Increased amplitude modulation of continuous respiration precedes sudden infant death syndrome -detection by spectral estimation of respirogram

Enhancing System Performance through Objective Feature Scoring of Multiple Persons' Breathing Using Non-Contact RF Approach

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system's performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system's performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject's body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

Non-Contact Monitoring of Breathing Pattern and Respiratory Rate via RGB Signal Measurement

Among all the vital signs, respiratory rate remains the least measured in several scenarios, mainly due to the intrusiveness of the sensors usually adopted. For this reason, all contactless monitoring systems are gaining increasing attention in this field. In this paper, we present a measuring system for contactless measurement of the respiratory pattern and the extraction of breath-by-breath respiratory rate. The system consists of a laptop’s built-in RGB camera and an algorithm for post-processing of acquired video data. From the recording of the chest movements of a subject, the analysis of the pixel intensity changes yields a waveform indicating respiratory pattern. The proposed system has been tested on 12 volunteers, both males and females seated in front of the webcam, wearing both slim-fit and loose-fit t-shirts. The pressure-drop signal recorded at the level of nostrils with a head-mounted wearable device was used as reference respiratory pattern. The two methods have been compared in terms of mean of absolute error, standard error, and percentage error. Additionally, a Bland–Altman plot was used to investigate the bias between methods. Results show the ability of the system to record accurate values of respiratory rate, with both slim-fit and loose-fit clothing. The measuring system shows better performance on females. Bland–Altman analysis showed a bias of −0.01 breaths·min−1, with respiratory rate values between 10 and 43 breaths·min−1. Promising performance has been found in the preliminary tests simulating tachypnea.

Contact-Based Methods for Measuring Respiratory Rate

There is an ever-growing demand for measuring respiratory variables during a variety of applications, including monitoring in clinical and occupational settings, and during sporting activities and exercise. Special attention is devoted to the monitoring of respiratory rate because it is a vital sign, which responds to a variety of stressors. There are different methods for measuring respiratory rate, which can be classed as contact-based or contactless. The present paper provides an overview of the currently available contact-based methods for measuring respiratory rate. For these methods, the sensing element (or part of the instrument containing it) is attached to the subject's body. Methods based upon the recording of respiratory airflow, sounds, air temperature, air humidity, air components, chest wall movements, and modulation of the cardiac activity are presented. Working principles, metrological characteristics, and applications in the respiratory monitoring field are presented to explore potential development and applicability for each method.

Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder

Background

Wearable and connected health devices along with the recent advances in mobile and cloud computing provide a continuous, convenient-to-patient, and scalable way to collect personal health data remotely. The Wavelet Health platform and the Wavelet wristband have been developed to capture multiple physiological signals and to derive biometrics from these signals, including resting heart rate (HR), heart rate variability (HRV), and respiration rate (RR).

Objective

This study aimed to evaluate the accuracy of the biometric estimates and signal quality of the wristband.

Methods

Measurements collected from 35 subjects using the Wavelet wristband were compared with simultaneously recorded electrocardiogram and spirometry measurements.

Results

The HR, HRV SD of normal-to-normal intervals, HRV root mean square of successive differences, and RR estimates matched within 0.7 beats per minute (SD 0.9), 7 milliseconds (SD 10), 11 milliseconds (SD 12), and 1 breaths per minute (SD 1) mean absolute deviation of the reference measurements, respectively. The quality of the raw plethysmography signal collected by the wristband, as determined by the harmonic-to-noise ratio, was comparable with that obtained from measurements from a finger-clip plethysmography device.

Conclusions

The accuracy of the biometric estimates and high signal quality indicate that the wristband photoplethysmography device is suitable for performing pulse wave analysis and measuring vital signs.

Serotonin gene variants are unlikely to play a significant role in the pathogenesis of the sudden infant death syndrome

Sudden infant death syndrome (SIDS) is defined as the sudden and unexpected death of an infant less than 12 months of age that is related to a sleep period and remains unexplained after a complete autopsy, death scene investigation, and review of the clinical history. The cause of SIDS is unknown, but a major subset of SIDS is proposed to result from abnormalities in serotonin (5-HT) and related neurotransmitters in regions of the lower brainstem that result in failure of protective homeostatic responses to life-threatening challenges during sleep. Multiple studies have implicated gene variants that affect different elements of 5-HT neurotransmission in the pathogenesis of these abnormalities in SIDS. In this review I discuss the data from these studies together with some new data correlating genotype with brainstem 5-HT neurochemistry in the same SIDS cases and conclude that these gene variants are unlikely to play a major role in the pathogenesis of the medullary 5-HT abnormalities observed in SIDS.

Possible predictors of cardiorespiratory events after immunization in preterm neonates

BACKGROUND

The influence of the first immunization on cardiorespiratory (CR) stability in very preterm infants is still a controversial subject.

OBJECTIVES

To describe the changes induced by immunization on heart and respiratory rate variability (HRV-RRV) and to test a potential association between preimmunization profiles and postimmunization CR events.

METHODS

Continuous 72-hour CR recordings and 2.5-hour polysomnographic recordings were performed on very preterm infants immunized after 7 weeks. The results are expressed as medians (interquartile ranges).

RESULTS

Immunization was performed on 31 very preterm infants [28 weeks' gestation (26.9-29), birth weight: 965 g (795-1,105)], and was associated with an increased incidence (p < 0.01) of events lasting more than 10 s: bradycardia <80 bpm [2.2 (1.1-7) vs. 1.8 (1-4)/12 h], desaturation [17.6 (9.4-36.4) vs. 13.9 (7.7-33.8)/12 h] and associated bradycardia-desaturation [IB+D, 4.1 (1.4-7.3) vs. 2.4 (1-4.6)/12 h], with mild changes in HRV and no change in RRV. The changes in IB+D frequency were correlated with preimmunization IB+D frequency (r = 0.44, p < 0.05), HRV spectral parameter low frequency/high frequency ratio (LF/HF, r = 0.55, p < 0.01) and approximate entropy of HRV (r = -0.39, p < 0.05).

CONCLUSION

The increase in CR events after the first immunization in very preterm infants was associated with: (1) sympathetic predominance in heart rate control (high LF/HF ratio), (2) abnormal oversimplification of HRV (low entropy) and (3) persistent respiratory rhythm control immaturity (high IB+D before vaccine).

Estimation of respiration rate from three-dimensional acceleration data based on body sensor network

Respiratory monitoring is widely used in clinical and healthcare practice to detect abnormal cardiopulmonary function during ordinary and routine activities. There are several approaches to estimate respiratory rate, including accelerometer(s) worn on the torso that are capable of sensing the inclination changes due to breathing. In this article, we present an adaptive band-pass filtering method combined with principal component analysis to derive the respiratory rate from three-dimensional acceleration data, using a body sensor network platform previously developed by us. In situ experiments with 12 subjects indicated that our method was capable of offering dynamic respiration rate estimation during various body activities such as sitting, walking, running, and sleeping. The experimental studies also suggested that our frequency spectrum-based method was more robust, resilient to motion artifact, and therefore outperformed those algorithms primarily based on spatial acceleration information.

Respiratory rate extraction from pulse oximeter and electrocardiographic recordings

We present an algorithm of respiratory rate extraction using particle filter (PF), which is applicable to both photoplethysmogram (PPG) and electrocardiogram (ECG) signals. For the respiratory rate estimation, 1 min data are analyzed with combination of a PF method and an autoregressive model where among the resultant coefficients, the corresponding pole angle with the highest magnitude is searched since this reflects the closest approximation of the true breathing rate. The PPG data were collected from 15 subjects with the metronome breathing rate ranging from 24 to 36 breaths per minute in the supine and upright positions. The ECG data were collected from 11 subjects with spontaneous breathing ranging from 36 to 60 breaths per minute during treadmill exercises. Our method was able to accurately extract respiratory rates for both metronome and spontaneous breathing even during strenuous exercises. More importantly, despite slow increases in breathing rates concomitant with greater exercise vigor with time, our method was able to accurately track these progressive increases in respiratory rates. We quantified the accuracy of our method by using the mean, standard deviation and interquartile range of the error rates which all reflected high accuracy in estimating the true breathing rates. We are not aware of any other algorithms that are able to provide accurate respiratory rates directly from either ECG signals or PPG signals with spontaneous breathing during strenuous exercises. Our method is near real-time realizable because the computational time on 1 min data segment takes only 10 ms on a 2.66 GHz Intel Core2 microprocessor; the data are subsequently shifted every 10 s to obtain near-continuous breathing rates. This is an attractive feature since most other techniques require offline data analyses to estimate breathing rates.

Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation

We present a new method that uses the pulse oximeter signal to estimate the respiratory rate. The method uses a recently developed time-frequency spectral estimation method, variable-frequency complex demodulation (VFCDM), to identify frequency modulation (FM) of the photoplethysmogram waveform. This FM has a measurable periodicity, which provides an estimate of the respiration period. We compared the performance of VFCDM to the continuous wavelet transform (CWT) and autoregressive (AR) model approaches. The CWT method also utilizes the respiratory sinus arrhythmia effect as represented by either FM or AM to estimate respiratory rates. Both CWT and AR model methods have been previously shown to provide reasonably good estimates of breathing rates that are in the normal range (12-26 breaths/min). However, to our knowledge, breathing rates higher than 26 breaths/min and the real-time performance of these algorithms are yet to be tested. Our analysis based on 15 healthy subjects reveals that the VFCDM method provides the best results in terms of accuracy (smaller median error), consistency (smaller interquartile range of the median value), and computational efficiency (less than 0.3 s on 1 min of data using a MATLAB implementation) to extract breathing rates that varied from 12-36 breaths/min.

Sudden infant death syndrome requires genetic predisposition, some form of stress and marginal malnutrition

Over the past 30 years or more, the problem of sudden, unexplained death in infants (SIDS) has made little headway. Many hypotheses have been offered but the basic cause remains elusive. The only successful prevention has been made by the supine sleeping posture. There is still, however, a hard core of unexplained incidents. There is evidence that certain stress factors are involved, and there is good evidence that the tragedy has a familial or genetic tendency. The third factor necessary for the event is inefficient oxidation in brain cells induced most commonly by marginal malnutrition in pregnancy or after birth. The absence of any one or more of these three factors decreases risk to the point of extinction. Anything that impedes healthy oxidation, or accelerates energy utilization through responding to stress, increases the risk greatly. Improving the biochemical mechanisms through appropriate nutrition is by far the best defense.