Design of a New Long-Time Continuous Photoplethysmography Signal Acquisition System to Obtain Accurate Measurement of Heart Rate

2020 ◽  
Author(s):  
Rajeev Kumar Pandey ◽  
Jerry Lin ◽  
Paul C.-P. Chao
Keyword(s):  
Heart Rate ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Motion Artifact ◽  
Acquisition System ◽  
Experimental Result ◽  
Signal Acquisition ◽  
Readout System ◽  
Long Time ◽  
Filter Signal

Abstract This study presents a time-interleave and low DC drift long-time continuous photoplethysmography (PPG) signal acquisition system to obtain accurate measurement of heart rate (HR) in real-time. Time-interleave functionality is used herein to minimize the mispositioning issue. Intensity tuning and transimpedance amplifier gain tuning is used herein to acquire a high-quality PPG signal. The front-end analog readout circuit is designed and implemented by using T18 process. The experimental result shows that the design readout system has the DC settling time of 1 second after the motion artifact. The measured current sensing range is 30nA–10uA. The estimated signal to noise ratio is 68dB@1Hz. The backend pre-signal processing incorporates a new convolution-based moving average filter, signal quality index estimator, and a peak-through detector. The non-invasive PPG sensor is applied to the wrist artery of the 40 healthy subjects for sensing the pulsation of the blood vessel. During the measurement, the subject did not drink (alcohol), eat, smoke or workout. The Measurement results shows that the heart rate accuracy and standard error are 95%, and 0.37±1.96bpm, respectively.

Design of ECG Acquisition System Based on Matlab

2013 ◽  
Vol 462-463 ◽  
pp. 1001-1004
Author(s):  
Xue Wang ◽  
Wen Liang Niu ◽  
Yuan Sheng Liu
Keyword(s):  
Heart Rate ◽  
Cost Savings ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Threshold Selection ◽  
Selection Algorithm ◽  
Absolute Value ◽  
Wave Detection ◽  
Processing Circuit

In order to more accurate detection of ECG signal and cost savings, this paper designed a set of ECG signal acquisition system based on Matlab. The system is composed of hardware and software. Hardware is made up of signal acquisition processing circuit and MPC82G516 microcontroller for AD conversion. The software mainly contains configuring Matlab serial to realize communication with MPC82G516 and uses abundant filter resources of Matlab to remove noise by differential, absolute value, smoothing and threshold selection algorithm of R wave detection, R wave peak provides the basis for calculating the heart rate. This paper uses Matlab friendly user interface for displaying received data to make the data more intuitive and read more easily.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

2021 ◽  
Author(s):  
Kianoosh Kazemi ◽  
Juho Laitala ◽  
Iman Azimi ◽  
Pasi Liljeberg ◽  
Amir M. Rahmani
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Detection Methods ◽  
Method Performance ◽  
Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

Non-contact respiratory rate and heart rate signal acquisition system

2019 ◽  
Vol 27 (6) ◽  
pp. 1354-1361
Author(s):  
郭 健 GUO Jian ◽  
陈雨行 CHEN Yu-hang ◽  
王丽荣 WANG Li-rong ◽  
韦 阳 WEI Yang ◽  
郭 宇 GUO Yu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Respiratory Rate ◽  
Acquisition System ◽  
Signal Acquisition

The Multi Wavelength Arrayed Flexible PPG Sensing Patch for to Estimate Heart Rate and Blood Oxygen

2020 ◽  
Author(s):  
Yu-Ting Chen ◽  
Tse-Yi Tu ◽  
Paul C.-P. Chao
Keyword(s):  
Heart Rate ◽  
Continuous Monitoring ◽  
Motion Artifact ◽  
Blood Oxygen ◽  
Rate Estimation ◽  
Multi Wavelength ◽  
Long Time ◽  
Heart Rate Estimation

Abstract This study aims to develop the Photoplethysmography (PPG) sensor patch for to estimate the heart rate (HR) and blood oxygen (SpO2). A newly developed multi wavelength arrayed flexible OLED-OPD PPG sensing patch elevates the performance of motion artifact for not only for heart rate estimation but also blood oxygen estimation. The PPG sensing patch ensures the long-time continuous monitoring of the PPG signal from the wrist artery during sleeping, walking and cycling. The accuracy of the HRs is 92% and the accuracy of SpO2 is 95%.

Application of Biomedical Signal Acquisition Equipment in Human Sport Heart Rate Monitoring

2020 ◽  
Vol 10 (4) ◽  
pp. 877-883
Author(s):  
Le He
Keyword(s):  
Heart Rate ◽  
Motion Artifact ◽  
Human Motion ◽  
Signal Quality ◽  
Signal Acquisition ◽  
Heart Rate Monitoring ◽  
Artifact Removal ◽  
Biomedical Signal ◽  
Baseline Drift ◽  
Sensor Signals

Aiming at exploring biomedical signal acquisition equipment used in human motion heart rate monitoring, the research on the related hardware design and signal processing method was carried out. A biomedical signal acquisition device based on photoplethysmography (PPG) is designed, and the equipment was applied to acquire PPG signals and acceleration sensor signals under different motion states. The analysis of the experimental data showed that, the fusion method of the acceleration sensing information in the motion artifact removal method is perfected. The effectiveness of the baseline drift removal algorithm, motion artifact removal algorithm and dynamic heart rate monitoring algorithm was verified by reconstructing the signal quality evaluation index. To sum up, taking MINDRAY VS-800 as a reference device, it is compared with the adaptive filtering technology in terms of signal quality, BPM detection results and algorithm complexity, and better results are finally obtained.

scholarly journals REMOVAL OF ELECTRODE MOTION ARTIFACT IN ECG SIGNALS USING WAVELET BASED THRESHOLD METHODS WITH GREY INCIDENCE DEGREE

2013 ◽  
pp. 11-18
Author(s):  
G. UMAMAHESWARA REDDY ◽  
M. MURALIDHAR
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Modern Society ◽  
Motion Artifact ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
Grey Theory ◽  
Wavelet Thresholding ◽  
Threshold Method ◽  
Ecg Signals

Cardiovascular diseases are one of the most frequent and dangerous problems in modern society in nowadays. Unfortunately electrocardiograms (ECG) signals, during their acquisition process, are affected by various types of noise and artifacts due to the movement, or breathing of the patient, electrode contact, power-line interferences, etc. The aim of this study was to develop an algorithm to remove electrode motion artifact in ECG signals. Donoho and Johnstone proposed Wavelet thresholding de-noising method based on discrete wavelet transform (DWT) is suitable for non-stationary signals. The wavelet transform coefficient is processed by using grey relation analysis of the grey theory, and a new wavelet threshold method namely wavelet threshold method with grey incidence degree (GID) (or the GID threshold method) based is introduced. It shows that the signal smoothness and similarity of the two signal criteria have been greatly improved by the GID threshold method compared with existing threshold methods. According to the characteristics of different ECG signals, GID threshold method gets better results than it can adaptively deal with noise separation and details remaining of the two opposing signal problems, so as to provide a better choice for wavelet threshold methods of signal processing. Performance analysis was performed by evaluating Mean Square Error (MSE), Signal-to-noise ratio (SNR) and visual inspection over the denoised signal from each algorithm. The experimental result shows that GID hard shrinkage method with sub-band or level dependent thresholding gives the best denoising performance on ECG signal. The result shows that soft threshold not always gives better denoising performance; it depends on which wavelet thresholding algorithm was chosen.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

2021 ◽  
Author(s):  
Kianoosh Kazemi ◽  
Juho Laitala ◽  
Iman Azimi ◽  
Pasi Liljeberg ◽  
Amir M. Rahmani
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Detection Methods ◽  
Method Performance ◽  
Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

scholarly journals Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

2021 ◽  
Author(s):  
Kianoosh Kazemi ◽  
Juho Laitala ◽  
Iman Azimi ◽  
Pasi Liljeberg ◽  
Amir M. Rahmani
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Detection Methods ◽  
Method Performance ◽  
Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

A New Low Power Photoplethysmography Signal Acquisition System For Mental Stress Estimation

2021 ◽  
Author(s):  
Rajeev Kumar Pandey ◽  
Paul C.-P. Chao
Keyword(s):  
Standard Deviation ◽  
Power Consumption ◽  
Low Power ◽  
Dynamic Range ◽  
Acquisition System ◽  
Total Power ◽  
Signal Acquisition ◽  
Readout System ◽  
Pulse Rate Variability ◽  
Total Power Consumption

Abstract This study presents a new low power and robust reflectance type optical Photoplethysmography (PPG) acquisition system for the mental distress estimation. The front-end circuit is implemented in the integrated chip with chip area of 1200μm × 1200μm and fabricated via TSMC T18 process. The sensing range of the readout circuit is 20nA to 11μA, and the total power consumption of the readout system is 100μW. The total power consumption of the design chip including the OLED driver power is 1.64mW. The designed acquisition system is applied to the wrist artery of the two healthy patients when they are calculating the pictorial puzzles and when they are in relax state. The statistical deviation of the heart rate (HR) from the average HR is increased when subjects are in the stress. Also, the standard deviation of pulse rate variability (PRV), the dynamic range of pulse repetition time (PRT), and the standard deviation of PRV derivative show the increasing temporal value when subjects are in the stress.

scholarly journals MOTION ARTIFACT REDUCTION IN FUNCTIONAL NEAR INFRARED SPECTROSCOPY SIGNALS BY AUTOREGRESSIVE MOVING AVERAGE MODELING BASED KALMAN FILTERING

2013 ◽  
Vol 06 (04) ◽  
pp. 1350035
Author(s):  
MEHDI AMIAN ◽  
S. KAMALEDIN SETAREHDAN
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Motion Artifact ◽  
Space Representation ◽  
Autoregressive Moving Average ◽  
Functional Near Infrared Spectroscopy ◽  
State Space Representation

Functional near infrared spectroscopy (fNIRS) is a technique that is used for noninvasive measurement of the oxyhemoglobin (HbO2) and deoxyhemoglobin (HHb) concentrations in the brain tissue. Since the ratio of the concentration of these two agents is correlated with the neuronal activity, fNIRS can be used for the monitoring and quantifying the cortical activity. The portability of fNIRS makes it a good candidate for studies involving subject's movement. The fNIRS measurements, however, are sensitive to artifacts generated by subject's head motion. This makes fNIRS signals less effective in such applications. In this paper, the autoregressive moving average (ARMA) modeling of the fNIRS signal is proposed for state-space representation of the signal which is then fed to the Kalman filter for estimating the motionless signal from motion corrupted signal. Results are compared to the autoregressive model (AR) based approach, which has been done previously, and show that the ARMA models outperform AR models. We attribute it to the richer structure, containing more terms indeed, of ARMA than AR. We show that the signal to noise ratio (SNR) is about 2 dB higher for ARMA based method.

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