DC) and a very small alternating component (AC).…”

Section: Theories and Principlesmentioning

confidence: 99%

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

Chao

et al. 2019

Sensors

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The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the quality of AVF: the blood flow volume (BFV) and the degree of stenosis (DOS). In hospitals, the BFV and DOS of AVFs are nowadays assessed using an ultrasound Doppler machine, which is bulky, expensive, hard to use, and time consuming. In this study, a newly-developed PPG sensor device was utilized to provide patients and doctors with an inexpensive and small-sized solution for ubiquitous AVF assessment. The readout in this sensor was custom-designed to increase the signal-to-noise ratio (SNR) and reduce the environment interference via maximizing successfully the full dynamic range of measured PPG entering an analog–digital converter (ADC) and effective filtering techniques. With quality PPG measurements obtained, machine learning classifiers including SVM were adopted to assess AVF quality, where the input features are determined based on optical Beer–Lambert’s law and hemodynamic model, to ensure all the necessary features are considered. Finally, the clinical experiment results showed that the proposed PPG sensor device successfully achieved an accuracy of 87.84% based on SVM analysis in assessing DOS at AVF, while an accuracy of 88.61% was achieved for assessing BFV at AVF.

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Quality Evaluation via PPG on the AVFs of Hemodialysis Patients Based on Both Blood Flow Volume and Degree of Stenosis

Chiang

Chao

et al. 2019

2019 Ieee Sensors

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A pulse oximetry system with motion artifact reduction based on Fourier analysis

Cited by 4 publications

References 14 publications

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

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

Quality Evaluation via PPG on the AVFs of Hemodialysis Patients Based on Both Blood Flow Volume and Degree of Stenosis

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