Heart Rate Estimation Research Articles

Remote Vital Sign Monitoring With Reduced Random Body Swaying Motion Using Heartbeat Template and Wavelet Transform Based on Constellation Diagrams

Remote vital signs monitoring using millimeter-wave (mmWave) sensors has gained lots of attention because of their contactless, portable advantages. However, their received signals are more sensitive to random body motions (RBM) which degrades the accuracy of heart rate (HR) detection. To overcome this challenge, multi-input multi-output (MIMO) configuration can be used to reduce RBM's impact as each channel has different points of view with respect to the subject under test (SUT). Here we propose the use of a Frequency Modulated Continuous Wave (FMCW) radar from Texas Instruments (TI) at 77 GHz to collect data from its 192-channel configuration. Since vital sign information extracted using Arctangent Demodulation (AD) could be corrupted by either RBM or respiratory harmonics, a method is needed to minimize such effects. Hence, we develop an algorithm where a Heartbeat Template (HBT) is extruded based on the Constellation Diagram that shows the Quadrature signals from the target's range profile. The HBT is then used to design an adapted-wavelet for Continuous Wavelet Transform (CWT) to magnify the heartbeat signals. Under circumstances where RBM overwhelms the heartbeat signal that the HBT cannot completely reduce its effects, a spectral-based HR selection method is also developed to estimate the HR. By employing the proposed methods, we have reduced mean-error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mu _e}$</tex-math></inline-formula> of HR estimation significantly from 9 bpm to less than 2 bpm.

A dynamic reconfigurable wearable device to acquire high quality PPG signal and robust heart rate estimate based on deep learning algorithm for smart healthcare system

Photoplethysmography (PPG) is a noninvasively technique used to detect vital signs such as heart rate (HR), saturation of peripheral oxygen (SpO2) and blood pressure (BP). Recently, wearable finger-type PPG devices are increasingly developed toward convenience of person under monitoring (PUM). Two most critical features of wearable PPG devices are high accuracy and long operation time. To enhance these functions, this paper proposes a new architecture to select, process and transfer only high quality PPG signals. Hence, data quality is significantly improved and power consumption on wireless module is minimized. In the proposed architecture, parameters of PPG sensor are reconfigured in real-time being suitable with skin characteristics of PUM. Moreover, the adaptive LED current control algorithm is proposed to dynamically change the LED current to remove various motion artifacts (MA) and get high amplitude PPG signal. We also develop a heart rate (HR) estimation framework utilizing deep learning (DL) model based on convolutional neural network (CNN) and long short-term memory (LSTM) network. The combination of CNN-LSTM can extract features from both spatial and temporal correlation. A light-weight model with two CNN-LSTM layers is built to estimate HR with in only 5s. For validation, we conduct experiments with volunteers doing various physical exercises. The results show PPG signals with high amplitude and signal-to-noise ratio (SNR). HR estimation is more accurate even during irregular and muscle strength exercises. The proposed adaptive architecture and DL-based HR estimation can overcome MA and minimize the power consumption on wireless transfer module of wearable PPG devices.

Heart Rate Estimation in Sports Based on Multi-Sensor Data for Sports Intensity Prediction

The heart rate (HR) is the most common measurement of the cardiovascular system. It reflects not only the cardiovascular function, but also the degree of recovery, and has high reliability. The heart rate monitoring can be used in athlete selection, sports training, medical supervision, and fitness to avoid the blindness of exercise intensity arrangement, provide an objective quantitative standard for scientific fitness, and improve the sports performance through monitoring sports intensity. In order to accurately predict the sports intensity, this paper adopts ECG signals and pulse wave to learn an ordinal regression model that can utilize the order relation between different sports intensity level. The experimental results have demonstrated the effectiveness of the proposed sports intensity method.

Socially meaningful visual context either enhances or inhibits vocalisation processing in the macaque brain

Social interactions rely on the interpretation of semantic and emotional information, often from multiple sensory modalities. Nonhuman primates send and receive auditory and visual communicative signals. However, the neural mechanisms underlying the association of visual and auditory information based on their common social meaning are unknown. Using heart rate estimates and functional neuroimaging, we show that in the lateral and superior temporal sulcus of the macaque monkey, neural responses are enhanced in response to species-specific vocalisations paired with a matching visual context, or when vocalisations follow, in time, visual information, but inhibited when vocalisation are incongruent with the visual context. For example, responses to affiliative vocalisations are enhanced when paired with affiliative contexts but inhibited when paired with aggressive or escape contexts. Overall, we propose that the identified neural network represents social meaning irrespective of sensory modality.

A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar.

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland–Altman consistency analysis statistical method with the algorithm’s respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis.

Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.

Heart rate estimation from facial videos with motion interference using T-SNE-based signal separation.

Remote photoplethysmography (RPPG) can detect heart rate from facial videos in a non-contact way. However, head movement often affects its performance in the real world. In this paper, a novel anti-motion interference method named T-SNE-based signal separation (TSS) is proposed to solve this problem. TSS first decomposes the observed color traces into pulse-related vectors and noise vectors using the T-SNE algorithm. Then, it selects the vector with the most significant spectral peak as the pulse signal for heart rate measurement. The proposed method is tested on a self-collected dataset (17 males and 8 females) and two public datasets (UBFC-RPPG and VIPL-HR). Experimental results show that the proposed method outperforms state-of-the-art methods, especially on the videos containing head movements, improving the Pearson correlation coefficient by 5% compared with the best contrasting method. To summarize, this work significantly strengthens the motion robustness of RPPG, which makes a substantial contribution to the development of video-based heart rate detection.

An Effective Photoplethysmography Heart Rate Estimation Framework Integrating Two-Level Denoising Method and Heart Rate Tracking Algorithm Guided by Finite State Machine

In order to achieve accurate heart rate (HR) estimation in complex scenes, this paper presents an effective photoplethysmography (PPG) HR estimation framework integrating two-level denoising method and HR tracking algorithm guided by finite state machine (FSM). Aiming at solving the problems of low signal-to-noise ratio and co-frequency (the noise frequency is close to the HR frequency) caused by motion artifacts, the two-level denoising method consisting of the cascaded adaptive filtering and the differential denoising guided by FSM are designed to remove motion-related noises in PPG signals. In order to solve the problem of HR tracking error caused by poor wrist contact, the HR tracking algorithm guided by FSM is proposed to obtain the global optimization capability. The results of HR estimation experiments conducted on the IEEE Signal Processing Cup database and the WeData database created by ourselves show that the proposed framework can effectively cope with the problems of low signal-to-noise ratio and co-frequency. Even if tracking errors occur due to poor wristband contact, the proposed HR tracking algorithm guided by FSM can correct them in time when the HR component appears again. The average absolute error of HR estimation on the two databases are 1.76 BPM (beats per minute) and 2.77 BPM, respectively, which is more accurate compared to other algorithms.

Validation of heart rate spot-check protocol to measure circadian variation and heart rate in healthy dogs and dogs with atrial fibrillation

IntroductionAlternatives for out-of-clinic heart rate (HR) measurement are required to optimise the management of atrial fibrillation (AF) in dogs. Additionally, the presence of circadian variation (CV) in HR in pet dogs remains unknown.We aimed to identify the number and duration of spot-checks required for an accurate estimation of 24-hour HR in canine AF. Circadian variation in HR was examined in healthy dogs and dogs with AF, and spot-check-derived HR was compared with a CV-derived gold standard. Materials and methodsAmbulatory electrocardiogram data from healthy dogs and dogs with AF were retrospectively analysed. Heart rate was calculated from the entire recording and pre-defined periods (spot-checks) of one hour to 30 and 60 s in duration. Circadian variation in HR was determined by cosinor analysis. Bias and limits of agreement of means and median HR with mesor HR were determined by correlation and Bland–Altman analysis. ResultsCircadian variation in HR was identified not only in 18/22 healthy dogs and 14/21 AF dogs but only on ambulatory electrocardiogram recordings.Four-hourly spot-checks provided the most accurate estimate of mesor HR in healthy dogs (bias of the median over 30 s 7.70, limits of agreement 7.48), whereas, in dogs with AF, four, six and eight-hourly spot-checks provided reliable estimates of mesor HR (bias within −1.29 and −29.5). ConclusionsFour, six and eight-hourly HR spot-checks can estimate 24-hourly HR in dogs with AF. There was CV in HR in most healthy pet dogs and dogs with AF. Spot-check protocols cannot identify CV in HR.

Contactless continuous heart rate monitoring system using ballistocardiography.

Cardiovascular disease is the number one cause of death in the world and is a serious problem. In the case of cardiopulmonary arrest due to myocardial infarction, the survival rate is as low as 13.3% one month after resuscitation, which birthed the need for continuous heart monitoring. In this study, we develop a Ballistocardiogram (BCG) measurement system using a load cell installed on a chair and a heart rate estimation algorithm that is robust to waveform changes, with the aim of constructing a non-contact heart rate acquisition system. The proposed system was evaluated by utilizing data obtained from 13 healthy subjects and 1 subject with abnormal ECG who were simultaneously measured with ECG. The output of the BCG system was confirmed to change with the same period as the ECG data obtained as the correct answer, and the synchronization of the R-peak positions was confirmed for all cases. As a result of comparing the heart rate intervals estimated from BCG and those obtained from ECG, it was confirmed that the same heart rate variability (HRV) features could be obtained even for abnormal ECG subject.

Heart rate estimation and validation algorithm for fetal phonocardiography

Objective. Fetal heart rate (FHR) is an important parameter for assessing fetal well-being and is usually measured by doppler ultrasound. Fetal phonocardiography can provide non-invasive, easy-to-use and passive alternative for fetal monitoring method if reliable FHR measurements can be made even in noisy clinical environments. Therefore, this work presents an automatic algorithm to determine FHR from the fetal heart sound recordings in a noisy clinical environment. Approach. Using an electronic stethoscope fetal heart sounds were recorded from the expecting mother’s abdomen, during weeks 30–40 of their pregnancy. Of these, 60 recordings were analyzed manually by two observers to obtain reference heart rate measurement. An algorithm was developed to determine FHR using envelope detection and autocorrelation of the signals. Algorithm performance was improved by implementing peak validation algorithm utilizing knowledge of valid FHR from prior windows and power spectral density function. The improvements in accuracy and reliability of algorithm were measured by mean absolute error (MAE) and positive percent agreement. Main results. By including the validation step, the MAE reduced from 11.50 to 7.54 beats per minute and positive percent agreement improved from 81% to 87%. Significance. We classified the recordings into good, moderate and poor quality to understand how the algorithm works in each of the case. The proposed algorithms provide good accuracy overall but are sensitive to the noises in recording environment that influence the quality of the signals.

Survey, Analysis and Comparison of Radar Technologies for Embedded Vital Sign Monitoring.

Contactless vital sign monitoring systems are becoming increasing in demand for a wide range of biomedical applications. Millimetre-wave radars and embedded signal processing are the most promising technologies to enable non-contact vital signs monitoring. In this work, the challenging task of heart rate estimation from radar data has been addressed. Three different radar systems from Infineon, Texas Instruments and Acconeer, and four algorithms, FFT, Median-FFT, STFT and Median-STFT, have been analysed and compared against a reference sensor. Accuracy, as well as power figures, have been reported for all the radar systems. A dataset of 16 volunteers has been acquired, yielding a total of 400 minutes of radar-recorded vital sign data. The accuracy of the four investigated algorithms has been reported on average and per subject for every radar. The algorithm exploiting the Short Time Fourier Transform (STFT) is able to achieve an error as low as 0.02% on a single person and of 6.4% in heart rate estimation on average across the whole dataset.

Lightweight neural network based model for real-time precise HR monitoring during high intensity workout using consumer smartwatches.

Mobile and wearable electronics is one of the rapidly developing areas of high technologies, which regularly appear new devices that offer new features for monitoring our health, level of physical exertion and everyday activity. From the point of view of medicine and sports, usually wearable devices are used to solve one of two tasks: round-the-clock continuous heart rate monitoring, and the control of the heart rate during workout. One of the drawbacks of such devices is the calculation errors that are caused by the motion artifacts. The most critical of them are those that arise in some cases when performing high-intensity training's, which are conjugate with intense hands movements, such as sprint running or boxing. In addition, limited resources of wearable devices do not always allow usage of very complex algorithms for processing certain events. In our work, an ultra-lightweight framework for a precise real-time heart rate monitoring during the high intensity physical exercises is developed. The model is a combination of a digital signal processing and deep convolutional and recurrent neural networks approaches. From the personalization point of view, the effect of anthropometric parameters has been studied. The model shows an average mean absolute error of 2.4±2.8 bpm during 5 fold cross-validation on an internal dataset, 2.9±3.4 bpm when evaluated on 12 IEEE SPC subjects, and 4.8±5.3 bpm when evaluated on 24 subjects from Wonkwang University dataset. Such results exceed the current state of the art solutions both in terms of the achieved accuracy of heart rate estimation and consumed computational resources. Clinical relevance- This work aims to provide a robust and lightweight neural network based framework for a real time heart rate estimation during high intensity physical exercises.

Remote Photoplethysmography and Heart Rate Estimation by Dynamic Region of Interest Tracking.

Remote photoplethysmography (PPG) estimates vital signs by measuring changes in the reflected light from the human skin. Compared to traditional PPG techniques, remote PPG enables contactless measurement at a reduced cost. In this paper, we propose a novel method to extract remote PPG signals and heart rate from videos. We propose an algorithm to dynamically track regions of interest (ROIs) and combine the signals from all ROIs based on signal qualities. To maintain a stable frame rate and accuracy, we propose a dynamic down-sampling approach, which makes our system robust to the different video resolutions and user-camera distances. We also propose the strategy of adaptive measurement time to estimate HR, which can achieve comparable accuracy in HR estimation while reducing the average measurement time. To test the accuracy of the proposed system, we have collected data from 30 subjects with facial masks. Experimental results show that the proposed system can achieve 3.0 bpm mean absolute error in HR estimation.

Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit.

Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonary signals from videos in NICU setup. We have proposed an end-to-end deep learning (DL) model that integrates a non-learning-based approach to generate surrogate ground truth (SGT) labels for supervision, thus refraining from direct dependency on true ground truth labels. We have performed an extended qualitative and quantitative analysis to examine the efficacy of our proposed DL-based pipeline and achieved an overall average mean absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2 bpm in the estimated heart rate.

A Novel Method for the Extraction of Fetal ECG Signals from Wearable Devices.

The role of fetal surveillance for the prediction and timely assessment of fetal distress is widely established. Fetal ECG (fECG) monitoring via wearable devices is a feasible solution for performing continuous monitoring of fetal wellbeing and it has seen a net increase in popularity in recent years. In this paper, we propose a novel adaptation of the Smart AdaptiVe Ecg Recognition (SAVER) algorithm for the detection of fECG in long-duration recordings acquired in clinical as well as unconventional settings. The methodology was trained and tested on 50 recordings of duration 1 hour ( 59.33 ±5.54 min) obtained using the Monica AN24 fetal monitor. We validated the performance against the automatic extraction performed by the Monica DK software. Our results show superior reliability of the proposed methodology in extracting fECG and associated estimates of fetal heart rate (fHR). Clinical relevance- The proposed methodology provides an efficient and reliable approach for the extraction of fECG signals acquired via wearable technologies, enabling continuous monitoring of fECG in and outside clinical settings.

Evaluation of a Wearable System for Fetal ECG Monitoring Using Cooperative Sensors.

Fetal electrocardiography (fECG) has gotten widespread interest in the last years as technology for fetal monitoring. Compared to cardiotocography (CTG), the current state of the art, it can be designed in smaller formfactor and is thus suited for long-term and unsupervised monitoring. In the present study we evaluated a wearable system which is based on CSEM's cooperative sensors, a versatile technology that allows for the measurement of multiple biosignals and an easy integration into a garment or patch. The system was tested on 25 patients with singleton pregnancies and an age of gestation ≥ 37 weeks. To reject unreliable fetal heart rate (fHR) estimations, the signal processing algorithm provides a signal quality index. In 12 out of 21 patients available for analysis, a good performance of fHR estimations was obtained with a mean absolute error < 5 bpm and an acceptance rate >70%. However, the remaining 9 patients showed low acceptance rates and high errors. Besides investigating the source of these high errors, future work includes the investigating improved signal processing algorithms, different body positions and the use of dry electrodes. Clinical Relevance - The aim of this work is to develop a wearable system that can be offered in hospitals as an alternative to cardiotocography, or as a home monitoring tool for at risk fetuses, in the era of evolving telemedicine.

DeepPulse: An Uncertainty-aware Deep Neural Network for Heart Rate Estimations from Wrist-worn Photoplethysmography.

Wearable Photoplethysmography (PPG) has gained prominence as a low cost, unobtrusive and continuous method for physiological monitoring. The quality of the collected PPG signals is affected by several sources of interference, predominantly due to physical motion. Many methods for estimating heart rate (HR) from PPG signals have been proposed with Deep Neural Networks (DNNs) gaining popularity in recent years. However, the "black-box" and complex nature of DNNs has caused a lack of trust in the predicted values. This paper contributes DeepPulse, an uncertainty-aware DNN method for estimating HR from PPG and accelerometer signals, with aims of increasing trust of the predicted HR values. To the best of the authors' knowledge no PPG HR estimation method has considered aleatoric and epistemic uncertainty metrics. The results show DeepPulse is the most accurate method for DNNs with smaller network sizes. Finally, recommendations are given to reduce epistemic uncertainty, validate uncertainty estimates, improve the accuracy of DeepPulse as well as reduce the model size for resource-constrained edge devices.

A U - Net Deep Learning Model for Infant Heart Rate Estimation from Ballistography.

Ballistography(BSG) is a non-intrusive and low- cost alternative to electrocardiography (ECG) for heart rate (HR) monitoring in infants. Due to the inter-patient variance and susceptibility to noise, heartbeat detection in the BSG waveform remains a challenge. The aim of this study was to estimate HR from a bed-based pressure mat BSG signal using a deep learning approach. We trained a U-Net deep neural network through supervised learning by deriving ground truth as the location of the heartbeats from simultaneously recorded ECG signals after peak matching. For improved generalization, we modified an existing U - Net to include an IC-layer. A predictive performance of 80% was achieved using the U-Net without the IC-layer. The inclusion of the IC-layer, while improving the generalization ability of the model to detect heartbeats, did not improve the HR estimation performance.

Heart rate detection using single-channel Doppler radar system.

A number of algorithms have been developed to extract heart rate from physiological motion data with the Doppler radar system. Yet, it is very challenging to elimi-nate the noise associated with surroundings, especially with a single-channel Doppler radar system. However, single-channel Doppler radars provide the advantage of operating at lower power. Additionally, the heart rate extraction using single-channel Doppler radar has remained somewhat unexplored. This has motivated us to develop effective signal processing algorithms for signals received from single-channel Doppler radars. In this paper, we have proposed and studied three algorithms for estimating heart rate. The first algorithm is based on applying FFT on an FIR filtered signal. In the second algorithm, autocorrelation was performed on the filtered data. Thirdly, we used a peak finding algorithm in conjunction with a moving average preceded by a clipper to determine the heart rate. The results obtained were compared with the heart rate readings from a pulse oximeter. With a mean difference of 2.6 bpm, the heart rate from Doppler radar matched that from the pulse oximeter most frequently when the peak finding algorithm was used. The results obtained using autocorrelation and peak finding algorithm (with standard deviations of 2.6 bpm and 4.0 bpm) suggest that a single channel Doppler radar system can be a viable alternative to contact heart rate monitors in patients for whom contact measurements are not feasible.