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In this short article, we present a new method to use a smartphone placed unattached on a subject’s chest in the supine position to obtain heartbeat and breathing signals and estimate heart and breathing rates, simultaneously. We collected 3-axis accelerometer, gyroscope, and magnetometer signals and performed sensor fusion to extract a user’s breathing signal and breathing rate. A hidden Markov model was used to segment the ballistocardiograph/seismocardiograph signals and extract the heart rate. The smartphone application was verified against breathing belt measurements and electrocardiogram measurements. We modified and proposed several suitable signal quality metrics for seismocardiograph signals. The overall results show that the application accurately estimated the breathing and heart rates, achieving a minimum mean percent error of 2.52% for breathing and 2.33% for heart rate. This work is a big step forward for vital sign estimation using inexpensive pervasive devices.

Heart rate variability (HRV) is a noninvasive marker for the cardiovascular system and the substantial influence of this system on the autonomous nervous system (ANS). Contact and remote photoplethysmography (PPG) proved to be a reliable means of pulse rate variability (PRV) measurement as in wearable devices or imaging PPG using video cameras with evolved technical specifications. However, PRV effectiveness remains unclear during controlled breathing rate. Specifically, respiratory rate, among other physiological factors, applies profound effects on ANS balance. In this study, we demonstrated that PRV can be a reliable marker, as HRV, during various controlled breathing rates. We evaluated the proposed PRV monitoring study against reference HRV obtained from an ECG measurement device. PRV was measured from video streams captured using a smartphone and a webcam for the upper abdominal section of healthy volunteers while in standing position and performing six different respiratory maneuvers. We evaluated frequency and nonlinear HRV indices. Agreement between all indices was tested for camera-based devices and ground truth for all performed tasks. The agreement presented a consistent mean close to 95% limit of agreement (LoA). Also, we employed spectral analysis and measured the spectral divergences, normalized root mean squared error (NRMSE), and correlation. Kullback-Leibler Divergence median and percentiles results indicate congruent spectrums, where the maximum median was 0.18. A significant spectral correlation with minimum value of 0.98, and relatively low spectrum NRMSE of 0.24 were observed. Moreover, the results led to HRV physiological related phenomena which agreed with physiologically interpretations found in the literature. [ABSTRACT FROM AUTHOR]

Thermal imaging-based techniques have recently emerged as a potential solution for the contactless measurement of breathing rate. However, these techniques were confined to detecting a single person’s breathing rate. In this paper, the simultaneous breathing monitoring of multiple persons is proposed by utilizing the cross-spectral visible-thermal image sequences. A cross-spectral multiple tracking-by-detection approach is used to detect multiple ROI in the visible spectrum and map the corresponding ROI in the thermal spectrum to extract the breathing signal. The linear coordinated mapping is performed by the use of a transformation matrix obtained from cross-spectral image registration. The results have been compared with the contact-based respiration belt for validation. The mean absolute error between estimated and belt-based measurements reached approximately 0.1 bpm (breaths per minute), 0.6 bpm, and 2.8 bpm for single, two, and three persons, respectively. The proposed method has a significant agreement and might be useful as a single measurement unit for contactless detection in the medical care centers, quarantine centers, and influenza screening at the airport. [ABSTRACT FROM AUTHOR]

Breathing rate (BR) is one of the vital signs used in physiological monitoring. Conventional BR monitoring requires attaching wired canula/thermistor on the buco-nasal area to measure air-flow, inducing discomfort to the subject. Abdominal/thoracic belts are also used to detect breathing movements whereas esophageal pressure is the gold standard to measure breathing effort. In this paper, we aim to validate the consistency of using only bed-sheet pressure sensors to monitor the BR in healthy adults. We propose a method and demonstrate that it could be used interchangeably with respiratory belts which were approved for medical use by the American Association of Sleep Medicine (AASM). We build a ten-sinusoidal model-based extended Kalman Filter to adaptively estimate the breathing movements’ signal from the body pressure distribution data. The model is posture-specific, I.e., parameters are optimized based on the detected posture. An artificial neural network (ANN) model was used to detect four bed postures to perform the kalman filter parameters’ optimization step. The BRs of 12 healthy adults are recorded using the pressure mattress and a reference respiratory belt. To validate the method as a surrogate measure, a Bland-Altman (BA) analysis was performed on both pressure and belt data, and the linear relationship is evaluated using Pearson Correlation Coefficient (PCC). Interestingly, a high inter-rater agreement, an average maximum difference of 1.93 Breaths Per Minute (BrPM), a confidence interval of 95%, along with a strong linear relationship of 95.8% on average between the two methods were interestingly obtained. The presented results show the suitability of the proposed solution in medical applications requiring respiration monitoring. [ABSTRACT FROM AUTHOR]

Remote sensing of life detection or a non-contact monitor of vital signals is an important application for Ultra-wideband (UWB) radar, such as health monitoring of a vehicle driver. Using the UWB radar to detect physiological signals of a dynamic human, three kind movement features (body motion, breathing, and heartbeat) must be considered generally to be extracted from echo pulses. Usually, moving body echo signal is much larger than other twos, which will cause signal interference and interaction problems. Meanwhile, since moving body causes breathing and heartbeat to act in dynamic spatial position, conventional fixed feature with maximum spectrum or local peak spectrum detection methods are not likely to find those movable physiological active features. Thus, how to reduce physiological feature bias from body motion, and efficiently obtain valuable physiological information are problems. To solve these problems, a novel multi-feature alignment (MFA) two-layer EEMD method is proposed. The proposed method simultaneously detects breathing and heartbeat information from a slightly swinging human (driver). The simulation and experiment results show the proposed method can effectively and reliably evaluate breathing and heart rates in static or/and dynamic body situation, both in laboratory and car. [ABSTRACT FROM AUTHOR]

Recently, noninvasive vital signs detection is widely used in through the wall (TTW) human detection and healthcare monitoring. In indoors and TTW environments, low signal to noise ratio make the detection of the weak vital signs difficult, and causes errors in the estimation of heartbeat, respiration rate. In this paper, different techniques are applied to the signal collected using a M-sequence ultra wideband sensor from the human subject, in order to suppress the clutter, detect the target range and estimate the heartbeat and respiration rate. First, moving average method is used to remove the stationary clutter. Next, Singular Value Decomposition (SVD) is applied to cancel the non-stationary clutter. Then, variance analysis is applied to estimate the range; when range has been detected, one dimensional Singular Spectrum Analysis (SSA) is applied to the range bin containing the subject data. It is observed that SSA completely succeeds to decompose the signal into heartbeat and respiration signal and a collection of unwanted signals as clutter, noise, and harmonics. Finally, Fast Fourier transform (FFT) is used for the estimation of the heartbeat and respiration rate. Comparison results show that the proposed algorithm outperforms a reference SVD based algorithms for heartbeat and respiration rate estimation. [ABSTRACT FROM AUTHOR]

Objective: The objective of this paper is to obtain accurate estimation of breathing rate (BR), using only the electrocardiogram (ECG) or the photoplethysmogram (PPG) signals, to avoid wearing cumbersome and uncomfortable sensors for direct measurements. Methods: Several respiration waveforms are derived from ECG or PPG signals based on amplitude, frequency, and baseline wander modulations. It is, however, difficult to determine their optimal combination for BR estimation due to the noise and patient specificity. We first propose to quantify the quality of modulation waveforms using respiratory quality indices (RQIs). We then present two methods: the first automatically selects the modulation signal with highest RQI for BR estimation, and the second tracks the respiration signal using the Kalman smoother to fuse modulation signals with highest RQI. Results: These two methods are evaluated on two independent datasets, one benchmark database (DB) with immobilized patients recordings and the second with those performing daily activities. Our results outperform existing methods in the literature in both the cases. Conclusion: Experimental results show that the RQIs coupled with a fusion algorithm increases the accuracy for BR estimations in dealing with derived modulation signals. Significance: This work describes a robust Kalman Smoother method applicable in multiple clinical contexts to improve breathing rate estimation from data fusion. [ABSTRACT FROM AUTHOR]

Breathing rate (BR) is a significant bio marker that provides both prognostic and diagnostic information for monitoring physiological condition. In addition to vital bio markers, such as blood oxygen saturation and pulse rate, BR can be extracted from non-invasive and wearable pulse oximeter based photoplethysmogram (PPG). Empirical mode decomposition (EMD) and its noise-assisted variants are widely used for decomposing non-linear and non-stationary signals. In this work, the effect of all variants of EMD in extracting BR from PPG has been investigated. We have used an EMD family PCA based hybrid model in extracting BR from PPG, which is a natural extension of our previously developed ensemble EMD (EEMD) PCA hybrid model. The performance of each model has been tested using two different datasets: MIMIC and Capnobase. Median absolute error varied from 0 to 5.03 and from 2.47 to 10.55 breaths/min for MIMIC and Capnobase dataset, respectively. Among all the EMD variants, EEMD-PCA and improved complete EEMD with adaptive noise (ICEEMDAN) PCA hybrid model present better performance for both datasets. This is the first study to compare EMD variants performance for decomposing real world signal and determine that from current methods, ICEEMDAN and EEMD are optimal for estimating BR from PPG. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) radar is an important remote sensing tool of life detection or a non-contact monitor of the vital signals. By processing the received UWB pulse echoes reflected from the body, different signals corresponding to heart activity and breathing, corrupted by body motion and the environment noise, are wanted to be separated clearly. However, the heartbeat signal is so tiny that it is covered by breathing harmonics and clutters. At the same time, since the frequencies of the vital signals are very close, usually around 1 Hz, it is difficult to apply an ordinary frequency filter to separate them apart. This problem induces that the vital signal detection method, usually, only detects the large breath signal, not the heartbeat signal. To solve this problem, a novel method is provided, in this paper, to extract the heartbeat and the breath information simultaneously. The method uses the feature time index with the first valley peak of the energy function of intrinsic mode functions (FVPIEF) calculated by pseudo bi-dimension ensemble empirical mode decomposition method and extracts the vital signals by the ensemble empirical mode decomposition (EEMD). Both simulation and experiment results evidently show that the proposed FVPIEF based two-layer EEMD method is effective for separating the small heartbeat signal from the large breath signal and significantly improves the evaluation of heart and breathing rates in both hold-breathing and breathing conditions. [ABSTRACT FROM AUTHOR]

Wireless body sensors are increasingly used by clinicians and researchers in a wide range of applications, such as sports, space engineering, and medicine. Monitoring vital signs in real time can dramatically increase diagnosis accuracy and enable automatic curing procedures, e.g., detect and stop epilepsy or narcolepsy seizures. Breathing parameters are critical in oxygen therapy, hospital, and ambulatory monitoring, while the assessment of cough severity is essential when dealing with several diseases, such as chronic obstructive pulmonary disease. In this paper, a low-power wireless respiratory monitoring system with cough detection is proposed to measure the breathing rate and the frequency of coughing. This system uses wearable wireless multimodal patch sensors, designed using off-the-shelf components. These wearable sensors use a low-power nine-axis inertial measurement unit to quantify the respiratory movement and a MEMs microphone to record audio signals. Data processing and fusion algorithms are used to calculate the respiratory frequency and the coughing events. The architecture of each wireless patch-sensor is presented. In fact, the results show that the small $26.67 \times 65.53$ mm2 patch-sensor consumes around 12–16.2 mA and can last at least 6 h with a miniature 100-mA lithium ion battery. The data processing algorithms, the acquisition, and wireless communication units are described. The proposed network performance is presented for experimental tests with a freely behaving user in parallel with the gold standard respiratory inductance plethysmography. [ABSTRACT FROM AUTHOR]

We develop and evaluate a respiratory rate (RR) estimation algorithm that utilizes data from the pressure-sensitive mat (PSM) technology for continuous patient monitoring in neonatal intensive care units. An analysis of the random effect of drift and systematic effect of creep in the PSM data is presented, showing that these are essentially dependent on the applied load and contact surface. Uncertainty measurements are pivotal when estimating physiologic parameters. The standard uncertainty in the PSM data is here represented by the percent drift. Next, we evaluate the applicability of the PSM technology to estimate RR in neonatal patient simulator trials under five mixed effects including internally and externally induced motion, mattress type, grunting, laying position, and different breathing rates. We analyze the limits of agreement on the mixed effects model to derive the uncertainty in the estimated RR obtained through two estimation techniques. In comparison with the gold standard RR values, we achieved a mean bias of 0.56 breaths per minute (bpm) with an error bounded by a 95% confidence interval of [−2.26, 3.37] bpm. These results meet the clinical accuracy requirements of RR within ±5 bpm. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnoea is one of the most common sleep disorders, which is characterized by the repetitive cessations of respiratory flow during sleep. This paper describes an alternative method to detect sleep parameters by measuring a variation in the earth’s magnetic field. A magnetometer sensor, laid onto the body, detects millimeter night-time breathing movements by measuring the change in the magnetic vectors. A soft, non-invasive wearable sensor is designed to be printed on a miniature printed circuit board, packaged in a minimal invasive board, which includes a wireless Bluetooth low-energy (BLE) module and a low-power microcontroller. A very low power consumption is achieved by applying a smart processing algorithm, which is also described. The algorithm delivers respiration rate, apnoea time, and time in movement. A comparison with a well-known air-flow sensor is also discussed. [ABSTRACT FROM PUBLISHER]

We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxygen saturation, by comparisons to standard methods for making such measurements (respiration belts, ECGs, and pulse-oximeters, respectively). Measurement of respiratory rate uses a previously reported algorithm developed for use with a pulse-oximeter, based on amplitude and frequency modulation sequences within the light signal. We note that this technology can also be used with recently developed algorithms for detection of atrial fibrillation or blood loss. [ABSTRACT FROM PUBLISHER]