Motion Artifact Suppression in Impedance Pneumography Signal for Portable Monitoring of Respiration: An Adaptive Approach

Impedance-Based Ventilation Detection and Signal Quality Control During Out-of-Hospital Cardiopulmonary Resuscitation

Feedback on ventilation could help improve cardiopulmonary resuscitation quality and survival from out-of-hospital cardiac arrest (OHCA). However, current technology that monitors ventilation during OHCA is very limited. Thoracic impedance (TI) is sensitive to air volume changes in the lungs, allowing ventilations to be identified, but is affected by artifacts due to chest compressions and electrode motion. This study introduces a novel algorithm to identify ventilations in TI during continuous chest compressions in OHCA. Data from 367 OHCA patients were included, and 2551 one-minute TI segments were extracted. Concurrent capnography data were used to annotate 20724 ground truth ventilations for training and evaluation. A three-step procedure was applied to each TI segment: First, bidirectional static and adaptive filters were applied to remove compression artifacts. Then, fluctuations potentially due to ventilations were located and characterized. Finally, a recurrent neural network was used to discriminate ventilations from other spurious fluctuations. A quality control stage was also developed to anticipate segments where ventilation detection could be compromised. The algorithm was trained and tested using 5-fold cross-validation, and outperformed previous solutions in the literature on the study dataset. The median (interquartile range, IQR) per-segment and per-patient F 1-scores were 89.1 (70.8-99.6) and 84.1 (69.0-93.9), respectively. The quality control stage identified most low performance segments. For the 50% of segments with highest quality scores, the median per-segment and per-patient F 1-scores were 100.0 (90.9-100.0) and 94.3 (86.5-97.8). The proposed algorithm could allow reliable, quality-conditioned feedback on ventilation in the challenging scenario of continuous manual CPR in OHCA.

Wearable cardiorespiratory monitoring with stretchable elastomer optical fiber

This work presents a stretchable elastomer optical fiber sensor incorporated into a belt for respiratory rate (RR) and heart rate (HR) monitoring. Different materials and shapes of prototypes designed were tested in terms of performance and the best choice was identified. The optimal sensor was tested by 10 volunteers to evaluate the performance. The proposed elastomer optical fiber sensor can achieve simultaneous measurement of RR and HR in different body positions, and also ballistocardiography (BCG) signal measurement in the lying position. The sensor has good accuracy and stability, with maximum errors of 1 bpm and 3 bpm for RR and HR, respectively, and average weighted mean absolute percentage error (MAPE) of 5.25% and root mean square error (RMSE) of 1.28 bpm. Moreover, the results of the Bland-Altman method showed good agreement of the sensor with manual counting of RR and with electrocardiogram (ECG) measurements of HR.

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones

In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.

Towards Estimation of Tidal Volume and Respiratory Timings via Wearable-Patch-Based Impedance Pneumography in Ambulatory Settings

OBJECTIVE

Evaluating convenient, wearable multi-frequency impedance pneumography (IP)-based respiratory monitoring in ambulatory persons with novel electrode positioning.

METHODS

A wearable multi-frequency IP system was utilized to estimate tidal volume (TV) and respiratory timings in 14 healthy subjects. A 5.1 cm × 5.1 cm tetrapolar electrode array, affixed to the sternum, and a conventional thoracic electrode configuration were employed to measure the respective IP signals, patch and thoracic IP. Data collected during static postures-sitting and supine-and activities-walking and stair-stepping-were evaluated against a simultaneously-obtained spirometer (SP) volume signal.

RESULTS

Across all measurements, estimated TV obtained from the patch and thoracic IP maintained a Pearson correlation coefficient (r) of 0.93 ± 0.05 and 0.95 ± 0.05 to the ground truth TV, respectively, with an associated root-mean-square error (RMSE) of 0.177 L and 0.129 L, respectively. Average respiration rates (RRs) were extracted from 30-second segments with mean-absolute-percentage errors (MAPEs) of 0.93% and 0.74% for patch and thoracic IP, respectively. Likewise, average inspiratory and expiratory timings were identified with MAPEs less than 6% and 4.5% for patch and thoracic IP, respectively.

CONCLUSION

We demonstrated that patch IP performs comparably to traditional, cumbersome IP configurations. We also present for the first time, to the best of our knowledge, that IP can robustly estimate breath-by-breath TV and respiratory timings during ambulation.

SIGNIFICANCE

This work represents a notable step towards pervasive wearable ambulatory respiratory monitoring via the fusion of a compact chest-worn form factor and multi-frequency IP that can be readily adapted for holistic cardiopulmonary monitoring.

Agreement between respiratory rate measurement using a combined electrocardiographic derived method versus impedance from pneumography

BACKGROUND

Impedance pneumography (IP) is the current device-driven method used to measure respiratory rate (RR) in hospitalized patients. However, RR alarms are common and contribute to alarm fatigue. While RR derived from electrocardiographic (ECG) waveforms hold promise, they have not been compared to the IP method.

PURPOSE

Study examined the agreement between the IP and combined-ECG derived (EDR) for normal RR (≥12 or ≤20 breaths/minute [bpm]); low RR (≤5 bpm); and high RR (≥30 bpm).

METHODOLOGY

One-hundred intensive care unit patients were included by RR group: (1) normal RR (n = 50; 25 low RR and 25 high RR); (2) low RR (n = 50); and (3) high RR (n = 50). Bland-Altman analysis was used to evaluate agreement.

RESULTS

For normal RR, a significant bias difference of -1.00 + 2.11 (95% CI -1.60 to -0.40) and 95% limit of agreement (LOA) of -5.13 to 3.13 was found. For low RR, a significant bias difference of -16.54 + 6.02 (95% CI: -18.25 to -14.83) and a 95% LOA of -28.33 to - 4.75 was found. For high RR, a significant bias difference of 17.94 + 12.01 (95% CI: 14.53 to 21.35) and 95% LOA of -5.60 to 41.48 was found.

CONCLUSION

Combined-EDR method had good agreement with the IP method for normal RR. However, for the low RR, combined-EDR was consistently higher than the IP method and almost always lower for the high RR, which could reduce the number of RR alarms. However, replication in a larger sample including confirmation with visual assessment is warranted.

A review of the literature on the accuracy, strengths, and limitations of visual, thoracic impedance, and electrocardiographic methods used to measure respiratory rate in hospitalized patients

Background

Respiratory rate (RR) is one of the most important indicators of a patient's health. In critically ill patients, unrecognized changes in RR are associated with poorer outcomes. Visual assessment (VA), impedance pneumography (IP), and electrocardiographic‐derived respiration (EDR) are the three most commonly used methods to assess RR. While VA and IP are widely used in hospitals, the EDR method has not been validated for use in hospitalized patients. Additionally, little is known about their accuracy compared with one another. The purpose of this systematic review was to compare the accuracy, strengths, and limitations of VA of RR to two methods that use physiologic data, namely IP and EDR.

Methods

A systematic review of the literature was undertaken using prespecified inclusion and exclusion criteria. Each of the studies was evaluated using standardized criteria.

Results

Full manuscripts for 23 studies were reviewed, and four studies were included in this review. Three studies compared VA to IP and one study compared VA to EDR. In terms of accuracy, when Bland–Altman analyses were performed, the upper and lower levels of agreement were extremely poor for both the VA and IP and VA and EDR comparisons.

Conclusion

Given the paucity of research and the fact that no studies have compared all three methods, no definitive conclusions can be drawn about the accuracy of these three methods. The clinical importance of accurate assessment of RR warrants new research with rigorous designs to determine the accuracy, and clinically meaningful levels of agreement of these methods.

Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions

Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled.

An impedance pneumography signal quality index: Design, assessment and application to respiratory rate monitoring

Impedance pneumography (ImP) is widely used for respiratory rate (RR) monitoring. However, ImP-derived RRs can be imprecise. The aim of this study was to develop a signal quality index (SQI) for the ImP signal, and couple it with a RR algorithm, to improve RR monitoring. An SQI was designed which identifies candidate breaths and assesses signal quality using: the variation in detected breath durations, how well peaks and troughs are defined, and the similarity of breath morphologies. The SQI categorises 32 s signal segments as either high or low quality. Its performance was evaluated using two critical care datasets. RRs were estimated from high-quality segments using a RR algorithm, and compared with reference RRs derived from manual annotations. The SQI had a sensitivity of 77.7 %, and specificity of 82.3 %. RRs estimated from segments classified as high quality were accurate and precise, with mean absolute errors of 0.21 and 0.40 breaths per minute (bpm) on the two datasets. Clinical monitor RRs were significantly less precise. The SQI classified 34.9 % of real-world data as high quality. In conclusion, the proposed SQI accurately identifies high-quality segments, and RRs estimated from those segments are precise enough for clinical decision making. This SQI may improve RR monitoring in critical care. Further work should assess it with wearable sensor data.

Use of wireless respiratory rate sensor monitoring during opioid patient-controlled analgesia after gynaecological surgery: A prospective cohort study

Background and Aims:

Respiratory depression is a rare but serious complication during opioid administration. Therefore, early detection of signs of deterioration is paramount. The current standard of care of using manual intermittent respiratory rate (RR) measurement is labour intensive and inefficient. We evaluated a wireless sensor monitor, Aingeal (Renew Health Ltd, Ireland), to continuously monitor RR, heart rate (HR) and temperature compared to standard clinical measurements.

Methods:

Patients who underwent major gynaecological operations and received postoperative opioid patient-controlled analgesia were recruited. Patients were connected to the sensor monitor via a central station software platform. The primary outcome was comparison of RR between sensor and nursing monitoring, with secondary outcomes being HR and temperature between two methods. Feedback from patients and healthcare providers was also collected. Bland-Altman analyses were used to compare the vital signs recorded in sensor against those in patient's electronic record.

Results:

A total of 1121 hours of vital signs data were analysed. Bias for RR was -0.90 (95% confidence interval (CI): -9.39, 7.60) breaths/min between nursing and averaged sensor readings. Bias for heart rate was -1.12 (95% CI: -26.27, 24.03) and bias for temperature was 1.45 (95% CI: -5.67, 2.76) between the two methods.

Conclusion:

There is satisfactory agreement of RR measurements, as well as HR and temperature measurements, by the wireless sensor monitor with standard clinical intermittent monitoring with overall good user experience.

On the potential of wearable bioimpedance for longitudinal fluid monitoring in end-stage kidney disease

Bioimpedance spectroscopy (BIS) has proven to be a promising non-invasive technique for fluid monitoring in HD patients. While current BIS-based monitoring of pre- and post-dialysis fluid status utilizes benchtop devices, designed for intramural use, advancements in micro-electronics have enabled the development of wearable bioimpedance systems. Wearable systems meanwhile can offer a similar frequency range for current injection as commercially available benchtop devices. This opens opportunities for unobtrusive longitudinal fluid status monitoring, including transcellular fluid shifts, with the ultimate goal of improving fluid management, thereby lowering mortality and improving quality of life for HD patients. Ultra-miniaturized wearable devices can also offer simultaneous acquisition of multiple other parameters, including hemodynamic parameters. Combination of wearable BIS and additional longitudinal multiparametric data may aid in the prevention of both hemodynamic instability as well as fluid overload. The opportunity to also acquire data during interdialytic periods using wearable devices likely will give novel pathophysiological insights and the development of smart (predicting) algorithms could contribute to personalizing dialysis schemes and ultimately to autonomous (nocturnal) home dialysis. This review provides an overview of current research regarding wearable bioimpedance, with special attention to applications in ESKD patients. Furthermore, we present an outlook on the future use of wearable bioimpedance within dialysis practice.

Wireless, Artefact Aware Impedance Sensor Node for Continuous Bio-Impedance Monitoring

Body bio-impedance is a unique parameter to monitor changes in body composition non-invasively. Continuous measurement of bio-impedance can track changes in body fluid content and cell mass and has widespread applications for physiological monitoring. State-of-the-art implementation of bio-impedance sensor devices is still limited for continuous use, in part, due to artefacts arising at the skin-electrode (SE) interface. Artefacts at the SE interface may arise due to various factors such as motion, applied pressure on the electrode surface, changes in ambient conditions or gradual drying of electrodes. This paper presents a novel bio-impedance sensor node that includes an artefact aware method for bio-impedance measurement. The sensor node enables autonomous and continuous measurement of bio-impedance and SE contact impedance at ten frequencies between 10 kHz to 100 kHz to detect artefacts at the SE interface. Experimental evaluation with SE contact impedance models using passive 2R1C electronic circuits and also with non-invasive in vivo measurements of SE contact impedance demonstrated high accuracy (with maximum error less than 1.5%) and precision of 0.6 Ω. The ability to detect artefacts caused by motion, vertically applied pressure and skin temperature changes was analysed in proof of concept experiments. Low power sensor node design achieved with 50mW in active mode and only 143 μW in sleep mode estimated a battery life of 90 days with a 250 mAh battery and duty-cycling impedance measurements every 60 seconds. Our method for artefact aware bio-impedance sensing is a step towards autonomous and unobtrusive continuous bio-impedance measurement for health monitoring at-home or in clinical environments.

Application of Modern Multi-Sensor Holter in Diagnosis and Treatment

Modern Holter devices are very trendy tools used in medicine, research, or sport. They monitor a variety of human physiological or pathophysiological signals. Nowadays, Holter devices have been developing very fast. New innovative products come to the market every day. They have become smaller, smarter, cheaper, have ultra-low power consumption, do not limit everyday life, and allow comfortable measurements of humans to be accomplished in a familiar and natural environment, without extreme fear from doctors. People can be informed about their health and 24/7 monitoring can sometimes easily detect specific diseases, which are normally passed during routine ambulance operation. However, there is a problem with the reliability, quality, and quantity of the collected data. In normal life, there may be a loss of signal recording, abnormal growth of artifacts, etc. At this point, there is a need for multiple sensors capturing single variables in parallel by different sensing methods to complement these methods and diminish the level of artifacts. We can also sense multiple different signals that are complementary and give us a coherent picture. In this article, we describe actual interesting multi-sensor principles on the grounds of our own long-year experiences and many experiments.

Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation

Feedback on chest compressions and ventilations during cardiopulmonary resuscitation (CPR) is important to improve survival from out-of-hospital cardiac arrest (OHCA). The thoracic impedance signal acquired by monitor-defibrillators during treatment can be used to provide feedback on ventilations, but chest compression components prevent accurate detection of ventilations. This study introduces the first method for accurate ventilation detection using the impedance while chest compressions are concurrently delivered by a mechanical CPR device. A total of 423 OHCA patients treated with mechanical CPR were included, 761 analysis intervals were selected which in total comprised 5 884 minutes and contained 34 864 ventilations. Ground truth ventilations were determined using the expired CO ₂ channel. The method uses adaptive signal processing to obtain the impedance ventilation waveform. Then, 14 features were calculated from the ventilation waveform and fed to a random forest (RF) classifier to discriminate false positive detections from actual ventilations. The RF feature importance was used to determine the best feature subset for the classifier. The method was trained and tested using stratified 10-fold cross validation (CV) partitions. The training/test process was repeated 20 times to statistically characterize the results. The best ventilation detector had a median (interdecile range, IDR) F ₁-score of 96.32 (96.26-96.37). When used to provide feedback in 1-min intervals, the median (IDR) error and relative error in ventilation rate were 0.002 (-0.334-0.572) min^-1 and 0.05 (-3.71-9.08)%, respectively. An accurate ventilation detector during mechanical CPR was demonstrated. The algorithm could be introduced in current equipment for feedback on ventilation rate and quality, and it could contribute to improve OHCA survival rates.

Chest Movement and Respiratory Volume both Contribute to Thoracic Bioimpedance during Loaded Breathing

Bioimpedance has been widely studied as alternative to respiratory monitoring methods because of its linear relationship with respiratory volume during normal breathing. However, other body tissues and fluids contribute to the bioimpedance measurement. The objective of this study is to investigate the relevance of chest movement in thoracic bioimpedance contributions to evaluate the applicability of bioimpedance for respiratory monitoring. We measured airflow, bioimpedance at four electrode configurations and thoracic accelerometer data in 10 healthy subjects during inspiratory loading. This protocol permitted us to study the contributions during different levels of inspiratory muscle activity. We used chest movement and volume signals to characterize the bioimpedance signal using linear mixed-effect models and neural networks for each subject and level of muscle activity. The performance was evaluated using the Mean Average Percentage Errors for each respiratory cycle. The lowest errors corresponded to the combination of chest movement and volume for both linear models and neural networks. Particularly, neural networks presented lower errors (median below 4.29%). At high levels of muscle activity, the differences in model performance indicated an increased contribution of chest movement to the bioimpedance signal. Accordingly, chest movement contributed substantially to bioimpedance measurement and more notably at high muscle activity levels.

Utility of a smartphone based system (cvrphone) to accurately determine apneic events from electrocardiographic signals

Background

Sleep disordered breathing manifested as sleep apnea (SA) is prevalent in the general population, and while it is associated with increased morbidity and mortality risk in some patient populations, it remains under-diagnosed. The objective of this study was to assess the accuracy of respiration-rate (RR) and tidal-volume (TV) estimation algorithms, from body-surface ECG signals, using a smartphone based ambulatory respiration monitoring system (cvrPhone).

Methods

Twelve lead ECG signals were collected using the cvrPhone from anesthetized and mechanically ventilated swine (n = 9). During ECG data acquisition, the mechanical ventilator tidal-volume (TV) was varied from 250 to 0 to 750 to 0 to 500 to 0 to 750 ml at respiratory rates (RR) of 6 and 14 breaths/min, respectively, and the RR and TV values were estimated from the ECG signals using custom algorithms.

Results

TV estimations from any two different TV settings showed statistically significant difference (p < 0.01) regardless of the RR. RRs were estimated to be 6.1±1.1 and 14.0±0.2 breaths/min at 6 and 14 breaths/min, respectively (when 250, 500 and 750 ml TV settings were combined). During apnea, the estimated TV and RR values were 11.7±54.9 ml and 0.0±3.5 breaths/min, which were significantly different (p<0.05) than TV and RR values during non-apnea breathing. In addition, the time delay from the apnea onset to the first apnea detection was 8.6±6.7 and 7.0±3.2 seconds for TV and RR respectively.

Conclusions

We have demonstrated that apnea can reliably be detected using ECG-derived RR and TV algorithms. These results support the concept that our algorithms can be utilized to detect SA in conjunction with ECG monitoring.