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 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 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.

DeepHeart

2021 ◽  
Vol 17 (2) ◽  
pp. 1-18
Author(s):  
Xiangmao Chang ◽  
Gangkai Li ◽  
Guoliang Xing ◽  
Kun Zhu ◽  
Linlin Tu
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Spectrum Analysis ◽  
Absolute Error ◽  
Physical Activities ◽  
Motion Artifacts ◽  
Training Data ◽  
Data Set ◽  
Electrocardiogram Signals ◽  
Wide Range

Heart rate (HR) estimation based on photoplethysmography (PPG) signals has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user’s physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios, making them impractical in real-world settings where a user may perform a wide range of physical activities. In this article, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from electrocardiogram signals based on a training data set. Then a set of denoising convolutional neural networks (DCNNs) are trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by an ensemble of DCNNs and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.61 beats per minute (bpm), outperforming a state-of-the-art deep learning approach (4 bpm) and a classical signal processing approach (2.34 bpm).

scholarly journals Acoustic Sensing as a Novel Wearable Approach for Cardiac Monitoring at the Wrist

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Piyush Sharma ◽  
Syed Anas Imtiaz ◽  
Esther Rodriguez-Villegas
Keyword(s):  
Heart Rate ◽  
Acoustic Pulse ◽  
Power Spectral Analysis ◽  
Pulse Signal ◽  
Absolute Error ◽  
Motion Artifacts ◽  
Cardiac Monitoring ◽  
Acoustic Sensing ◽  
Power Spectral ◽  
Commercial Device

AbstractThis paper introduces the concept of using acoustic sensing over the radial artery to extract cardiac parameters for continuous vital sign monitoring. It proposes a novel measurement principle that allows detection of the heart sounds together with the pulse wave, an attribute not possible with existing photoplethysmography (PPG)-based methods for monitoring at the wrist. The validity of the proposed principle is demonstrated using a new miniature, battery-operated wearable device to sense the acoustic signals and a novel algorithm to extract the heart rate from these signals. The algorithm utilizes the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. It has been validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between −1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. The results in this proof of concept study demonstrate the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for continuous monitoring of heart rate at the wrist.

DEVELOPMENT OF A DEVICE FOR CONTROL OF THE STATE OF THE ORGANISM BASED ON PHOTOPLETISMOGRAPHY USING TECHNOLOGIES OF DIGITAL ADAPTIVE FILTRATION

2020 ◽  
pp. 100-107
Author(s):  
Алексей Дмитриевич Акишин ◽  
Иван Павлович Семчук ◽  
Александр Петрович Николаев
Keyword(s):  
Heart Rate ◽  
Cardiovascular System ◽  
Adaptive Filtering ◽  
Least Squares Method ◽  
Optimal Algorithm ◽  
Heart Diseases ◽  
Cardiac Activity ◽  
Motion Artifacts ◽  
The Body ◽  
Advantages And Disadvantages

Постоянно растущий интерес к разработке новых неинвазивных и безманжетных методов измерения параметров сердечной деятельности, использование которых давало бы возможность непрерывного и удаленного контроля сердечно-сосудистой системы, обуславливает актуальность данной работы. В многочисленных публикациях продолжаются обсуждения преимуществ и недостатков различных методов ранней диагностики сердечно-сосудистых заболеваний. Однако артефакты движения являются сильной помехой, мешающей точной оценке показателей функционирования сердечно-сосудистой системы. Одним из перспективных методов контроля является метод оценки физиологических параметров с использованием фотоплетизмографии. Данная статья посвящена разработке устройства для фотоплетизмографических исследований и алгоритмических методов обработки регистрируемых сигналов для обеспечения мониторинга сердечного ритма с заданной точностью. В работе используются технологии цифровой адаптивной фильтрации полученных сигналов для мониторинга сердечного ритма в условиях внешних механических и электрических помеховых воздействий, ухудшающих точностные характеристики системы, а также разработана архитектура системы и изготовлен макет устройства, который позволил провести измерения для определения оптимального алгоритма цифровой обработки сигналов. При использовании устройства применялись методы адаптивной фильтрации на основе фильтров Винера, фильтров на основе метода наименьших квадратов и Калмановской фильтрации. Разработанное устройство для фотоплетизмографических исследований обеспечило возможность мониторинга сердечного ритма с заданной точностью, контроля текущего состояния организма и может быть использовано в качестве средства диагностики заболеваний сердца The constantly growing interest in the development of new non-invasive and cuff-free methods for measuring the parameters of cardiac activity, the use of which would give the possibility of continuous and remote monitoring of the cardiovascular system, determines the relevance of this work. Numerous publications continue to discuss the advantages and disadvantages of various methods of early diagnosis of cardiovascular disease. However, motion artifacts are a strong hindrance to the accurate assessment of the performance of the cardiovascular system. One of the promising control methods is the method for assessing physiological parameters using photoplethysmography. This article is devoted to the development of a device for photoplethysmographic studies and algorithmic methods for processing recorded signals to ensure monitoring of the heart rate with a given accuracy. The work uses technologies of digital adaptive filtering of the received signals to monitor the heart rate in conditions of external mechanical and electrical interference, which worsen the accuracy characteristics of the system, as well as the architecture of the system and a prototype of the device, which made it possible to carry out measurements to determine the optimal algorithm for digital signal processing. When using the device, the methods of adaptive filtering based on Wiener filters, filters based on the least squares method and Kalman filtering were used. The developed device for photoplethysmographic studies provided the ability to monitor the heart rate with a given accuracy, control the current state of the body and can be used as a means of diagnosing heart diseases

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

scholarly journals Contactless Video-Based Heart Rate Monitoring of a Resting and an Anesthetized Pig

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 442
Author(s):  
Meiqing Wang ◽  
Ali Youssef ◽  
Mona Larsen ◽  
Jean-Loup Rault ◽  
Daniel Berckmans ◽  
...  
Keyword(s):  
Heart Rate ◽  
Estimation Error ◽  
Well Being ◽  
Absolute Error ◽  
Mean Value ◽  
Video Data ◽  
Window Functions ◽  
The Face ◽  
Short Time ◽  
Better Than

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

scholarly journals Dedicated Algorithm for Unobtrusive Fetal Heart Rate Monitoring Using Multiple Dry Electrodes

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4298
Author(s):  
Alessandra Galli ◽  
Elisabetta Peri ◽  
Yijing Zhang ◽  
Rik Vullings ◽  
Myrthe van der Ven ◽  
...  
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Processing System ◽  
Motion Artifacts ◽  
Accurate Estimation ◽  
Fetal Heart Rate Monitoring ◽  
Dry Electrodes ◽  
Textile Electrodes ◽  
Dry Electrode

Multi-channel measurements from the maternal abdomen acquired by means of dry electrodes can be employed to promote long-term monitoring of fetal heart rate (fHR). The signals acquired with this type of electrode have a lower signal-to-noise ratio and different artifacts compared to signals acquired with conventional wet electrodes. Therefore, starting from the benchmark algorithm with the best performance for fHR estimation proposed by Varanini et al., we propose a new method specifically designed to remove artifacts typical of dry-electrode recordings. To test the algorithm, experimental textile electrodes were employed that produce artifacts typical of dry and capacitive electrodes. The proposed solution is based on a hybrid (hardware and software) pre-processing step designed specifically to remove the disturbing component typical of signals acquired with these electrodes (triboelectricity artifacts and amplitude modulations). The following main processing steps consist of the removal of the maternal ECG by blind source separation, the enhancement of the fetal ECG and identification of the fetal QRS complexes. Main processing is designed to be robust to the high-amplitude motion artifacts that corrupt the acquisition. The obtained denoising system was compared with the benchmark algorithm both on semi-simulated and on real data. The performance, quantified by means of sensitivity, F1-score and root-mean-square error metrics, outperforms the performance obtained with the original method available in the literature. This result proves that the design of a dedicated processing system based on the signal characteristics is necessary for reliable and accurate estimation of the fHR using dry, textile electrodes.

scholarly journals RESTING HEART RATE MODERATES THE RELATIONSHIP BETWEEN NEUROPSYCHIATRIC SYMPTOMS, MCI, AND ALZHEIMER’S DISEASE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S641-S641
Author(s):  
Shanna L Burke
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Heart Rate ◽  
Relative Risk ◽  
Neuropsychiatric Symptoms ◽  
Cognitive Status ◽  
Resting Heart Rate ◽  
Data Set ◽  
Increased Risk ◽  
The Relationship

Abstract Little is known about how resting heart rate moderates the relationship between neuropsychiatric symptoms and cognitive status. This study examined the relative risk of NPS on increasingly severe cognitive statuses and examined the extent to which resting heart rate moderates this relationship. A secondary analysis of the National Alzheimer’s Coordinating Center Uniform Data Set was undertaken, using observations from participants with normal cognition at baseline (13,470). The relative risk of diagnosis with a more severe cognitive status at a future visit was examined using log-binomial regression for each neuropsychiatric symptom. The moderating effect of resting heart rate among those who are later diagnosed with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) was assessed. Delusions, hallucinations, agitation, depression, anxiety, elation, apathy, disinhibition, irritability, motor disturbance, nighttime behaviors, and appetite disturbance were all significantly associated (p<.001) with an increased risk of AD, and a reduced risk of MCI. Resting heart rate increased the risk of AD but reduced the relative risk of MCI. Depression significantly interacted with resting heart rate to increase the relative risk of MCI (RR: 1.07 (95% CI: 1.00-1.01), p<.001), but not AD. Neuropsychiatric symptoms increase the relative risk of AD but not MCI, which may mean that the deleterious effect of NPS is delayed until later and more severe stages of the disease course. Resting heart rate increases the relative risk of MCI among those with depression. Practitioners considering early intervention in neuropsychiatric symptomology may consider the downstream benefits of treatment considering the long-term effects of NPS.

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