scholarly journals Development of a Wearable Reflection-Type Pulse Oximeter System to Acquire Clean PPG Signals and Measure Pulse Rate and SpO2 with and without Finger Motion

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1905
Author(s):  
Partha Pratim Banik ◽  
Shifat Hossain ◽  
Tae-Ho Kwon ◽  
Hyoungkeun Kim ◽  
Ki-Doo Kim
Keyword(s):  
Pulse Rate ◽  
Pulse Oximeter ◽  
Electronic Circuit ◽  
Absolute Error ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Percentage Error ◽  
Signal Acquisition ◽  
Analog Filter ◽  
Finger Motion

Clinical devices play a vital role in diagnosing and monitoring people’s health. A pulse oximeter (PO) is one of the most common clinical devices for critical medical care. In this paper, we explain how we developed a wearable PO. We propose a new electronic circuit based on an analog filter that can separate red and green photoplethysmography (PPG) signals, acquire clean PPG signals, and estimate the pulse rate (PR) and peripheral capillary oxygen saturation (SpO2). We propose a PR and SpO2 measurement algorithm with and without the motion artifact. We consider three types of motion artifacts with our acquired clean PPG signal from our proposed electronic circuit. To evaluate our proposed algorithm, we measured the accuracy of our estimated SpO2 and PR. To evaluate the quality of our estimated PR (bpm) and SpO2 (%) with and without the finger motion artifact, we used the quality evaluation metrics: mean absolute percentage error (MAPE), mean absolute error (MAE), and reference closeness factor (RCF). Without the finger motion condition, we found that our proposed wearable PO device achieved an average 2.81% MAPE, 2.08 bpm MAE, 0.97 RCF, and 98.96% SpO2 accuracy. With a finger motion, the proposed wearable PO device achieved an average 4.5% MAPE, 3.66 bpm MAE, 0.96 RCF, and 96.88% SpO2 accuracy. We also show a comparison of our proposed PO device with a commercial Fingertip PO (FPO) device. We have found that our proposed PO device performs better than the commercial FPO device under finger motion conditions. To demonstrate the implementation of our wearable PO, we developed a smartphone app to allow the PO device to share PPG signals, PR, and SpO2 through Bluetooth communication. We also show the possible applications of our proposed PO as a wearable, hand-held PO device, and a PPG signal acquisition system.

scholarly journals Camera-based heart rate estimation for hospitalized newborns in the presence of motion artifacts

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Qiong Chen ◽  
Yalin Wang ◽  
Xiangyu Liu ◽  
Xi Long ◽  
Bin Yin ◽  
...  
Keyword(s):  
Heart Rate ◽  
Body Movement ◽  
Region Of Interest ◽  
Ground Truth ◽  
Absolute Error ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Majority Voting ◽  
Thermal Cameras ◽  
Electrocardiogram Ecg

Abstract Background Heart rate (HR) is an important vital sign for evaluating the physiological condition of a newborn infant. Recently, for measuring HR, novel RGB camera-based non-contact techniques have demonstrated their specific superiority compared with other techniques, such as dopplers and thermal cameras. However, they still suffered poor robustness in infants’ HR measurements due to frequent body movement. Methods This paper introduces a framework to improve the robustness of infants’ HR measurements by solving motion artifact problems. Our solution is based on the following steps: morphology-based filtering, region-of-interest (ROI) dividing, Eulerian video magnification and majority voting. In particular, ROI dividing improves ROI information utilization. The majority voting scheme improves the statistical robustness by choosing the HR with the highest probability. Additionally, we determined the dividing parameter that leads to the most accurate HR measurements. In order to examine the performance of the proposed method, we collected 4 hours of videos and recorded the corresponding electrocardiogram (ECG) of 9 hospitalized neonates under two different conditions—rest still and visible movements. Results Experimental results indicate a promising performance: the mean absolute error during rest still and visible movements are 3.39 beats per minute (BPM) and 4.34 BPM, respectively, which improves at least 2.00 and 1.88 BPM compared with previous works. The Bland-Altman plots also show the remarkable consistency of our results and the HR derived from the ground-truth ECG. Conclusions To the best of our knowledge, this is the first study aimed at improving the robustness of neonatal HR measurement under motion artifacts using an RGB camera. The preliminary results have shown the promising prospects of the proposed method, which hopefully reduce neonatal mortality in hospitals.

scholarly journals Removal of Motion Artifacts in Photoplethysmograph Sensors during Intensive Exercise for Accurate Heart Rate Calculation Based on Frequency Estimation and Notch Filtering

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3312 ◽  
Author(s):  
Min Wang ◽  
Zhe Li ◽  
Qirui Zhang ◽  
Guoxing Wang
Keyword(s):  
Heart Rate ◽  
Frequency Estimation ◽  
Absolute Error ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Data Set ◽  
Motion Patterns ◽  
Intensive Exercise ◽  
Notch Filtering ◽  
Rate Frequency

With photoplethysmograph (PPG) sensors showing increasing potential in wearable health monitoring, the challenging problem of motion artifact (MA) removal during intensive exercise has become a popular research topic. In this study, a novel method that combines heart rate frequency (HRF) estimation and notch filtering is proposed. The proposed method applies a cascaded adaptive noise cancellation (ANC) based on the least mean squares (LMS)-Newton algorithm for preliminary motion artifacts reduction, and further adopts special heart rate frequency tracking and correction schemes for accurate HRF estimation. Finally, notch filters are employed to restore the PPG signal with estimated HRF based on its quasi-periodicity. On an open source data set that features intensive running exercise, the proposed method achieves a competitive mean average absolute error (AAE) result of 0.92 bpm for HR estimation. The practical experiments are carried out with the PPG evaluation platform developed by ourselves. Under three different intensive motion patterns, a 0.89 bpm average AAE result is achieved with the average correlation coefficient between recovered PPG signal and reference PPG signal reaching 0.86. The experimental results demonstrate the effectiveness of the proposed method for accurate HR estimation and robust MA removal in PPG during intensive exercise.

scholarly journals Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

2020 ◽  
Author(s):  
Chiou-Jye Huang ◽  
Yamin Shen ◽  
Ping-Huan Kuo ◽  
Yung-Hsiang Chen
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
The Other ◽  
Percentage Error ◽  
Model Assessment ◽  
Absolute Percentage Error ◽  
Spatiotemporal Feature ◽  
The Mean

AbstractThe coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which are commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those of the other models. This indicated that the proposed framework can accurately predict the accumulated number of confirmed cases in the three countries and serve as a crucial reference for devising public health strategies.

scholarly journals Predicting the Istanbul Stock Exchange Index Return using Technical Indicators

2013 ◽  
Vol 2 (3) ◽  
pp. 111-117
Author(s):  
Senol Emir
Keyword(s):  
Stock Exchange ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Model Parameters ◽  
Technical Indicators ◽  
Istanbul Stock Exchange ◽  
Vector Machines ◽  
Classical Linear Regression ◽  
Daily Returns

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.

scholarly journals Use of Transforms in Biomedical Signal Processing and Analysis

2021 ◽  
Author(s):  
Ette Harikrishna ◽  
Komalla Ashoka Reddy
Keyword(s):  
Wavelet Transforms ◽  
Research Work ◽  
Reference Signal ◽  
Low Frequency ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Biomedical Signal Processing ◽  
Blood Oxygen Saturation ◽  
Biomedical Signals ◽  
Electrocardiogram Ecg

Biomedical signals like electrocardiogram (ECG), photoplethysmographic (PPG) and blood pressure were very low frequency signals and need to be processed for further diagnosis and clinical monitoring. Transforms like Fourier transform (FT) and Wavelet transform (WT) were extensively used in literature for processing and analysis. In my research work, Fourier and wavelet transforms were utilized to reduce motion artifacts from PPG signals so as to produce correct blood oxygen saturation (SpO2) values. In an important contribution we utilized FT for generation of reference signal for adaptive filter based motion artifact reduction eliminating additional sensor for acquisition of reference signal. Similarly we utilized the transforms for other biomedical signals.

A method for predicting the capacity of a lithium-ion battery based on deep neural network

2021 ◽  
Author(s):  
JamesChan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Lithium Ion ◽  
Chemical Conversion ◽  
Absolute Error ◽  
Production Costs ◽  
Percentage Error ◽  
Average Percentage ◽  
True Value ◽  
Battery Capacity

This paper proposes a solution to predict the capacity of the lithium-ion battery's capacity division process using deep learning methods. This solution extracts the physical observation records of part of the process steps from the chemical conversion and volumetric processes as features, and trains a Deep Neural Network (DNN) to achieve accurate prediction of battery capacity. According to the test, the average percentage absolute error (Mean Absolute Percentage Error, MAPE) of the battery capacity predicted by this model is only 0.78% compared with the true value. Combining this model with the production line can greatly reduce production time and energy consumption, and reduce battery production costs.

Forecasting Malaysian overnight islamic interbank rate using the Box-Jenkins model

2021 ◽  
Vol 2 (1) ◽  
pp. 38-51
Author(s):  
N.S.M. Radzi ◽  
S.R. Yaziz
Keyword(s):  
Monetary Policy ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Prediction Intervals ◽  
Percentage Error ◽  
Mean Square ◽  
Islamic Banks ◽  
Financial Instruments ◽  
Policy Makers ◽  
Forecasting Performance

Modelling the overnight Islamic interbank rate (IIR) is imperative to define the IIR performance as it would help the Islamic banks to adjust its costs of funding effectively and facilitate the policy makers to regulate a comprehensive monetary policy in Malaysia. The IIR framework which has been regulated by Bank Negara Malaysia under dual banking and financial system has always been overlooked in most previous studies in modelling the financial instruments rates. Therefore, it is vital to select the appropriate model as it resembles with the features of the IIR. The study assesses the forecasting performance of overnight IIR using the Box-Jenkins model. The suggested Box-Jenkins model has been applied to the Malaysian overnight IIR (in percentage) from 02/01/2001 to 31/12/2020. The empirical results determine that ARIMA (0,1,1) is the most appropriate model in forecasting overnight IIR as the model provides the smallest Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In multistep ahead forecasting, it can be summarised that ARIMA (0,1,1) model is able to trail the actual data trend of daily Malaysian overnight IIR up to 5-day ahead within 95% prediction intervals.

scholarly journals A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

scholarly journals Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network

2020 ◽  
Vol 30 (11) ◽  
pp. 5923-5932
Author(s):  
M.-L. Kromrey ◽  
D. Tamada ◽  
H. Johno ◽  
S. Funayama ◽  
N. Nagata ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Arterial Phase ◽  
Artifact Reduction ◽  
Gadoxetate Disodium ◽  
Mr Images ◽  
Liver Mri ◽  
Lesion Conspicuity

Abstract Objectives To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium–enhanced multi-arterial phase MRI of the liver. Methods This retrospective study included 192 patients (131 men, 68.7 ± 10.3 years) receiving gadoxetate disodium–enhanced liver MRI in 2017. Datasets were submitted to a newly developed filter (MARC), consisting of 7 convolutional layers, and trained on 14,190 cropped images generated from abdominal MR images. Motion artifact for training was simulated by adding periodic k-space domain noise to the images. Original and filtered images of pre-contrast and 6 arterial phases (7 image sets per patient resulting in 1344 sets in total) were evaluated regarding motion artifacts on a 4-point scale. Lesion conspicuity in original and filtered images was ranked by side-by-side comparison. Results Of the 1344 original image sets, motion artifact score was 2 in 597, 3 in 165, and 4 in 54 sets. MARC significantly improved image quality over all phases showing an average motion artifact score of 1.97 ± 0.72 compared to 2.53 ± 0.71 in original MR images (p < 0.001). MARC improved motion scores from 2 to 1 in 177/596 (29.65%), from 3 to 2 in 119/165 (72.12%), and from 4 to 3 in 34/54 sets (62.96%). Lesion conspicuity was significantly improved (p < 0.001) without removing anatomical details. Conclusions Motion artifacts and lesion conspicuity of gadoxetate disodium–enhanced arterial phase liver MRI were significantly improved by the MARC filter, especially in cases with substantial artifacts. This method can be of high clinical value in subjects with failing breath-hold in the scan. Key Points • This study presents a newly developed deep learning–based filter for artifact reduction using convolutional neural network (motion artifact reduction with convolutional neural network, MARC). • MARC significantly improved MR image quality after gadoxetate disodium administration by reducing motion artifacts, especially in cases with severely degraded images. • Postprocessing with MARC led to better lesion conspicuity without removing anatomical details.

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