• Title/Summary/Keyword: PPG (Photoplethysmography)

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Effective PPG Signal Processing Method for Detecting Emotional Stimulus (감성 자극 판단을 위한 효과적인 PPG 신호 처리 방법)

  • Oh, Dong-Gi;Min, Byung-Seok;Kwon, Sung-Oh;Kim, Hyun-Joong
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37 no.5C
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    • pp.393-402
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    • 2012
  • In this study, we propose a signal processing algorithm to measure the arousal level of a human subject using a PPG(Photoplethysmography) sensor. From the measured PPG signals, the arousal level is determined by PPI(Pulse to Pulse Interval) and discrete-time signal processing. We ran psychophysical experiments displaying visual stimuli on TV display while measuring PPG signal from a finger, where the nature landscape scenes were used for restorative effect, and the urban environments were used to stimulate the stress. However, the measured PPG signals may include noise due to subject movement and measurement error, which results in incorrect detections. In this paper, to mitigate the noise impact on stimulus detection, we propose a detecting algorithm using digital signal processing methods and statistics of measured signals. A filter is adopted to remove a high frequency noise and adaptively designed taking into account the statistics of the measured PPG signals. Moreover we employ a hysteresis method to reduce the distortion of PPI in decision of emotional. Via experiment, we show that the proposed scheme reduces signal noise and improves stimulus detection.

The Motion Artifact Reduction from the PPG based on EWMA (지수가중 이동평균 기반의 PPG 신호 동잡음 제거)

  • Lee, Jun-Yeon
    • Journal of Digital Convergence
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    • v.11 no.8
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    • pp.183-190
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    • 2013
  • The Photoplethysmogram is a similar periodic signal that synchrinized to a heartbeat. In this paper, we propose a exponential weight moving average filter that use similarity of Photoplethysmogram. This filtering method has the average value of each samples through separating the cycle of PPG signal. If there are some motion artifacts in continuous PPG signal, disjoin the signal based on cycle. And then, we made these signals to have same cycle by coordinating the number of sample. After arrange these cycles in 2 dimension, we put the average value of each samples from starting till now. So, we can eliminate the motion artifacts without damaged PPG signal.

User-Adaptive Movement Noise Detection Algorithm Using Wavelet Transform (Wavelet을 이용한 사용자 적응 동잡음 판단 알고리즘)

  • Ban, Dahee;Kwon, Sungoh
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.6
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    • pp.1120-1129
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    • 2015
  • In this paper, we propose an algorithm to detect movement noise in PPG(Photoplethysmography) measurements. Movement noise significantly deteriorate PPG signals in measurement, so that a movement noise detection algorithm is critical before using measured PPG signals for applications such as diagnosis. To detect movement noise, we apply wavelet transform to PPG signals instead of short-time Fourier transform and decide if the measured signlas include movement noise. To that end, we adaptively choose a wavelet, which is the most similar to the subject's PPG pattern. In the case when movement noise is intentionally added in the 20% and 30% of the total experiment time, our algorithm detects time-slots including movement and outperforms previous works.

Sensitivity illumination system using biological signal (생체신호를 이용한 감성조명 시스템)

  • Han, Young-Oh;Kim, Dong-Woo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.4
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    • pp.499-508
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    • 2014
  • In this paper, we implemented a LED sensitivity illumination system, being driven in response to changes in the biological signals of GSR and PPG signal. After measuring biological signals of a human body from GSR and PPG sensor modules, MCU decided the state of relaxation or arousal of the subject, being based on the wake relaxation identifying map proposed in this paper. A developed LED sensitivity illumination system makes the subject to reach a normal state by giving a change of the LED illumination color, corresponding to a state of the subject.

A Study on Selection of the Optimal Region of Interest for Smart Scale Photoplethysmography (스마트 체중계의 PPG 신호를 위한 최적의 측정 위치 선택에 대한 연구)

  • Jung, SeungGi;Han, TaeTang;Kim, ChanYoung;Moon, Chanki;Nam, Yunyoung
    • Proceedings of the Korea Information Processing Society Conference
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    • 2015.10a
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    • pp.555-558
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    • 2015
  • 본 논문에서는 체중계에 올라선 상태에서 발바닥 다섯 부위의 PPG 신호를 측정하고 분석하여 가장 강한 신호가 측정되는 최적의 위치를 찾기 위해 비교 실험하였다. PPG 신호는 스마트폰 카메라 측정하였고, 신호의 정확률을 비교하기 위해 발바닥과 손가락에서 동시에 PPG신호를 측정하였다. 발바닥과 손가락 끝에서 얻은 PPG 신호로부터 RRI를 산출한 후 Bland-Altman을 이용하여 유의성을 비교 분석하였다. 실험은 5명의 젊은 남녀를 대상으로 수행되었으며 실험결과 부위 1과 부위 2에서 높은 유의성을 보였다.

A Study on Accelerometer Based Motion Artifact Reduction in Photoplethysmography Signal (가속도계를 이용한 광전용적맥파의 동잡음 제거)

  • Kang, Joung-Hoon;Cho, Baek-Hwan;Lee, Jong-Shill;Chee, Young-Joon;Kim, In-Young;Kim, Sun-I.
    • Journal of Biomedical Engineering Research
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    • v.28 no.3
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    • pp.369-376
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    • 2007
  • With the convergence of ubiquitous networking and medical technologies, ubiquitous healthcare(U-Healthcare) service has come in our life, which enables a patient to receive medical services at anytime and anywhere. In the u-Healthcare environment, intelligent real-time biosignal aquisition/analysis techniques are inevitable. In this study, we propose a motion artifact cancelation method in portable photoplethysmography(PPG) signal aquisition using an accelerometer and an adaptive filter. A preliminary experiment represented that the component of the pedestrian motion artifact can be found under 5Hz in the spectral analysis. Therefore, we collected PPG signals under both simulated conditions with a motor that generates circular motion with uniform velocity (from 1 to 5Hz) and a real walking condition. We then reduced the motion artifact using a recursive least square adaptive filter which takes the accelerometer output as a noise reference. The results showed that the adaptive filter can remove the motion artifact effectively and recover peak points in PPG signals, which represents our method can be useful to detect heart rate in real walking condition.

A Study of Biosignal Analysis System for Sensibility Evaluation (감성을 평가하기 위한 생체신호 분석 시스템에 관한 연구)

  • Lee, Ji-Hyeoung;Kim, Kyung-Ho
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.12
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    • pp.19-26
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    • 2010
  • In this paper, we studied about the Embedded System of the biosignal measurement and analysis to sensibility evaluation in daily life for non-intrusive. This system is two kinds of measuring biosiganls(Electrocardiogram:ECG, Photoplethysmography:PPG) and analyzed by real-time wireless transmission to notebook PC using bluetooth for consistent and reliability of physiological way to assess continuously changing sensibility. Comparative studied of an autonomic nerve system activity ratio on characteristics frequency band of two kinds of biosignal analyzed frequency way using the Fast Fourier Transform(FFT) and Power Spectrum Density(PSD). Also the key idea of this system is to minimize computing of analysis algorithm for faster and more accurate to assess the sensibility, and the result of the visualization using graph. In this paper, we evaluated the analysis system to assess sensibility that measuring various situation in daily life using a non-intrusive biosignal measurement system, and the accuracy and reliability in comparison with difference of result by development analysis system.

The study of blood glucose level prediction using photoplethysmography and machine learning (PPG와 기계학습을 활용한 혈당수치 예측 연구)

  • Cheol-Gu, Park;Sang-Ki, Choi
    • Journal of Digital Policy
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    • v.1 no.2
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    • pp.61-69
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    • 2022
  • The paper is a study to develop and verify a blood glucose level prediction model based on biosignals obtained from photoplethysmography (PPG) sensors, ICT technology and data. Blood glucose prediction used the MLP architecture of machine learning. The input layer of the machine learning model consists of 10 input nodes and 5 hidden layers: heart rate, heart rate variability, age, gender, VLF, LF, HF, SDNN, RMSSD, and PNN50. The results of the predictive model are MSE=0.0724, MAE=1.1022 and RMSE=1.0285, and the coefficient of determination (R2) is 0.9985. A blood glucose prediction model using bio-signal data collected from digital devices and machine learning was established and verified. If research to standardize and increase accuracy of machine learning datasets for various digital devices continues, it could be an alternative method for individual blood glucose management.

Cuffless Blood Pressure Estimation Based on a Convolutional Neural Network using PPG and ECG Signals for Portable or Wearable Blood Pressure Devices (휴대용 및 웨어러블 측정기를 위한 ECG와 PPG 신호를 활용한 합성곱 신경망 알고리즘 기반의 비가압식 혈압 추정 방법)

  • Cho, Jinwoo;Choi, Ahyoung
    • Journal of Korea Society of Industrial Information Systems
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    • v.25 no.3
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    • pp.1-10
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    • 2020
  • In this paper, we propose an algorithm for estimating blood pressure using ECG (Electrocardiogram) and PPG (Photoplethysmography) signals. To estimate the BP (Blood pressure), we generate a periodic input signal, remove the noise according to the differential and threshold methods, and then estimate the systolic and diastolic blood pressures based on the convolutional neural network. We used 49 patient data of 3.1GB in the MIMIC database. As a result, it was found that the prediction error (RMSE) of systolic BP was 5.80mmHg, and the prediction error of diastolic BP was 2.78mmHg. This result confirms that the performance of class A is satisfied with the existing BP monitor evaluation method proposed by the British High Blood Pressure Association.