Showing papers on "Heartbeat published in 2018"

Automated ECG Classification Using Dual Heartbeat Coupling Based on Convolutional Neural Network

Abstract: A high performance electrocardiogram (ECG)-based arrhythmic beats classification system is presented in this paper. The classifier was designed based on convolutional neural network (CNN). Single channel ECG signal was segmented into heartbeats in accordance with the changing heartbeat rate. The beats were transformed into dual beat coupling matrix as 2-D inputs to the CNN classifier, which captured both beat morphology and beat-to-beat correlation in ECG. A systematic training beat selection procedure was also proposed which automatically include the most representative beats into the training set to improve classification performance. The classification system was evaluated for the detection of supraventricular ectopic beats (SVEB or S beats) and VEB using the MIT-BIH arrhythmia database. Our proposed method has demonstrated superior performance than several state-of-the-art detectors. In particular, our proposed CNN system has improved sensitivity and positive predictive rate for S beats by more than 12.2% and 11.9%, respectively, over these top performing algorithms. Our proposed CNN classifier with an automatic training beats selection process has shown to outperform the previous methods. The classifier is also a personalized one by combining training set from a common pool and a subject-specific set of ECG data. Our proposed system provides a reliable and fully automatic tool for detection of arrhythmia heartbeat without the need for manual feature extraction or expert assistant. It can potentially be implemented on portable device for the long-term monitoring of cardiac arrhythmia.

Heartbeat counting is unrelated to heartbeat detection: A comparison of methods to quantify interoception

Abstract: Recent research has identified individual differences in interoceptive sensitivity as a key source of variation in action, cognition, and emotion. This research has relied heavily on a single method for assessing interoceptive sensitivity: the accuracy of counting heartbeats while at rest. The validity of this method was assessed here by comparing the heartbeat counting (HBC) performance of 48 individuals with their heartbeat detection (HBD) performance. The HBC task required participants to report the numbers of heartbeats counted during brief signaled periods and indexed cardioceptive accuracy by the difference between the numbers of reported and actual heartbeats. In the HBD task, participants indicated the temporal location of heartbeat sensations relative to the onset of ventricular contraction. On each trial, they judged whether heartbeat sensations were or were not simultaneous with brief tones presented at one of six fixed delays following R waves of the ECG. In this method, cardioceptive accuracy or precision was indexed by variability in the temporal locations, relative to the R wave, of tones judged to be simultaneous with heartbeat sensations. Although intratask correlations indicated that each method yielded reliable scores, intertask correlations showed that HBC scores were unrelated to HBD scores. These results, which indicate that heartbeat detection and heartbeat counting are distinct processes, raise important questions about the assessment of interoceptive sensitivity and the involvement of this attribute in the psychological processes that have been associated with it on the basis of their correlations with HBC performance.

Phonocardiographic Sensing Using Deep Learning for Abnormal Heartbeat Detection

Abstract: Deep learning-based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliable and highly accurate systems, which are robust to the background noise in the heartbeat sound. In this paper, we propose a Recurrent Neural Networks (RNNs)-based automated cardiac auscultation solution. Our choice of RNNs is motivated by their great success of modeling sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models significantly outperform the best reported results in the literature. We also present the run-time complexity of various RNNs, which provides insight about their complexity versus performance trade-offs.

Noncontact Heartbeat and Respiration Monitoring Based on a Hollow Microstructured Self-Powered Pressure Sensor.

Abstract: Advances in mobile networks and low-power electronics have driven smart mobile medical devices at a tremendous pace, evoking increased interest in household healthcare, especially for those with cardiovascular or respiratory disease. Thus, flexible battery-free pressure sensors, with great potential for monitoring respiration and heartbeat in a smart way, are urgently demanded. However, traditional flexible battery-free pressure sensors for subtle physiological signal detecting are mostly tightly adhered onto the skin instead of working under the pressure of body weight in a noncontact mode, as the low sensitivity in the high-pressure region can hardly meet the demands. Moreover, a hollow microstructure (HM) with higher deformation than solid microstructures and great potential for improving the pressure sensitivity of self-powered sensors has never been investigated. Here, for the first time, we demonstrated a noncontact heartbeat and respiration monitoring system based on a flexible HM-enhanced self-pow...

Radar-Based Heart Sound Detection

Abstract: This paper introduces heart sound detection by radar systems, which enables touch-free and continuous monitoring of heart sounds. The proposed measurement principle entails two enhancements in modern vital sign monitoring. First, common touch-based auscultation with a phonocardiograph can be simplified by using biomedical radar systems. Second, detecting heart sounds offers a further feasibility in radar-based heartbeat monitoring. To analyse the performance of the proposed measurement principle, 9930 seconds of eleven persons-under-tests’ vital signs were acquired and stored in a database using multiple, synchronised sensors: a continuous wave radar system, a phonocardiograph (PCG), an electrocardiograph (ECG), and a temperature-based respiration sensor. A hidden semi-Markov model is utilised to detect the heart sounds in the phonocardiograph and radar data and additionally, an advanced template matching (ATM) algorithm is used for state-of-the-art radar-based heartbeat detection. The feasibility of the proposed measurement principle is shown by a morphology analysis between the data acquired by radar and PCG for the dominant heart sounds S1 and S2: The correlation is 82.97 ± 11.15% for 5274 used occurrences of S1 and 80.72 ± 12.16% for 5277 used occurrences of S2. The performance of the proposed detection method is evaluated by comparing the F-scores for radar and PCG-based heart sound detection with ECG as reference: Achieving an F1 value of 92.22 ± 2.07%, the radar system approximates the score of 94.15 ± 1.61% for the PCG. The accuracy regarding the detection timing of heartbeat occurrences is analysed by means of the root-mean-square error: In comparison to the ATM algorithm (144.9 ms) and the PCG-based variant (59.4 ms), the proposed method has the lowest error value (44.2 ms). Based on these results, utilising the detected heart sounds considerably improves radar-based heartbeat monitoring, while the achieved performance is also competitive to phonocardiography.

Heartbeat Classification Using Abstract Features From the Abductive Interpretation of the ECG

Abstract: Objective: This paper aims to prove that automatic beat classification on ECG signals can be effectively solved with a pure knowledge-based approach, using an appropriate set of abstract features obtained from the interpretation of the physiological processes underlying the signal. Methods: A set of qualitative morphological and rhythm features are obtained for each heartbeat as a result of the abductive interpretation of the ECG. Then, a QRS clustering algorithm is applied in order to reduce the effect of possible errors in the interpretation. Finally, a rule-based classifier assigns a tag to each cluster. Results: The method has been tested with the MIT-BIH Arrhythmia Database records, showing a significantly better performance than any other automatic approach in the state-of-the-art, and even improving most of the assisted approaches that require the intervention of an expert in the process. Conclusion: The most relevant issues in ECG classification, related to a large extent to the variability of the signal patterns between different subjects and even in the same subject over time, will be overcome by changing the reasoning paradigm. Significance: This paper demonstrates the power of an abductive framework for time-series interpretation to make a qualitative leap in the significance of the information extracted from the ECG by automatic methods.

Wavelet-Transform-Based Data-Length-Variation Technique for Fast Heart Rate Detection Using 5.8-GHz CW Doppler Radar

Abstract: The fast detection of heart rate (HR) is challenging when using the noncontact continuous-wave (CW) Doppler radar. Applying the Fourier transform (FT) to the baseband signal analysis, the accuracy is degraded due to the insufficient frequency resolution if using less than 5-s time window to realize fast detection. Moreover, respiratory harmonic peak might be incorrectly picked as the heartbeat signal if its magnitude is larger than the heartbeat peak in frequency spectrum. In this paper, a wavelet-transform-based data-length-variation technique is proposed to realize the fast detection of HR. With this technique, HR can be extracted with 3–5-s data length, and the respiratory harmonics can be distinguished from heartbeat signals, because the frequency of wavelet harmonic is not as tolerant of the change of the data length as heartbeat in the wavelet frequency spectrum. The algorithm is verified by simulation using numerical computing tool and demonstrated by human tests utilizing a 5.8-GHz CW Doppler radar platform. Compared to the traditional frequency domain method using FT, the proposed technique reduces the average error of HR from 26.7% to 3.5% using 3–5-s length of data varied in the range of ±0.5 s.

Respiration and Heartbeat Rates Measurement Based on Autocorrelation Using IR-UWB Radar

Abstract: Respiration rate (RR) and heartbeat rate (HR) are important physiological parameters for a person. Impulse radio ultra-wideband (IR-UWB) is a promising technology for non-contact sensing and monitoring. This brief presents a new method based on autocorrelation to measure the RR and HR using IR-UWB radar. The correlation coefficient waveform contains the vital sign signals, overcoming the effect of noise and clutter. Applying fast Fourier transform, the respiration frequency can be acquired easily. A clever method also based on autocorrelation is proposed to locate the subject. The receive signal matrix is divided into a set of bins in the direction of fast time. By removing one block from the matrix each time and re-applying the autocorrelation, the removed block resulting the smallest correlations is corresponding to the location of a subject. Moreover, variational mode decomposition algorithm is adopted to successfully separate the respiration and heartbeat signals. Experiments are carried out using a PulsOn410 UWB radar. The results show that the proposed low-complexity algorithm has high accuracy.

Acousticcardiogram: Monitoring Heartbeats using Acoustic Signals on Smart Devices

Abstract: Vital signs such as heart rate and heartbeat interval are currently measured by electrocardiograms (ECG) or wearable physiological monitors. These techniques either require contact with the patient's skin or are usually uncomfortable to wear, rendering them too expensive and user-unfriendly for daily monitoring. In this paper, we propose a new noninvasive technology to generate an Acousticcardiogram (ACG) that precisely monitors heartbeats using inaudible acoustic signals. ACG uses only commodity microphones and speakers commonly equipped on ubiquitous off-the-shelf devices, such as smartphones and laptops. By transmitting an acoustic signal and analyzing its reflections off human body, ACG is capable of recognizing the heart rate as well as heartbeat rhythm. We employ frequency-modulated sound signals to separate reflection of heart from that of background motions and breath, and continuously track the phase changes of the acoustic data. To translate these acoustic data into heart and breath rates, we leverage the dual microphone design on COTS mobile devices to suppress direct echo from speaker to microphones, identify heart rate in frequency domain, and adopt an advanced algorithm to extract individual heartbeats. We implement ACG on commercial devices and validate its performance in real environments. Experimental results demonstrate ACG monitors user's heartbeat accurately, with median heart rate estimation error of 0.6 beat per minute (bpm), and median heartbeat interval estimation error of 19 ms.

Smart Textile Using Hetero-Core Optical Fiber for Heartbeat and Respiration Monitoring

Abstract: This paper aims to describe a novel smart textile that uses a single-mode hetero-core optical fiber sensor for monitoring heartbeat and respiration. The smart textile was designed by weaving hetero-core optical fibers together with the wool fabric. This novel textile that can detect variations in shapes can be incorporated into clothes. Such clothes can offer comfort to the wearer. To simultaneously monitor heartbeat and respiration, the proposed textile is sewn on to the clothes to be able to sense minute load changes produced by chest movements. The vital signs, which were calculated from the heartbeat and respiration frequency of a healthy adult, while sitting, were in agreement with those verified using commercial monitoring devices. In addition, to confirm the capability of monitoring vital signs, seven monitoring trials were performed in which the subjects were asked to wear and take off the smart cloth. The vital signs rates were successfully extracted within the four beats per minute error by the fiber optic sensor system. The proposed smart textile in clothes can monitor vital signs in daily life activities, such as sitting and standing.

Noncontact Sleep Stage Estimation Using a CW Doppler Radar

Abstract: Sleep stage estimation is crucial to the evaluation of sleep quality and is a proven biometric in diagnosing cardiovascular diseases. In this paper, we design a continuous wave (CW) Doppler radar to accurately measure sleep-related signals, including respiration, heartbeat, and body movement. Body movement index, respiration per minute (RPM), variance of RPM, amplitude difference accumulation (ADA) of respiration, rapid eye movement parameter, sample entropy, heartbeat per minute (HPM), variance of HPM, ADA of heartbeat, deep parameter, and time feature have been extracted and fed into different machine learning classifiers. A total of 11 all night polysomnography recordings from 13 healthy examinees were used to validate the proposed CW Doppler radar system and the ability to detect sleep stage information from it. Comparative studies and statistical results have shown that the subspace K-nearest neighbor algorithm outperforms the other classifiers with the highest accuracy of up to 86.6%. With the Relief F algorithm, features have been ranked, and the selected feature subsets have been preliminary tested to identify the optimal feature subset. Meanwhile, comparative analysis of our classification performance under different sleep stage patterns with prior works has been carried out to show the significant improvements over state-of-the-art solutions. These results suggest that the proposed scheme is suitable for long-term sleep monitoring.

A Direct Phase-Tracking Doppler Radar Using Wavelet Independent Component Analysis for Non-Contact Respiratory and Heart Rate Monitoring

Abstract: A continuous wave Doppler radar, operating as a phase-locked-loop in phase demodulator configuration, is proposed and in vivo demonstrated for noncontact vital signs monitoring. The radar architecture exhibits a unique precision in tracking the phase modulation caused by human cardiopulmonary activity from which heartbeat and respiration can simultaneously be extracted. The single mixer architecture is immune to the null point and does not require small-angle approximation conditions, which distinguishes it from pre-existing other approaches. This enables the proposed radar to behave highly linear, with very precise detection of phase modulations induced by any kind of movement, independently from amplitude and speed. After simulations and technical tests to validate functionality and safety of the proposed architecture, a practical setup was demonstrated on human volunteers. Wavelet independent component analysis was applied to successfully retrieve respiratory and heart rate information from the radar baseband signal.

Heartbeat-enhanced immersive virtual reality to treat complex regional pain syndrome

Abstract: Objectives To develop and test a new immersive digital technology for complex regional pain syndrome (CRPS) that combines principles from mirror therapy and immersive virtual reality and the latest research from multisensory body processing. Methods In this crossover double-blind study, 24 patients with CRPS and 24 age- and sex-matched healthy controls were immersed in a virtual environment and shown a virtual depiction of their affected limb that was flashing in synchrony (or in asynchrony in the control condition) with their own online detected heartbeat (heartbeat-enhanced virtual reality [HEVR]). The primary outcome measures for pain reduction were subjective pain ratings, force strength, and heart rate variability (HRV). Results HEVR reduced pain ratings, improved motor limb function, and modulated a physiologic pain marker (HRV). These significant improvements were reliable and highly selective, absent in control HEVR conditions, not observed in healthy controls, and obtained without the application of tactile stimulation (or movement) of the painful limb, using a readily available biological signal (the heartbeat) that is most often not consciously perceived (thus preventing placebo effects). Conclusions Next to these specific and well-controlled analgesic effects, immersive HEVR allows the application of prolonged and repeated doses of digital therapy, enables the automatized integration with existing pain treatments, and avoids application of painful bodily cues while minimizing the active involvement of the patient and therapist. Classification of evidence This study provides Class III evidence that HEVR reduces pain and increases force strength in patients with CRPS.

Measures of sympathetic and parasympathetic autonomic outflow from heartbeat dynamics

Abstract: While it is possible to obtain reliable estimates of parasympathetic activity from the ECG, a satisfying method to disentangle the sympathetic component from HRV has not been proposed yet. To overc...

Very deep feature extraction and fusion for arrhythmias detection

Abstract: The electrocardiogram (ECG) is a picture of heart electrical conduction, which is widely used to diagnose many types of diseases such as abnormal heartbeat rhythm (arrhythmia). However, it is very difficult to detect the abnormal ECG characteristics because of the nonlinearity and the complexity of ECG signals from one side, and the noise effect of these signals from the other side, which make it very difficult to perform direct information extraction. Therefore, in this study we propose a very deep convolutional neural network (VDCNN) by using small filters throughout the whole net to reduce the noise affect and improve the performance. Our approach introduces multi-canonical correlation analysis (MCCA), a method to learn selective adaptive layer’s features such that the resulting representations are highly linearly correlated and speed up the training task. Moreover, the Q-Gaussian multi-class support vector machine (QG-MSVM) is introduced for classification, an algorithm which has a better learning performance and generalization ability on ECG signals processing. As a result, we come up with expressively more accurate architecture which is able to differentiate between the normal (NSR) heartbeats and three common types of arrhythmia atrial fibrillation (A-Fib), atrial flutter (AFL), and paroxysmal supraventricular tachycardia (PSVT) without performing any noise filtering or pre-processing techniques. Experimental results show that the proposed algorithm outperforms the state-of-the-art methods.

RF-ECG: Heart Rate Variability Assessment Based on COTS RFID Tag Array

Abstract: As an important indicator of autonomic regulation for circulatory function, Heart Rate Variability (HRV) is widely used for general health evaluation. Apart from using dedicated devices (e.g, ECG) in a wired manner, current methods search for a ubiquitous manner by either using wearable devices, which suffer from low accuracy and limited battery life, or applying wireless techniques (e.g., FMCW), which usually utilize dedicated devices (e.g., USRP) for the measurement. To address these issues, we present RF-ECG based on Commercial-Off-The-Shelf (COTS) RFID, a wireless approach to sense the human heartbeat through an RFID tag array attached on the chest area in the clothes. In particular, as the RFID reader continuously interrogates the tag array, two main effects are captured by the tag array: the reflection effect representing the RF-signal reflected from the heart movement due to heartbeat; the moving effect representing the tag movement caused by chest movement due to respiration. To extract the reflection signal from the noisy RF-signals, we develop a mechanism to capture the RF-signal variation of the tag array caused by the moving effect, aiming to eliminate the signals related to respiration. To estimate the HRV from the reflection signal, we propose a signal reflection model to depict the relationship between the RF-signal variation from the tag array and the reflection effect associated with the heartbeat. A fusing technique is developed to combine multiple reflection signals from the tag array for accurate estimation of HRV. Experiments with 15 volunteers show that RF-ECG can achieve a median error of 3% of Inter-Beat Interval (IBI), which is comparable to existing wired techniques.

Multichannel Investigation of Interoception: Sensitivity Is Not a Generalizable Feature.

Abstract: Objective: The term interoception refers to the perception of bodily cues. In empirical studies, it is assessed using heartbeat detection or tracking tasks, often with the implicit assumption that cardioception reflects general interoceptive ability. Studies that applied a multichannel approach measured only a limited number of modalities. In the current study, six modalities were assessed to gain a deeper understanding of the relationship between the different sensory channels of interoception. Methods: For 118 university students (53% male) gastric perception (water load test), heartbeat perception (Schandry task), proprioception (elbow joint), ischemic pain (tourniquet technique), balancing ability (one leg stand), and perception of bitter taste were measured. Pair-wise correlation analysis and exploratory factor analyses (principal component analysis (PCA) and maximum likelihood (ML) extraction with oblimin rotation) were then carried out with a three-factor solution to investigate the underlying associations. Results: Correlation analysis only revealed significant associations between variables belonging to the same sensory modality (gastric perception, pain, bitter taste). Similarly, the three factors that consistently emerged in the factor analyses represented the three aforementioned modalities. Discussion: Interoceptive sensitivity assessed by using one channel only cannot be generalized. Interoceptive modalities carrying crucial information for survival are not integrated with other channels.

Power-Accuracy Trade-Offs for Heartbeat Classification on Neural Networks Hardware

Abstract: Heartbeat classification using electrocardiogram (ECG) data is an essential feature of modern day wearable devices. State-of-the-art machine learning-based heartbeat classifiers are designed using convolutional neural networks (CNN). Despite their high classification accuracy, CNNs require significant computational resources and power. This makes the mapping of CNNs on resourceand power-constrained wearable devices challenging. In this paper, we propose heartbeat classification using spiking neural networks (SNN), an alternative approach based on a biologically inspired, event-driven neural networks. SNNs compute and transfer information using discrete spikes that require fewer operations and less complex hardware resources, making them energy-efficient compared to CNNs. However, due to complex error-backpropagation involving spikes, supervised learning of deep SNNs remains challenging. We propose an alternative approach to SNN-based heartbeat classification. We start with an optimized CNN implementation of the heartbeat classification task and then convert the CNN operations, such as multiply-accumulate, pooling and softmax, into spiking equivalent with a minimal loss of accuracy. We evaluate the SNN-based heartbeat classification using publicly available ECG database of the Massachusetts Institute of Technology and Beth Israel Hospital (MIT/BIH), and demonstrate a minimal loss in accuracy when compared to 85.92% accuracy of a CNN-based hearbeat classification. We demonstrate that, for every operation, the activation of SNN neurons in each layer is sparse when compared to CNN neurons, in the same layer. We also show that this sparsity increases with an increase in the number of layers of the neural network. In addition, we detail the power-accuracy trade-off of the SNN and show a 87.76% and 96.82% reduction in SNN neuron and synapse activity,respectively, for accuracy loss ranging between 0.6% and 1.00%, when compared to a CNN-only implementation. Keywords– Heartbeat classification, spiking neural network(SNN), convolution neural network(CNN)

Ultra-Wideband Impulse Radar Through-Wall Detection of Vital Signs.

Abstract: This paper presents a new system for the detection of human respiration behind obstacles using impulse ultra-wideband (UWB) radar. In complex environments, low signal-to-noise ratios (SNRs) as they can result in significant errors in the respiration, heartbeat frequency, and range estimates. To improve the performance, the complex signal demodulation (CSD) technique is extended by employing the signal logarithm and derivative. A frequency accumulation (FA) method is proposed to suppress mixed products of the heartbeat and respiration signals and spurious respiration signal harmonics. The respiration frequency is estimated using the phase variations in the received signal, and a discrete short-time Fourier transform (DSFT) is used to estimate the range. The performance of the proposed system is evaluated along with that of several well-known techniques in the literature.

Unlock with Your Heart: Heartbeat-based Authentication on Commercial Mobile Phones

Abstract: In this paper, we propose to use the vibration of the chest in response to the heartbeat as a biometric feature to authenticate the user on mobile devices We use the built-in accelerometer to capture the heartbeat signals on commercial mobile phones The user only needs to press the phone on his/her chest, and the system can identify the user within a few heartbeats To reliably extract heartbeat features, we design a two-step alignment scheme that can handle the natural variability in human heart rates We further use an adaptive template selection scheme to authenticate the user under different body postures and body states Based on heartbeat signals collected on twenty users, the experimental results show that our method can achieve an authentication accuracy of 9649% and the heartbeat features are stable over a period of three months

Heartbeat classification in wearables using multi-layer perceptron and time-frequency joint distribution of ECG

Abstract: Heartbeat classification using electrocardiogram (ECG) data is a vital assistive technology for wearable health solutions. We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint distribution of ECG. Fundamental to this is a multi-layer perceptron, which incorporates these signatures to detect cardiac arrhythmia. This approach is validated with ECG data from MIT-BIH arrhythmia database. Results show that our approach has an average 95.7% accuracy, an improvement of 22% over state-of-the-art approaches. Additionally, ECG sparse distributed representations generates only 3.7% false negatives, reduction of 89% with respect to existing ECG signal classification techniques.

Inter-Patient ECG Classification Using Deep Convolutional Neural Networks

Abstract: In this paper we present fully automatic interpatient electrocardiogram (ECG) signal classification method using deep convolutional neural networks (CNN). ECG is simple and non-invasive way to measure the electric activity of the heart and it provides valuable information about the condition of the heart. It is widely utilized for detecting different abnormalities in heartbeat. Identifying and classification abnormalities is timeconsuming, because it often requires analyzing each heartbeat of the ECG recording. Therefore, automatic classification of the arrhythmias using machine-learning technologies can bring various benefits. In this paper, we are focusing on inter-patient arrhythmia classification, where separate patient data is used in training and test phase. This method is more realistic in clinical environment, where trained model needs to operate with patients, whose ECG data was not available during the training phase. Our proposed method gives 92% sensitivity, 97% positive predictivity and 23% false positive rate for normal heartbeats. For supraventricular ectopic beat, our approach gives 62% sensitivity, 56% positive predictivity and 2% false positive rate. For ventricular ectopic beat, our method gives 89% sensitivity, 51% positive predictivity and 6% false positive rate. These results from our fully automatic feature learning approach are on par with solutions that require manual feature engineering.

Improving Twin Support Vector Machine Based on Hybrid Swarm Optimizer for Heartbeat Classification

Abstract: The computer-aided diagnosis system is used to reduce the high mortality rate among heart patients through detecting cardiac diseases at an early stage. Since the process of detecting the cardiac heartbeat is a hard task because of the human eye cannot be distinguished between the variations in electrocardiogram (ECG) signals due to they are very small. There are several machine learning approaches are applied to improve the performance of detecting the heartbeats, however, these methods suffer from some limitations such as high time computational and slow convergence. To avoid these limitations, this paper proposed an ECG heartbeat classification approach, called Swarm-TWSVM, that combined twin support vector machines (TWSVMs) with the hybrid between the particle swarm optimization with gravitational search algorithm (PSOGSA). Also, the empirical mode decomposition (EMD) has been applied for the ECG noise removing, and feature extraction, then PSOGSA was used to find the optimal parameters of TWSVM to improve the classification process. The experiments were performed using the MIT-BIH arrhythmia database and results show that the Swarm- TWSVM gives better accuracy than TWSVM 99.44 and 85.87%, respectively.

CRH: A Contactless Respiration and Heartbeat Monitoring System with COTS RFID Tags

Abstract: Monitoring respiration and heartbeat contributes to disease prediction, sub-health diagnosis, exercise and sleep quality analysis, fatigue warning, and even emotion estimation. There is a compelling need for contactless, easy-to-deploy and long-term respiration and heartbeat monitoring. In this paper, we present CRH, an RFID-based contactless respiration and heartbeat monitoring system. The key insight is that the RFID signal fluctuation induced by the chest motion is synchronous with respiration and heartbeat. Therefore, CRH collects the temporal phase information from the tag array near or on body to extract respiration and heartbeat signals using a sequence of signal processing techniques. We propose a signal separation method based on multi-tag empirical mode decomposition (EMD) to obtain respiration rate and heart rate after preprocessing. Furthermore, CRH can also detect intense motions and abnormal respiration. We implement and evaluate CRH using Commercial Off-The-Shelf (COTS) RFID devices. Extensive experimental results in different scenarios show that CRH can achieve high accuracy for monitoring multi-user respiration and heart rates, validating its wide applicability and high reliability for contactless fine-grained respiration and heartbeat monitoring.

Design and implementation of monitoring system for breathing and heart rate pattern using WiFi signals

Abstract: Breathing pattern and heart rate can be major indicators of a person's physical condition, and an easy way to measure the vital signs can be useful in health monitoring. In this paper, we propose a new method for identifying the changes in breathing and heart rate pattern of a person using commercial WiFi devices. The amplitude of signal waves can represent the periodic up-and-down chest movements caused by breathing and heartbeat, and prominent changes of the signal pattern can be detected by using the Dynamic Time Warping algorithm. We verified the feasibility of the proposed method in real testbeds and evaluated the method through various experiments with 10 participants. The proposed method achieves 94% accuracy in identifying a subjects physical status. This low-cost method will be useful for monitoring our health in everyday life.