Heartbeat Signals Research Articles

Respiration and heart rates measurement using 77GHz FMCW radar with blind source separation algorithm.

This paper presents a blind source separation based signal processing method of measuring respiration and heartbeat rates using frequency modulated continues wave (FMCW) radar with working frequency range 77GHz-81GHz. To improve signal quality, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used in preprocessing stage to first decompose the phase signal into several intrinsic mode functions (IMFs) and then the phase signal is reconstructed by adding the selected IMFs. The accurate measurement results can be obtained by the proposed method both in separating respiratory and heartbeat signals of one person (the average deviations are 1.1 beat per minute and 6.8 beat per minute respectively), and in separating respiratory signals of two persons.

Driver Vital Signs Monitoring Using Millimeter Wave Radio

As automobiles have become an essential part to facilitate our daily life, advanced driver assistance systems (ADASs) have been gaining more and more interest in assisting drivers to enhance both safety and convenience. To respond timely in case of an emergency, ADAS needs to keep track of the driver’s health/consciousness, which is generally achieved by monitoring the driver’s vital signs, including respiration rate (RR), heart rate (HR), and heart rate variability (HRV). However, most of the state-of-art solutions need to assume that the human is stationary, which does not hold in practical driving scenarios. To tackle the problem, we propose a novel system, which can estimate driver’s RR, HR, and interbeat intervals (IBIs) in the presence of driver’s motion artifacts using commercial millimeter-wave (mmWave) radio. The system consists of two key components. First, to extract the reflection signals containing vital signals, the motion artifacts are first removed by a novel motion compensation module, followed by the periodicity check to identify the components with vital signals. Second, the respiration and heartbeat signals are reconstructed by jointly optimizing the decomposition of all the extracted compound vital signals over different range-azimuth bins. We evaluate the system performance in a real driving environment and investigate the impact of different parameters, including the device locations, pavement conditions, and motion types. The experimental results show that the proposed system can achieve a median error of 0.16 respiration per minute (RPM), 0.82 beat per minute (BPM), and 46 ms for RR, HR, and IBI estimations, corresponding to the relative accuracy of 99.17%, 98.94%, and 94.11%, respectively.

Perfluorooctane Sulfonamide (PFOSA) Induces Cardiotoxicity via Aryl Hydrocarbon Receptor Activation in Zebrafish.

Perfluorooctane sulfonamide (PFOSA), a precursor of perfluorooctanesulfonate (PFOS), is widely used during industrial processes, though little is known about its toxicity, particularly to early life stage organisms that are generally sensitive to xenobiotic exposure. Here, following exposure to concentrations of 0.01, 0.1, 1, 10, and 100 μg/L PFOSA, transcriptional, morphological, physiological, and biochemical assays were used to evaluate the potential effects on aquatic organisms. The top Tox functions in exposed zebrafish were related to cardiac diseases predicted by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Ingenuity Pathway Analysis (IPA) analysis. Consistent with impacts predicted by transcriptional changes, abnormal cardiac morphology, disordered heartbeat signals, as well as reduced heart rate and cardiac output were observed following the exposure of 0.1, 1, 10, or 100 μg/L PFOSA. Furthermore, these PFOSA-induced cardiac effects were either prevented or alleviated by supplementation with an aryl hydrocarbon receptor (AHR) antagonist or ahr2-morpholino knock-down, uncovering a seminal role of AHR in PFOSA-induced cardiotoxicity. Our results provide the first evidence in fish that PFOSA can impair proper heart development and function and raises concern for PFOSA analogues in the natural environment.

ECG Classification Using Combination of Linear and Non-Linear Features with Neural Network.

In this paper, we present an approach to improve the accuracy and reliability of ECG classification. The proposed method combines features analysis of linear and non-linear ECG dynamics. Non-linear features are represented by complexity measures of assessment of ordinal network non-stationarity. We describe the basic concept of ECG partitioning and provide an experiment on PQRST complex data. The results demonstrate that the proposed technique effectively detects abnormalities via automatic feature extraction and improves the state-of-the-art detection performance on one of the standard collections of heartbeat signals, the ECG5000 dataset.

Research and Design of Radar System for Respiratory and Heartbeat Signal Detection

The respiratory and heartbeat signals can accurately reflect the health status of the tester, which is of great clinical significance. Compared with the traditional contact detection method, the non-contact radar detection method does not require the tester to wear any sensor equipment and will not cause any discomfort to the tester. The frequency modulated continuous wave (FMCW) radar has the characteristics of simple structure, high resolution, strong stability, and low transmission power and is used for the detection of respiratory and heartbeat signals. This paper designs a low-power, low-cost respiratory, and heartbeat signal detection system based on FMCW radar. In addition, the variational modal decomposition (VMD) method is used to separate respiration and heartbeat signals to obtain accurate respiration and heartbeat rates. The results show that the radar system for detecting respiratory and heartbeat signals has high detection accuracy.

Improving heart rate variability information consistency in Doppler cardiogram using signal reconstruction system with deep learning for Contact-free heartbeat monitoring

A contact-free continuous heart rate variability (HRV) analysis is required to conduct daily heart monitoring and minimize physical contact during medical remedies owing to COVID-19. This paper suggests a Doppler cardiogram (DCG) signal processing and reconstruction system that enables the standard deviation of normal-to-normal peaks (SDNN) obtained from DCG to be used as an actual HRV index. The heartbeat signals of twelve healthy adults were recorded. Three electrodes and a Doppler radar module were used to record the electrocardiogram (ECG) and DCG signals, respectively. To optimize the performance of the signal reconstruction system, two signal processing methods were applied to the dataset. These DCG signals were reconstructed into a signal that mimicked the ECG using a variational autoencoder (VAE), to enhance the consistency of the SDNN. The synthetic signal quality was assessed by comparing the SDNN of the synthetic ECG with that of the reference ECG. A total of 1,430 signals were reconstructed to achieve a valid SDNN. A unified analysis of the signal reconstruction results using different signal processing methods was built up to raise the consistency growth. The final result of the signal reconstruction system represented a consistency improvement of 75.5%, compared to the SDNN of the input DCG.

A wavelet-based capsule neural network for ECG biometric identification

Electrocardiogram (ECG) signals have received a high level of attention from the biometric research community due to their unique nature for each person, which makes them suitable for developing accurate and reliable human identification systems. Although most existing ECG-based biometric recognition methods have received prominent results, several consecutive heartbeat segments are used in their approaches to achieve high accuracy, which is challenging to apply in biometric systems deployed in real-world applications. This paper proposes a new approach for human identification via ECG, based on a combination of Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) along with a novel kind of deep learning technique known as Capsule network. The CWT is used to transform a single heartbeat signal into the time–frequency domain, and the DWT is adopted to extract spectral information of 2D frequency-time scalogram images to further improve accuracy. The discrete wavelet coefficients are then used as input to the capsule network promoting the recognition performance due to its high learning capacities. To support real-life practicality of ECG biometric identification system, the effectiveness and efficiency of our approach were evaluated over four databases that include normal and abnormal ECG records: PTB Diagnosis ECG (PTB), MIT-BIH Arrhythmia, MIT-BIH Normal Sinus Rhythm (NSRDB), and the MIT-BIH ST Change (STDB) databases. Experimental results demonstrate that our proposed method was able to achieve high identification accuracies and outperforming other state-of-the-art methods, by achieving an accuracy of 99.5%, 98.1%, 98.2%, and 100% on the PTB, MIT-BIH arrhythmia, STDB, and NSRDB respectively. Furthermore, the approach showed very good generalization ability since the training and test sets were completely different, which demonstrates the feasibility to promote the application of our approach in practice.

A medical radar system for non-contact vital sign monitoring and clinical performance evaluation in hospitalized older patients

Medical radar plays an important role in non-contact vital sign monitoring. However, radar is sensitive and susceptible to noise that deteriorates signal quality, making its application unstable in medical practice settings. Previous studies have focused on detecting vital signs from radar output, but mainly used high-quality signals in laboratory settings. Therefore, signal quality evaluation to maintain good-quality signals for processing is crucial for radar applications. This paper presents a vital sign monitoring system using medical radar that automatically evaluates signal quality to eliminate unsatisfactory signals using the support-vector machine and to extract heartbeat and respiration signals using the singular value decomposition method. To evaluate the system performance, we tested it on 10 healthy subjects and compared the obtained vital signs with those measured using a contact-type ECG device and a respiration belt. Two evaluation steps were conducted. First, we measured 10 subjects for evaluating the signal quality in a laboratory setting. The results showed that the accuracy for each subject archive was 93.2%–100%. Second, we extracted vital signs including respiration rate (RR) and the heart rate (HR) following signal quality classification. The results showed RR and HR deviation from 0 to 1 bpm, and 0 to 4 bpm, respectively. Moreover, the system was applied in a real medical practice setting for continuously monitoring the bedside of hospitalized older patients at a hospital.

Breathing affects self-other voice discrimination in a bodily state associated with somatic passivity.

A growing number of studies have focused on identifying cognitive processes that are modulated by interoceptive signals, particularly in relation to the respiratory or cardiac cycle. Considering the fundamental role of interoception in bodily self‐consciousness, we here investigated whether interoceptive signals also impact self‐voice perception. We applied an interactive, robotic paradigm associated with somatic passivity (a bodily state characterized by illusory misattribution of self‐generated touches to someone else) to investigate whether somatic passivity impacts self‐voice perception as a function of concurrent interoceptive signals. Participants' breathing and heartbeat signals were recorded while they performed two self‐voice tasks (self‐other voice discrimination and loudness perception) and while simultaneously experiencing two robotic conditions (somatic passivity condition; control condition). Our data reveal that respiration, but not cardiac activity, affects self‐voice perception: participants were better at discriminating self‐voice from another person’s voice during the inspiration phase of the respiration cycle. Moreover, breathing effects were prominent in participants experiencing somatic passivity and a different task with the same stimuli (i.e., judging the loudness and not identity of the voices) was unaffected by breathing. Combining interoception and voice perception with self‐monitoring framework, these data extend findings on breathing‐dependent changes in perception and cognition to self‐related processing.

Highly sensitive and stretchable piezoelectric strain sensor enabled wearable devices for real-time monitoring of respiratory and heartbeat simultaneously

The World Health Organization has declared COVID-19 a pandemic. The demand for devices or systems to diagnose and track COVID-19 infections noninvasively not only in hospitals but also in home settings has led to increased interest in consumer-grade wearables. A common symptom of COVID-19 is dyspnea, which may manifest as an increase in respiratory and heart rates. In this paper, a novel piezoelectric strain sensor is presented for real-time monitoring of respiratory and heartbeat signals. A highly sensitive and stretchable piezoelectric strain sensor is fabricated using a piezoelectric film with a serpentine layout. The thickness of the patterned PVDF flexible piezoelectric strain sensor is only 168 μm, and the voltage sensitivity reaches 0.97 mV/μɛ. The effective modulus is 13.5 MPa, which allows the device to fit to the skin and detect the small strain exhibited by the human body. Chest vibrations are captured by the piezoelectric sensor, which produces an electrical output voltage signal conformally mapped with respiratory–cardiac activities. The separate heart activity and respiratory signals are extracted from the mixed respiratory–cardiac signal by an empirical mode decomposition data processing algorithm. By detecting vital signals such as respiratory and heart rates, the proposed device can aid early diagnosis and monitoring of respiratory diseases such as COVID-19.

A Vital Signs Fast Detection and Extraction Method of UWB Impulse Radar Based on SVD.

The identification of weak vital signs has always been one of the difficulties in the field of life detection. In this paper, a novel vital sign detection and extraction method with high efficiency, high precision, high sensitivity and high signal-to-noise ratio is proposed. Based on the NVA6100 pulse radar system, the radar matrix which contains several radar pulse detection signals is received. According to the characteristics of vital signs and radar matrices, the Singular Value Decomposition (SVD) is adopted to perform signal denoising and decomposition after preprocessing, and the temporal and spatial eigenvectors of each principal component are obtained. Through the energy proportion screening, the Wavelet Transform decomposition and linear trend suppression, relatively pure vital signs in each principal component, are obtained. The human location is detected by the Energy Entropy of spatial eigenvectors, and the respiratory signal and heartbeat signal are restored through a Butterworth Filter and an MTI harmonic canceller. Finally, through an analysis of the performance of the algorithm, it is proved to have the properties of efficiency and accuracy.

A Survey on Touchless Heart Rate Measurement Using Facial Expression for Covid Patient

Abstract: This research describes both a touch-free heartbeat detection system and a cardiopulmonary signal modeling technique. A vector network analyzer is used to test a microwave system for detection of a heartbeat signal at a distance of 1 m from a person. The developed system can detect heartbeat signals and adjust their frequency and strength. Measurements are taken at 2.4, 5.8, 10, 16, and 60 GHz, as well as at power levels ranging from 0 to -27 dBm. Based on data for both breathing and heartbeats, a model of the recorded signals reflecting cardiopulmonary activity is provided. The heartbeat rate and variability are obtained from the modeling signal using wavelet and classic filters. 0and-20dB Keywords: Haar cascade algorithm, Image processing, cardiopulmonary signal modeling technique, Testing.

Using ECG signal as an entropy source for efficient generation of long random bit sequences

Electrocardiogram (ECG) signal produced by the human heart has been investigated as a potential entropy source for cryptographic random bit generation for a long time. The throughput of existing methods remains as the bottleneck for its deployment in practical applications. To overcome this problem, we develop a Bernoulli entropy source by processing a single heartbeat ECG signal to obtain a long random bit sequence (RBS). The proposed method converts the signal into an IID (independent and identically distributed) source of entropy using efficient interpolation and optimization techniques. Several heartbeat signals, obtained from two different databases, were used to test the entropy source generating RBSs with one million bits. The entropy source was evaluated with the latest NIST recommendation for IID source validation and it passed all recommended tests and the average min-entropy obtained from several heartbeat signals of different individuals was close to the perfect entropy value of 1.0. It was also observed that the entropy increases monotonically with the increase of the length of keys. The proposed method can efficiently produce a long RBS for cryptographic applications, such as key generation for one-time pad and image encryption. The method could be further explored to generate a true random number with a personalized signature, which is crucial for information security in the future generation of computing.

Mutual Interference Suppression Using Signal Separation and Adaptive Mode Decomposition in Noncontact Vital Sign Measurements

Noncontact vital sign measurements based on millimeter-wave radar can realize long-range detection of respiratory and heartbeat signals, therefore it is gradually applied in more and more scenes. With an increase in the number of millimeter-wave radar devices, the mutual interference between radar signals will occur, which will increase the noise floor. Moreover, respiratory and heartbeat signals are relatively weak and easy to be submerged by the high noise floor. To solve this problem, we present a novel method for suppressing mutual interference and extracting vital signs using improved morphological component analysis (IMCA) and an adaptive parameter optimization variational mode decomposition (APVMD) algorithm. The IMCA algorithm is used to suppress the mutual interference based on the different sparsities of components in different sparse domains, and this improvement solves the inability of the MCA algorithm to detect the interfered signal. Then, the APVMD algorithm is used to extract respiratory and heartbeat signals from the signal containing residual noise. The rate of energy loss is used as the index to optimize the parameters, and the step size of the penalty value is adaptively adjusted according to the center frequency of the mode. The simulation and field experiment results show that the proposed method can significantly improve the signal-to-noise ratios (SNRs) of respiratory and heartbeat signals in the case of mutual interference between radar signals. Moreover, a comparison with other interference suppression and extraction methods shows that the proposed method better improves the SNR and accuracy.

Non-Contact Heart Rate Monitoring Based on Millimeter Wave Radar

Millimeter Wave(mmWave) radar has been widely used in vital sign monitoring with non-contact and privacy protection. To reduce the effect of random body motion, respiration, and its harmonics on heart rate estimation, the heart rate monitoring based on frequency-modulated continuous-wave (FMCW) radar is researched in this paper. First, a vital sign signal extraction algorithm is designed for the Range Bin variation caused by random body motion and heavy breathing. Second, a heartbeat signal extraction algorithm is designed based on Adaptive Notch Filter (ANF) and Empirical Wavelet Transform (EWT). The harmonic of respiration will be suppressed, and the heartbeat signal will be separated. Finally, the weighted estimation is performed according to the relationship of harmonics of the heartbeat signal to obtain the heart rate. Twenty subjects are invited to the experiment. The experimental results show that the proposed method can improve the signal-to-noise ratio (SNR), reduce the harmonic interference, and estimate the heart rate with a mean absolute error less than 4BPM.

Learning-Refined Integral Null Space Pursuit Algorithm for Noncontact Multisubjects Vital Signs Measurements Using SFCW-UWB and IR-UWB Radar

Under low signal-to-noise-and-clutter ratio circumstances, non-contact multi-subjects vital sign assessment based on ultrawideband (UWB) radar is extremely difficult. It is challenging to obtain an accurate measurement since the respiratory frequency harmonics are mixed with noise during detection and are extremely near to the frequency of the heartbeat signal. We use an impulse radio ultrawideband (IR-UWB) radar and a stepped-frequency continuous-wave ultrawideband (SFCW-UWB) radar in measurement to get reliable vital signs from several participants. For adaptively extracting respiratory and heartbeat patterns, it is proposed to use the learning refined integral null space pursuit algorithm (LR-INSP), which is based on the learning refined integral null space pursuit method and optimum route learning. The refined-INSP technique aims to provide fine-grained multi-layer and multi-branch harmonic components. Then, to obtain a reliable and stable vital sign path from the multi-branch decomposition, an optimum path learning technique is presented. According to the experimental findings, the average signal accuracy assessment (SAA) of the heartbeat and respiratory frequencies for IR-UWB radar is 95.41% and 89.97%, respectively. The average SAA of the heartbeat and respiratory frequencies for SFCW-UWB radar is 94.39% and 99.48%, respectively.

Respiration and Heartbeat Signal Acquisition Method Based on Fiber Optic Sensing

Respiration and Heartbeat Signal Acquisition Method Based on Fiber Optic Sensing

An Intelligent Signal Processing Method for Motional Vital Signs Detection System Based on Deep Learning

Detection of vital signs for motional human targets in complex environment has always been a major challenge in the field of remote detection, remote healthcare and emergency rescue, because polytropic and multimodal interferences make intelligent signal processing more difficult. In this paper, a systematic intelligent signal processing scheme which contains signal preprocessing, vital signs identification, motion trajectory estimation and respiratory signal and heartbeat signal extraction is established. Based on CNN (Convolutional Neural Networks) model, accurate identification of motional vital signs getting rid of the interference of harmonics and distortion can be realized. Then, the misidentified outliers are eliminated with K-means clustering algorithm. Next, the motion trajectory of human targets can be estimated with Kalman filtering algorithm. Finally, the SVD-EEMD algorithm is proposed for respiratory signal and heartbeat signal extraction of dynamic human targets. The introduction of deep learning algorithms makes the proposed method have good performance of high accuracy, good robustness, strong adaptability and high efficiency, which can be observed in actual detection tasks contrast experiments.

Learning Temporal Similarity of Remote Photoplethysmography for Fast 3D Mask Face Presentation Attack Detection

To detect 3D mask face presentation attack, remote Photoplethysmography (rPPG), a biomedical technique that measures the heartbeat signal remotely with a normal RGB camera, is adopted as a robust liveness cue. Although existing rPPG-based solutions exhibit strong performance in experiments, the required observation time is too long (10-12 seconds) to be user-friendly in real applications such as E-payment and smartphone unlock. To shorten the observation time (within 1-second), we propose a fast rPPG-based 3D mask presentation attack detection (PAD) method by analyzing the similarity of rPPG signals in the time domain. In particular, based on facial and background local rPPG signals, we design a set of temporal similarity features to investigate the robust properties of rPPG shape and phase. Following the same direction, we refine the traditional rPPG extractor into a learnable network to cooperate with our TSrPPG feature for better robustness. An effective but lightweight spatiotemporal convolution network is constructed with a self-supervised learning strategy, aiming at enhancing the consistency of genuine facial rPPG signals and reducing the correlation of rPPG signals on masked faces. Extensive experiments are conducted on 3DMAD, HKBU-MARs V1+ and V2+, and CSMAD, which totally involve 18772 short-term video slots with a large number of real-world variations, in terms of mask type, mask transmittance, lighting condition, recording device, resolution of facial region, and compression configuration. Our proposed method persists the good performance of rPPG-based solution with only 1-second observation and outperforms the state-of-the-art competitors on discriminability and generalizability. Evaluations on prints attack, display attack, and disguise attacks with transparent masks, make-up and tattoo further exhibit its potential on handling a wider variety of attacks. To our best knowledge, this is the first work that addresses the length of observation time issue of rPPG-based 3D mask PAD.

ConvNet: 1D-Convolutional Neural Networks for Cardiac Arrhythmia Recognition Using ECG Signals

In healthcare, diagnostic tools of cardiac diseases are commonly known by the electrocardiogram (ECG) analysis. Atypical electrical activity can produce a cardiac arrhythmia. Various difficulties can be imposed to clinicians e.g., myocardial infarction arrhythmia via the non-stationarity and irregularity heart beat signals. Through the assistance of computer-aided diagnosis methods, timely specification of arrhythmia diseases reduces the mortality rate of affected patients. In this study, a 1 Lead QRS complex -layer deep convolutional neural network is proposed for the recognition of arrhythmia datasets. By the use of this CNN model, we planned a complete structure of the classification architecture after a pre-processing stage counting the denoising and QRS complex signals detection procedure. The chief benefit of the new proposed methodology is that the automatically training the QRS complexes without requiring all original extracted ECG signals. The proposed model was trained on the increased ECG database and separated into five classes. Experimental results display that the established CNN method has improved performance when compared to the state-of-the-art studies.