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

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.

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

Sensors ◽

10.3390/s19153312 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3312 ◽

Cited By ~ 3

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.

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Use of Transforms in Biomedical Signal Processing and Analysis

10.5772/intechopen.98239 ◽

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.

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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 ◽

10.3390/electronics9111905 ◽

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.

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Cyst Detection and Motion Artifact Elimination in Enface Optical Coherence Tomography Angiograms

Applied Sciences ◽

10.3390/app10113994 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3994

Author(s):

Emanuele Torti ◽

Caterina Toma ◽

Stela Vujosevic ◽

Paolo Nucci ◽

Stefano De Cillà ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Optical Coherence Tomography Angiography ◽

Ground Truth ◽

Motion Artifact ◽

Jaccard Index ◽

Motion Artifacts ◽

Optical Coherence ◽

Medical Doctors ◽

Crucial Importance ◽

Novel Algorithm

The correct detection of cysts in Optical Coherence Tomography Angiography images is of crucial importance for allowing reliable quantitative evaluation in patients with macular edema. However, this is a challenging task, since the commercially available software only allows manual cysts delineation. Moreover, even small eye movements can cause motion artifacts that are not always compensated by the commercial software. In this paper, we propose a novel algorithm based on the use of filters and morphological operators, to eliminate the motion artifacts and delineate the cysts contours/borders. The method has been validated on a dataset including 194 images from 30 patients, comparing the algorithm results with the ground truth produced by the medical doctors. The Jaccard index between the algorithmic and the manual detection is 98.97%, with an overall accuracy of 99.62%.

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Motion Artifact Reduction for Wrist-Worn Photoplethysmograph Sensors Based on Different Wavelengths

Sensors ◽

10.3390/s19030673 ◽

2019 ◽

Vol 19 (3) ◽

pp. 673 ◽

Cited By ~ 17

Author(s):

Yifan Zhang ◽

Shuang Song ◽

Rik Vullings ◽

Dwaipayan Biswas ◽

Neide Simões-Capela ◽

...

Keyword(s):

Signal Reconstruction ◽

Motion Artifact ◽

Motion Artifacts ◽

Health Conditions ◽

Average Error ◽

Artifact Reduction ◽

Modular Algorithm ◽

Algorithm Framework ◽

Electrocardiogram Ecg

Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.

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A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography

Sensors ◽

10.3390/s20061596 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1596 ◽

Cited By ~ 1

Author(s):

Shuai Yu ◽

Sheng Liu

Keyword(s):

Heart Rate ◽

Least Squares ◽

Motion Artifact ◽

Recursive Least Squares ◽

Detection Accuracy ◽

Artifact Removal ◽

Ecg Signals ◽

The Right ◽

Electrocardiogram Ecg ◽

Recursive Least Squares Filter

This paper presents a novel adaptive recursive least squares filter (ARLSF) for motion artifact removal in the field of seismocardiography (SCG). This algorithm was tested with a consumer-grade accelerometer. This accelerometer was placed on the chest wall of 16 subjects whose ages ranged from 24 to 35 years. We recorded the SCG signal and the standard electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. These subjects were asked to perform standing and walking movements on a treadmill. ARLSF was developed in MATLAB to process the collected SCG and ECG signals simultaneously. The SCG peaks and heart rate signals were extracted from the output of ARLSF. The results indicate a heartbeat detection accuracy of up to 98%. The heart rates estimated from SCG and ECG are similar under both standing and walking conditions. This observation shows that the proposed ARLSF could be an effective method to remove motion artifact from recorded SCG signals.

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Wristbands in Home-Based Rehabilitation—Validation of Heart Rate Measurement

Sensors ◽

10.3390/s22010060 ◽

2021 ◽

Vol 22 (1) ◽

pp. 60

Author(s):

Magdalena Jachymek ◽

Michał T. Jachymek ◽

Radosław M. Kiedrowicz ◽

Jarosław Kaźmierczak ◽

Edyta Płońska-Gościniak ◽

...

Keyword(s):

Heart Rate ◽

Stress Test ◽

Absolute Error ◽

Concordance Correlation ◽

Heart Rates ◽

Home Based ◽

Accuracy And Precision ◽

Validation Criteria ◽

Precision And Accuracy ◽

Electrocardiogram Ecg

The possibility of using a smartwatch as a rehabilitation tool to monitor patients’ heart rates during exercise has gained the attention of many researchers. This study aimed to evaluate the accuracy and precision of the HR measurement performed by two wrist monitors: the Fitbit Charge 4 and the Xiaomi Mi Band 5. Thirty-one healthy volunteers were asked to perform a stress test on a treadmill. Their heart rates were recorded simultaneously by the wristbands and an electrocardiogram (ECG) at 1 min intervals. The mean absolute error percentage (MAPE), Lin’s concordance correlation coefficient (LCCC), and Bland–Altman analysis were calculated to compare the precision and accuracy of heart rate measurements. The estimated validation criteria were MAPE < 10% and LCCC < 0.8. The overall MAPE and LCCC of the Fitbit were 10.19% (±11.79%) and 0.753 (95% CI: 0.717–0.785), respectively. The MAPE and LCCC of the Xiaomi were 6.89% (±9.75) and 0.903 (0.886–0.917), respectively. The precision and accuracy of both devices decreased with the increased exercise intensity. The accuracy of wearable wrist-worn heart rate monitors varies and depends on the intensity of training. Therefore, the decision to use such a device as a heart rate monitor during in-home rehabilitation should be made with caution.

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Removal of ECG Baseline Wander Using a Resting Cycle Template

Engineering Proceedings ◽

10.3390/ecsa-8-11289 ◽

2021 ◽

Vol 10 (1) ◽

pp. 45

Author(s):

Eladio Altamira-Colado ◽

Miguel Bravo-Zanoguera ◽

Daniel Cuevas-González ◽

Marco Reyna-Carranza ◽

Roberto López-Avitia

Keyword(s):

Spline Interpolation ◽

Motion Artifact ◽

Motion Artifacts ◽

New Method ◽

Daily Activities ◽

Pass Filter ◽

Ecg Signals ◽

Baseline Wander ◽

Low Amplitude ◽

Electrocardiogram Ecg

The development of electrocardiogram (ECG) wearable devices has increased due to its applications on ambulatory patients. ECG signals provide useful information about the heart behavior, but when daily activities are monitored, motion artifacts are introduced producing saturation of the signal, thus losing the information. The typical resolution used to record ECG signals is of maximum 16-bit, which might not be enough to detect low-amplitude potentials and at the same time avoid saturation due to baseline wander, since this last issue demands a low-gain signal chain. A high-resolution provides a more detailed ECG signal under a low gain input, and if the signal is corrupted by motion artifact noise but is not saturated, it can be filtered to recover the signal of interest. In this work, a 24-bit ADC is used to record the ECG, and a new method, the rest ECG cycle template, is proposed to remove the baseline wander. This new method is compared to high-pass filter and spline interpolation methods in their ability to remove baseline wander. This new method presumes that a user is able to establish a rest ECG during his/her daily activities.

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Peak Space Motion Artifact Cancellation Applied to Textile Electrode Waist Electrocardiograms Recording During Outdoors Walking and Jogging

10.1101/2022.01.07.475456 ◽

2022 ◽

Author(s):

Bruce R Hopenfeld

Keyword(s):

Real World ◽

Single Channel ◽

Ground Truth ◽

Motion Artifact ◽

Heart Beat ◽

Pattern Search ◽

Motion Artifacts ◽

Qrs Detection ◽

Detection Scheme ◽

Textile Electrode

Background: Obtaining reliable rate heart estimates from waist based electrocardiograms (ECGs) poses a very challenging problem due to the presence of extreme motion artifacts. The literature reveals few, if any, attempts to apply motion artifact cancellation methods to waist based ECGs. This paper describes a new methodology for ameliorating the effects of motion artifacts in ECGs by specifically targeting ECG peaks for elimination that are determined to be correlated with accelerometer peaks. This peak space cancellation was applied to real world waist based ECGs. Algorithm Summary: The methodology includes successive applications of a previously described pattern-based heart beat detection scheme (Temporal Pattern Search, or TEPS) that can also detect patterns in other types of peak sequences. In the first application, TEPS is applied to accelerometer signals recorded contemporaneously with ECG signals to identify high-quality accelerometer peak sequences (SA) indicative of quasi-periodic motion likely to impair identification of peaks in a corresponding ECG signal. The process then performs ECG peak detection and locates the closest in time ECG peak to each peak in an SA. The differences in time between ECG and SA peaks are clustered. If the number of elements in a cluster of peaks in an SA exceeds a threshold, the ECG peaks in that cluster are removed from further processing. After this peak removal process, further QRS detection proceeds according to TEPS. Experiment: The above procedure was applied to data from real world experiments involving four sessions of walking and jogging on a dirt road for approximately 20-25 minutes. A compression shirt with textile electrodes served as the ground truth recording. A textile electrode based chest strap was worn around the waist to generate a single channel signal upon which to test peak space cancellation/TEPS. Results: Both walking and jogging heart rates were generally well tracked. In the four recordings, the percentage of 5 second segments within 10 beats/minute of reference was 96%, 99%, 92% and 96%. The percentage of segments within 5 beats/minute of reference was 86%, 90%, 82% and 78%. There was very good agreement between the RR intervals associated with the reference and waist recordings. For acceptable quality segments, the root mean square sum of successive RR interval differences (RMSSD) was calculated for both the reference and waist recordings. Next, the difference between waist and reference RMSSDs was calculated (∆RMSSD). The mean ∆RMSSD (over acceptable segments) was 4.6 m, 5.2 ms, 5.2 ms and 6.6 ms for the four recordings. Conclusion: Given that only one waist ECG channel was available, and that the strap used for the waist recording was not tailored for that purpose, the proposed methodology shows promise for waist based sinus rhythm QRS detection.

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

Scientific Reports ◽

10.1038/s41598-019-55599-5 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

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.

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