Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study

Chronic respiratory diseases affect millions and are leading causes of death in the US and worldwide. Pulmonary auscultation provides clinicians with critical respiratory health information through the study of Lung Sounds (LS) and the context of the breathing-phase and chest location in which they are measured. Existing auscultation technologies, however, do not enable the simultaneous measurement of this context, thereby potentially limiting computerized LS analysis. In this work, LS and Impedance Pneumography (IP) measurements were obtained from 10 healthy volunteers while performing normal and forced-expiratory (FE) breathing maneuvers using our wearable IP and respiratory sounds (WIRS) system. Simultaneous auscultation was performed with the Eko CORE stethoscope (EKO). The breathing-phase context was extracted from the IP signals and used to compute phase-by-phase (Inspiratory (I), expiratory (E), and their ratio (I:E)) and breath-by-breath acoustic features. Their individual and added value was then elucidated through machine learning analysis. We found that the phase-contextualized features effectively captured the underlying acoustic differences between deep and FE breaths, yielding a maximum F1 Score of 84.1 ±11.4% with the phase-by-phase features as the strongest contributors to this performance. Further, the individual phase-contextualized models outperformed the traditional breath-by-breath models in all cases. The validity of the results was demonstrated for the LS obtained with WIRS, EKO, and their combination. These results suggest that incorporating breathing-phase context may enhance computerized LS analysis. Hence, multimodal sensing systems that enable this, such as WIRS, have the potential to advance LS clinical utility beyond traditional manual auscultation and improve patient care.

Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance

BACKGROUND

Deep learning has been successfully applied to ECG data to aid in the accurate and more rapid diagnosis of acutely decompensated heart failure (ADHF). Previous applications focused primarily on classifying known ECG patterns in well-controlled clinical settings. However, this approach does not fully capitalize on the potential of deep learning, which directly learns important features without relying on a priori knowledge. In addition, deep learning applications to ECG data obtained from wearable devices have not been well studied, especially in the field of ADHF prediction.

METHODS

We used ECG and transthoracic bioimpedance data from the SENTINEL-HF study, which enrolled patients (≥21 years) who were hospitalized with a primary diagnosis of heart failure or with ADHF symptoms. To build an ECG-based prediction model of ADHF, we developed a deep cross-modal feature learning pipeline, termed ECGX-Net, that utilizes raw ECG time series and transthoracic bioimpedance data from wearable devices. To extract rich features from ECG time series data, we first adopted a transfer learning approach in which ECG time series were transformed into 2D images, followed by feature extraction using ImageNet-pretrained DenseNet121/VGG19 models. After data filtering, we applied cross-modal feature learning in which a regressor was trained with ECG and transthoracic bioimpedance. Then, we concatenated the DenseNet121/VGG19 features with the regression features and used them to train a support vector machine (SVM) without bioimpedance information.

RESULTS

The high-precision classifier using ECGX-Net predicted ADHF with a precision of 94 %, a recall of 79 %, and an F1-score of 0.85. The high-recall classifier with only DenseNet121 had a precision of 80 %, a recall of 98 %, and an F1-score of 0.88. We found that ECGX-Net was effective for high-precision classification, while DenseNet121 was effective for high-recall classification.

CONCLUSION

We show the potential for predicting ADHF from single-channel ECG recordings obtained from outpatients, enabling timely warning signs of heart failure. Our cross-modal feature learning pipeline is expected to improve ECG-based heart failure prediction by handling the unique requirements of medical scenarios and resource limitations.

Multi-frequency Electrical Impedance Pneumography System as Point-Of-Care Device

In this work we present the development of a multi-frequency electrical impedance pneumography (EIP) system based on a portable acquisition device and a mobile platform. This design is intended as an upgrade to our previous device for clinical use in the screening of patients with pulmonary diseases. The acquisition device uses the bioimpedance analog front end MAX30001, a mux/demux stage, Bluetooth 4.0 communication and an ESP32 microcontroller unit. It generates an excitation current of 8 μA_pp in a range of selectable frequencies from 1 kHz to 130 kHz. The mobile platform provides a real-time respiration signal at 64 S/s and allows the configuration of the device. Results show less error in higher frequencies, which are the most common in these applications, ensuring the feasibility of the system for use in humans. Some hardware constraints related to EIP integrated circuits are discussed, an their effect in the signal acquisition.

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.

A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers

Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, and kinematics to track changes in cardiopulmonary status. We first validated the system on healthy subjects (n = 10) and then conducted a feasibility study on patients (n = 14) with HF in clinical settings. Three measurements were taken throughout the course of hospitalization, and parameters relevant to lung fluid status—the ratio of the resistances at 5 kHz to those at 150 kHz (K)—and respiratory timings (e.g., respiratory rate) were extracted. We found a statistically significant increase in K (p < 0.05) from admission to discharge and observed respiratory timings in physiologically plausible ranges. The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne–Stokes) and inspiratory crackles from patient recordings, respectively. We demonstrated that the proposed system is suitable for detecting changes in pulmonary fluid status and capturing high-quality respiratory signals and lung sounds in a clinical setting.

Estimation of Tidal Volume Using Load Cells on a Hospital Bed

Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifi- able respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respi- ratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respira- tory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low- frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings.

Detection of Respiratory Phases to Estimate Breathing Pattern Parameters using Wearable Bioimpendace

Many studies have focused on novel noninvasive techniques to monitor respiratory rate such as bioimpedance. We propose an algorithm to detect respiratory phases using wearable bioimpedance to compute time parameters like respiratory rate, inspiratory and expiratory times, and duty cycle. The proposed algorithm was compared with two other algorithms from literature designed to estimate the respiratory rate using physiological signals like bioimpedance. We acquired bioimpedance and airflow from 50 chronic obstructive pulmonary disease (COPD) patients during an inspiratory loading protocol. We compared performance of the algorithms by computing accuracy and mean average percentage error (MAPE) between the bioimpedance parameters and the reference parameters from airflow. We found similar performance for the three algorithms in terms of accuracy (>0.96) and respiratory time and rate errors (<3.42 %). However, the proposed algorithm showed lower MAPE in duty cycle (10.18 %), inspiratory time (10.65 %) and expiratory time (8.61 %). Furthermore, only the proposed algorithm kept the statistical differences in duty cycle between COPD severity levels that were observed using airflow. Accordingly, we suggest bioimpedance to monitor breathing pattern parameters in home situations.Clinical relevance- This study exhibits the suitability of wearable thoracic bioimpedance to detect respiratory phases and to compute accurate breathing pattern parameters.

A wearable eddy current based pulmonary function sensor for continuous non-contact point-of-care monitoring during the COVID-19 pandemic

Pulmonary function testing (PFT) allows for quantitative analysis of lung function. However, as a result of the coronavirus disease 2019 (COVID-19) pandemic, a majority of international medical societies have postponed PFTs in an effort to mitigate disease transmission, complicating the continuity of care in high-risk patients diagnosed with COVID-19 or preexisting lung pathologies. Here, we describe the development of a non-contact wearable pulmonary sensor for pulmonary waveform analysis, pulmonary volume quantification, and crude thoracic imaging using the eddy current (EC) phenomenon. Statistical regression analysis is performed to confirm the predictive validity of the sensor, and all data are continuously and digitally stored with a sampling rate of 6,660 samples/second. Wearable pulmonary function sensors may facilitate rapid point-of-care monitoring for high-risk individuals, especially during the COVID-19 pandemic, and easily interface with patient hospital records or telehealth services.

Effortful listening: Sympathetic activity varies as a function of listening demand but parasympathetic activity does not

Research on listening effort has used various physiological measures to examine the biological correlates of listening effort but a systematic examination of the impact of listening demand on cardiac autonomic nervous system activity is still lacking. The presented study aimed to close this gap by assessing cardiac sympathetic and parasympathetic responses to variations in listening demand. For this purpose, 45 participants performed four speech-in-noise tasks differing in listening demand-manipulated as signal-to-noise ratio varying between +23 dB and -16 dB-while their pre-ejection period and respiratory sinus arrythmia responses were assessed. Cardiac responses showed the expected effect of listening demand on sympathetic activity, but failed to provide evidence for the expected listening demand impact on parasympathetic activity: Pre-ejection period reactivity increased with increasing listening demand across the three possible listening conditions and was low in the very high (impossible) demand condition, whereas respiratory sinus arrythmia did not show this pattern. These findings have two main implications. First, cardiac sympathetic responses seem to be the more sensitive correlate of the impact of task demand on listening effort compared to cardiac parasympathetic responses. Second, very high listening demand may lead to disengagement and correspondingly low effort and reduced cardiac sympathetic response.

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.

Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added value of machine learning algorithms to separate clean from noisy bio-impedance signals. We compared three approaches: a heuristic algorithm, a feature-based classification model (SVM) and a convolutional neural network (CNN). The dataset consists of 47 chronic obstructive pulmonary disease patients who performed an inspiratory threshold loading protocol. During this protocol, their respiration was recorded with a bio-impedance device and a spirometer, which served as a gold standard. Four annotators scored the signals for the presence of artefacts, based on the reference signal. We have shown that the accuracy of both machine learning approaches (SVM: 87.77 ± 2.64% and CNN: 87.20 ± 2.78%) is significantly higher, compared to the heuristic approach (84.69 ± 2.32%). Moreover, no significant differences could be observed between the two machine learning approaches. The feature-based and neural network model obtained a respective AUC of 92.77±2.95% and 92.51±1.74%. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

Low Power Bio-Impedance Sensor Interfaces: Review and Electronics Design Methodology

Assessing blood flow, respiration patterns, and body composition with wearable and noninvasive bio-impedance (BioZ) sensors has distinctive advantages over the conventional clinical practice. The merits of BioZ sensors derive from having long-term monitoring capability and improved user friendliness. These open up the way to build medical grade wearable devices for chronic conditions. Low power, high precision BioZ sensor interface IC is the heart of such devices, it also determines the signal integrity of the overall system. Nevertheless, electrical design challenges from both circuit and system perspective still need to be addressed. This paper reviews the pioneering BioZ interface ICs and systems, and proposes major electrical specifications for wearable BioZ sensors. System design methodologies and circuit optimization techniques are summarized as guidelines to develop the next generation BioZ interface electronics.

Surface imaging for real-time patient respiratory function assessment in intensive care

PURPOSE

Monitoring of physiological parameters is a major concern in Intensive Care Units (ICU) given their role in the assessment of vital organ function. Within this context, one issue is the lack of efficient noncontact techniques for respiratory monitoring. In this paper, we present a novel noncontact solution for real-time respiratory monitoring and function assessment of ICU patients.

METHODS

The proposed system uses a Time-of-Flight depth sensor to analyze the patient's chest wall morphological changes in order to estimate multiple respiratory function parameters. The automatic detection of the patient's torso is also proposed using a deep neural network model trained on the COCO dataset. The evaluation of the proposed system was performed on a mannequin and on 16 mechanically ventilated patients (a total of 216 recordings) admitted in the ICU of the Brest University Hospital.

RESULTS

The estimation of respiratory parameters (respiratory rate and tidal volume) showed high correlation with the reference method (r = 0.99; P < 0.001 and r = 0.99; P < 0.001) in the mannequin recordings and (r = 0.95, P < 0.001 and r = 0.90, P < 0.001) for patients.

CONCLUSION

This study describes and evaluates a novel noncontact monitoring system suitable for continuous monitoring of key respiratory parameters for disease assessment of critically ill patients.

Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study

Background

Accumulation of excess body fluid and autonomic dysregulation are clinically important characteristics of acute decompensated heart failure. We hypothesized that transthoracic bioimpedance, a noninvasive, simple method for measuring fluid retention in lungs, and heart rate variability, an assessment of autonomic function, can be used for detection of fluid accumulation in patients with acute decompensated heart failure.

Objective

We aimed to evaluate the performance of transthoracic bioimpedance and heart rate variability parameters obtained using a fluid accumulation vest with carbon black–polydimethylsiloxane dry electrodes in a prospective clinical study (System for Heart Failure Identification Using an External Lung Fluid Device; SHIELD).

Methods

We computed 15 parameters: 8 were calculated from the model to fit Cole-Cole plots from transthoracic bioimpedance measurements (extracellular, intracellular, intracellular-extracellular difference, and intracellular-extracellular parallel circuit resistances as well as fitting error, resonance frequency, tissue heterogeneity, and cellular membrane capacitance), and 7 were based on linear (mean heart rate, low-frequency components of heart rate variability, high-frequency components of heart rate variability, normalized low-frequency components of heart rate variability, normalized high-frequency components of heart rate variability) and nonlinear (principal dynamic mode index of sympathetic function, and principal dynamic mode index of parasympathetic function) analysis of heart rate variability. We compared the values of these parameters between 3 participant data sets: control (n=32, patients who did not have heart failure), baseline (n=23, patients with acute decompensated heart failure taken at the time of admittance to the hospital), and discharge (n=17, patients with acute decompensated heart failure taken at the time of discharge from hospital). We used several machine learning approaches to classify participants with fluid accumulation (baseline) and without fluid accumulation (control and discharge), termed with fluid and without fluid groups, respectively.

Results

Among the 15 parameters, 3 transthoracic bioimpedance (extracellular resistance, R₀; difference in extracellular-intracellular resistance, R₀ – R_∞, and tissue heterogeneity, α) and 3 heart rate variability (high-frequency, normalized low-frequency, and normalized high-frequency components) parameters were found to be the most discriminatory between groups (patients with and patients without heart failure). R₀ and R₀ – R_∞ had significantly lower values for patients with heart failure than for those without heart failure (R₀: P=.006; R₀ – R_∞: P=.001), indicating that a higher volume of fluids accumulated in the lungs of patients with heart failure. A cubic support vector machine model using the 5 parameters achieved an accuracy of 92% for with fluid and without fluid group classification. The transthoracic bioimpedance parameters were related to intra- and extracellular fluid, whereas the heart rate variability parameters were mostly related to sympathetic activation.

Conclusions

This is useful, for instance, for an in-home diagnostic wearable to detect fluid accumulation. Results suggest that fluid accumulation, and subsequently acute decompensated heart failure detection, could be performed using transthoracic bioimpedance and heart rate variability measurements acquired with a wearable vest.

Combining Bioimpedance and Myographic Signals for the Assessment of COPD During Loaded Breathing

Chronic Obstructive Pulmonary Disease (COPD) is one of the most common chronic conditions. The current assessment of COPD requires a maximal maneuver during a spirometry test to quantify airflow limitations of patients. Other less invasive measurements such as thoracic bioimpedance and myographic signals have been studied as an alternative to classical methods as they provide information about respiration. Particularly, strong correlations have been shown between thoracic bioimpedance and respiratory volume. The main objective of this study is to investigate bioimpedance and its combination with myographic parameters in COPD patients to assess the applicability in respiratory disease monitoring. We measured bioimpedance, surface electromyography and surface mechanomyography in forty-three COPD patients during an incremental inspiratory threshold loading protocol. We introduced two novel features that can be used to assess COPD condition derived from the variation of bioimpedance and the electrical and mechanical activity during each respiratory cycle. These features demonstrate significant differences between mild and severe patients, indicating a lower inspiratory contribution of the inspiratory muscles to global respiratory ventilation in the severest COPD patients. In conclusion, the combination of bioimpedance and myographic signals provides useful indices to noninvasively assess the breathing of COPD patients.

Resting high frequency heart rate variability and PTSD symptomatology in Veterans: Effects of respiration, role in elevated heart rate, and extension to spouses

Heart rate variability (HRV) associated with parasympathetic activity (i.e., cardiac vagal tone) is reduced in posttraumatic stress disorder (PTSD), but possible confounding effects of respiration have not been studied sufficiently. Further, reduced parasympathetic inhibition might contribute to elevated heart rate (HR) in PTSD. Finally, reduced HRV in PTSD might extend to intimate partners, given their chronic stress exposure. In 65 couples (male Veterans, female partners), elevated PTSD symptomatology (n = 32; 28 met full DSM IV criteria, 4 fell slightly short) was documented by structured interview and self-reports. Baseline HR, high-frequency HRV (HF-HRV), cardiac pre-ejection period (PEP), and respiration rate and depth were measured via impedance cardiography. Veterans with PTSD symptoms displayed reduced lnHF-HRV, even when adjusting for respiration, but their partners did not. In mediational analyses, elevated resting HR in PTSD was accounted for by lnHF-HRV but not PEP. Results strengthen evidence regarding HF-HRV and elevated HR in PTSD.

Analysis of Time Delay between Bioimpedance and Respiratory Volume Signals under Inspiratory Loaded Breathing

Bioimpedance is known for its linear relation with volume during normal breathing. For that reason, bioimpedance can be used as a noninvasive and comfortable technique for measuring respiration. The goal of this study is to analyze the temporal behavior of bioimpedance measured in four different electrode configurations during inspiratory loaded breathing. We measured four bioimpedance channels and airflow simultaneously in 10 healthy subjects while incremental inspiratory loads were imposed. Inspiratory loading threshold protocols are associated with breathing pattern changes and were used in respiratory mechanics studies. Consequently, this respiratory protocol allowed us to induce breathing pattern changes and evaluate the temporal relationship of bioimpedance with volume. We estimated the temporal delay between bioimpedance and volume respiratory cycles to evaluate the differences in their temporal behavior. The delays were computed as the lag which maximize the cross-correlation of the signals cycle by cycle. Six of the ten subjects showed delays in at least two different inspiratory loads. The delays were dependent on electrode configuration, hence the appearance of the delays between bioimpedance and volume were conditioned to the location and geometry of the electrode configuration. In conclusion, the delays between these signals could provide information about breathing pattern when breathing conditions change.

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.

A Sensor for Spirometric Feedback in Ventilation Maneuvers during Cardiopulmonary Resuscitation Training

This work proposes adapting an existing sensor and embedding it on mannequins used in cardiopulmonary resuscitation (CPR) training to accurately measure the amount of air supplied to the lungs during ventilation. Mathematical modeling, calibration, and validation of the sensor along with metrology, statistical inference, and spirometry techniques were used as a base for aquiring scientific knowledge of the system. The system directly measures the variable of interest (air volume) and refers to spirometric techniques in the elaboration of its model. This improves the realism of the dummies during the CPR training, because it estimates, in real-time, not only the volume of air entering in the lungs but also the Forced Vital Capacity (FVC), Forced Expiratory Volume (FEVt) and Medium Forced Expiratory Flow (FEF_20–75%). The validation of the sensor achieved results that address the requirements for this application, that is, the error below 3.4% of full scale. During the spirometric tests, the system presented the measurement results of (305 ± 22, 450 ± 23, 603 ± 24, 751 ± 26, 922 ± 27, 1021 ± 30, 1182 ± 33, 1326 ± 36, 1476 ± 37, 1618 ± 45 and 1786 ± 56) × 10⁻⁶ m³ for reference values of (300, 450, 600, 750, 900, 1050, 1200, 1350, 1500, 1650 and 1800) × 10⁻⁶ m³, respectively. Therefore, considering the spirometry and pressure boundary conditions of the manikin lungs, the system achieves the objective of simulating valid spirometric data for debriefings, that is, there is an agreement between the measurement results when compared to the signal generated by a commercial spirometer (Koko brand). The main advantages that this work presents in relation to the sensors commonly used for this purpose are: (i) the reduced cost, which makes it possible, for the first time, to use a respiratory volume sensor in medical simulators or training dummies; (ii) the direct measurement of air entering the lung using a noninvasive method, which makes it possible to use spirometry parameters to characterize simulated human respiration during the CPR training; and (iii) the measurement of spirometric parameters (FVC, FEVt, and FEF_20–75%), in real-time, during the CPR training, to achieve optimal ventilation performance. Therefore, the system developed in this work addresses the minimum requirements for the practice of ventilation in the CPR maneuvers and has great potential in several future applications.

Tidal breathing flow profiles during sleep in wheezing children measured by impedance pneumography

For the first time, impedance pneumography (IP) enables a continuous analysis of the tidal breathing flow volume (TBFV), overnight. We studied how corticosteroid inhalation treatments, sleep stage, and time from sleep onset modify the nocturnal TBFV profiles of children. Seventy children, 1-5 years old and with recurrent wheezing, underwent three, full-night TBFVs recordings at home, using IP. The first recorded one week before ending a 3-months inhaled corticosteroids treatment, and remaining two, 2 and 4 weeks after treatment. TBFV profiles were grouped by hour from sleep onset and estimated sleep stage. Compared with on-medication, the off-medication profiles showed lower volume at exhalation peak flow, earlier interruption of expiration, and less convex middle expiration. The differences in the first two features were significant during non-rapid eye movement (NREM), and the differences in the third were more prominent during REM after 4 h of sleep. These combinations of TBFV features, sleep phase, and sleep time potentially indicate airflow limitation in young children.

The association between individual differences in executive functioning and resting high-frequency heart rate variability

Both resting high-frequency heart rate variability (HF-HRV) and executive functioning (EF) are individual differences implicated in vulnerability to a wide range of adverse outcomes. The overlapping set of associations, along with theoretical models positing connections between the brain regions subserving the executive functions and the parasympathetic nervous system, suggest that the two factors should be correlated. Seeking to address limitations in prior research, the current study examined the association between EF, measured comprehensively with individually-administered neuropsychological tests and controlling for lower-order cognitive processes, and resting physiology, measured with impedence cardiography, in healthy, community participants (68% female; mean age = 27, SD = 6.5). Results confirmed a significant association between EF and resting HF-HRV, but no association with resting state sympathetic nervous system activation (pre-ejection period). These findings may inform future investigation of transdiagnostic mechanisms related to these two individual difference factors.

Electrocardiogram-Derived Tidal Volume During Treadmill Stress Test

OBJECTIVE

Electrocardiogram (ECG) has been regarded as a source of respiratory information with the main focus in the estimation of the respiratory rate. Although little research concerning the estimation of tidal volume (TV) has been conducted, there are several ECG-derived features that have been related with TV in the literature, such as ECG-derived respiration, heart rate variability, and respiratory rate. In this paper, we exploited these features for estimating TV using a linear model.

METHODS

25 young (33.4 ± 5.2 years) healthy male volunteers were recruited for performing a maximal (MaxT) and a submaximal (SubT) treadmill stress test, which were conducted on different days. Both tests were automatically segmented in stages attending to the heart rate. Afterwards, a subject-specific TV model was calibrated for each stage, employing features from MaxT, and the model was later used for estimating the TV in SubT.

RESULTS

During exercise, the different proposed approaches led to relative fitting errors lower than 14% in most of the cases and 6% in some of them.

CONCLUSION

Low achieved fitting errors suggest that TV can be estimated from ECG during a treadmill stress test.

SIGNIFICANCE

The results suggest that it is possible to estimate TV during exercise using only ECG-derived features.

Assessing regional lung mechanics by combining electrical impedance tomography and forced oscillation technique

There is a lack of noninvasive pulmonary function tests which can assess regional information of the lungs. Electrical impedance tomography (EIT) is a radiation-free, non-invasive real-time imaging that provides regional information of ventilation volume regarding the measurement of electrical impedance distribution. Forced oscillation technique (FOT) is a pulmonary function test which is based on the measurement of respiratory mechanical impedance over a frequency range. In this article, we introduce a new measurement approach by combining FOT and EIT, named the oscillatory electrical impedance tomography (oEIT). Our oEIT measurement system consists of a valve-based FOT device, an EIT device, pressure and flow sensors, and a computer fusing the data streams. Measurements were performed on five healthy volunteers at the frequencies 3, 4, 5, 6, 7, 8, 10, 15, and 20 Hz. The measurements suggest that the combination of FOT and EIT is a promising approach. High frequency responses are visible in the derivative of the global impedance index ΔZeit(t,fos). $\Delta {Z_{{\text{eit}}}}(t,{f_{{\text{os}}}}).$ The oEIT signals consist of three main components: forced oscillation, spontaneous breathing, and heart activity. The amplitude of the oscillation component decreases with increasing frequency. The band-pass filtered oEIT signal might be a new tool in regional lung function diagnostics, since local responses to high frequency perturbation could be distinguished between different lung regions.

Fusion of Heart Rate, Respiration and Motion Measurements from a Wearable Sensor System to Enhance Energy Expenditure Estimation

This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21⁻65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R² = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R² = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R² = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications.

Analysis of Consistency of Transthoracic Bioimpedance Measurements Acquired with Dry Carbon Black PDMS Electrodes, Adhesive Electrodes, and Wet Textile Electrodes

The detection of intrathoracic volume retention could be crucial to the early detection of decompensated heart failure (HF). Transthoracic Bioimpedance (TBI) measurement is an indirect, promising approach to assessing intrathoracic fluid volume. Gel-based adhesive electrodes can produce skin irritation, as the patient needs to place them daily in the same spots. Textile electrodes can reduce skin irritation; however, they inconveniently require wetting before each use and provide poor adherence to the skin. Previously, we developed waterproof reusable dry carbon black polydimethylsiloxane (CB/PDMS) electrodes that exhibited a good response to motion artifacts. We examined whether these CB/PDMS electrodes were suitable sensing components to be embedded into a monitoring vest for measuring TBI and the electrocardiogram (ECG). We recruited N = 20 subjects to collect TBI and ECG data. The TBI parameters were different between the various types of electrodes. Inter-subject variability for copper-mesh CB/PDMS electrodes and Ag/AgCl electrodes was lower compared to textile electrodes, and the intra-subject variability was similar between the copper-mesh CB/PDMS and Ag/AgCl. We concluded that the copper mesh CB/PDMS (CM/CB/PDMS) electrodes are a suitable alternative for textile electrodes for TBI measurements, but with the benefit of better skin adherence and without the requirement of wetting the electrodes, which can often be forgotten by the stressed HF subjects.

The Ultrasonic Directional Tidal Breathing Pattern Sensor: Equitable Design Realization Based on Phase Information

Pulmonary ailments are conventionally diagnosed by spirometry. The complex forceful breathing maneuver as well as the extreme cost of spirometry renders it unsuitable in many situations. This work is aimed to facilitate an emerging direction of tidal breathing-based pulmonary evaluation by designing a novel, equitable, precise and portable device for acquisition and analysis of directional tidal breathing patterns, in real time. The proposed system primarily uses an in-house designed blow pipe, 40-kHz air-coupled ultrasound transreceivers, and a radio frequency (RF) phase-gain integrated circuit (IC). Moreover, in order to achieve high sensitivity in a cost-effective design philosophy, we have exploited the phase measurement technique, instead of selecting the contemporary time-of-flight (TOF) measurement; since application of the TOF principle in tidal breathing assessments requires sub-micro to nanosecond time resolution. This approach, which depends on accurate phase measurement, contributed to enhanced sensitivity using a simple electronics design. The developed system has been calibrated using a standard 3-L calibration syringe. The parameters of this system are validated against a standard spirometer, with maximum percentage error below 16%. Further, the extracted respiratory parameters related to tidal breathing have been found to be comparable with relevant prior works. The error in detecting respiration rate only is 3.9% compared to manual evaluation. These encouraging insights reveal the definite potential of our tidal breathing pattern (TBP) prototype for measuring tidal breathing parameters in order to extend the reach of affordable healthcare in rural regions and developing areas.

Assessment of calibration methods on impedance pneumography accuracy

The aim was to assess accuracy of tidal volumes (TV) calculated by impedance pneumography (IP), reproducibility of calibration coefficients (CC) between IP and pneumotachometry (PNT), and their relationship with body posture, breathing rate and depth. Fourteen students performed three sessions of 18 series: normal and deep breathing at 6, 10, 15 breaths/min rates, while supine, sitting and standing; 18 CC were calculated for every session. Session 2 was performed 2 months after session 1, session 3 1-3 days after session 2. TV were calculated using full or limited set of CC from current session, in case of sessions 2 and 3 also using CC from session 1 and 2, respectively. When using full set of CC from current session, IP underestimated TV by -3.2%. Using CC from session 2 for session 3 measurements caused decrease of relative difference: -3.9%, from session 1 for session 2: -5.3%; for limited set of CC: -5.0%. The body posture had significant effect on CC. The highest accuracy was obtained when all factors influencing CC were considered. The application of CC related only to body posture may result in shortening of calibration and moderate accuracy loss. Using CC from previous session compromises accuracy moderately.

Measurement of tidal breathing flows in infants using impedance pneumography

Tidal breathing flow volume (TBFV) profiles have been used to characterise altered lung function. Impedance pneumography (IP) is a novel option for assessing TBFV curves noninvasively. The aim of this study was to extend the application of IP for infants and to estimate the agreement between IP and direct pneumotachograph (PNT) measurements in assessing tidal airflow and flow-derived indices.

Tidal flow profiles were recorded for 1 min simultaneously with PNT and uncalibrated IP at baseline in 44 symptomatic infants, and after methacholine-induced bronchoconstriction in a subgroup (n=20).

The agreement expressed as the mean deviation from linearity ranged between 3.9 and 4.3% of tidal peak inspiratory flow, but was associated with specific airway conductance (p=0.002) and maximal flow at functional residual capacity (V′maxFRC) (p=0.004) at baseline. Acute bronchoconstriction induced by methacholine did not significantly affect the agreement of IP with PNT. TBFV indices derived from IP were slightly underestimated compared to PNT, but were equally well repeatable and associated with baseline V′maxFRC (p=0.012 and p=0.013, respectively).

TBFV profiles were consistent between IP and PNT in most infants, but the agreement was affected by reduced lung function. TBFV parameters were not interchangeable between IP and PNT, but had a similar association with lung function in infants.

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

The focus of this paper is motion artifact (MA) reduction from the impedance pneumography (IP) signal, which is widely used to monitor respiration. The amplitude of the MA that contaminates the IP signal is often much larger than the amplitude of the respiratory component of the signal. Moreover, the morphology and frequency composition of the artifacts may be very similar to that of the respiration, making it difficult to remove these artifacts. The proposed filter uses a regularization term to ensure that the pattern of the filtered signal is similar to that of respiration. It also ensures that the amplitude of the filter output is within the expected range of the IP signal by imposing an ε-tube on the filtered signal. The adaptive ε-tube filter is 100 times faster than the previously proposed nonadaptive version and achieves higher accuracies. Moreover, the experimental results, using several different performance measures, suggest that the proposed method outperforms popular MA reduction methods such as normalized least mean squares (NLMS) and recursive least squares (RLS) as well as independent component analysis (ICA). When used to extract the respiratory rate, the adaptive ε-tube achieves a mean error of 1.27 breaths per minute (BPM) compared to 4.72 and 4.63 BPM for the NLMS and RLS filters, respectively. When compared to the ICA algorithm, the proposed filter has an error of 1.06 BPM compared to 3.47 BPM for ICA. The statistical analyses indicate that all of the reported performance improvements are significant.

Tidal breathing flow-volume curves with impedance pneumography during expiratory loading

Diagnosis of asthma in the preschoold children is difficult due to lack of objective lung function tests suitable for this age group. Impedance pneumography (IP) is a mode of measurement that may potentially enable ambulatory 24h recording of tidal breathing indices and respiratory dynamics that are known to relate to small airway obstruction. The aim of this research was to induce changes in breathing control and mechanics and study the ability of IP to reproduce TBFVC and track its changes under potentially difficult conditions. This was achieved by a comparison of direct mouth pneumotachograph (PNT) and IP tidal breathing flow-volume curves (TBFVC) during free breathing and expiratory loading obtained from 17 young lung-healthy subjects. The expiratory loading produced strong and significant changes in the respiratory pattern and mouth pressure. The agreement of PNT and IP normalized TBFVCs was found excellent having the highest distance between the normalized TBFVCs of (mean ± SD) 7.4 % ± 3.6 % and 6.2 % ± 3.0 % during free and loaded breathing, respectively. The agreement was not affected by the presence of the expiratory load despite it poses multiple potential hazards for the IP measurements. We conclude that by using correct electrode placement and cardiac filtering, IP was able to accurately reproduce and track changes in normalized TBFVCs under normal and abnormal respiratory conditions in healthy adult subjects.

Portable bioimpedance monitor evaluation for continuous impedance measurements. Towards wearable plethysmography applications

Personalised Health Systems (PHS) that could benefit the life quality of the patients as well as decreasing the health care costs for society among other factors are arisen. The purpose of this paper is to study the capabilities of the System-on-Chip Impedance Network Analyser AD5933 performing high speed single frequency continuous bioimpedance measurements. From a theoretical analysis, the minimum continuous impedance estimation time was determined, and the AD5933 with a custom 4-Electrode Analog Front-End (AFE) was used to experimentally determine the maximum continuous impedance estimation frequency as well as the system impedance estimation error when measuring a 2R1C electrical circuit model. Transthoracic Electrical Bioimpedance (TEB) measurements in a healthy subject were obtained using 3M gel electrodes in a tetrapolar lateral spot electrode configuration. The obtained TEB raw signal was filtered in MATLAB to obtain the respiration and cardiogenic signals, and from the cardiogenic signal the impedance derivative signal (dZ/dt) was also calculated. The results have shown that the maximum continuous impedance estimation rate was approximately 550 measurements per second with a magnitude estimation error below 1% on 2R1C-parallel bridge measurements. The displayed respiration and cardiac signals exhibited good performance, and they could be used to obtain valuable information in some plethysmography monitoring applications. The obtained results suggest that the AD5933-based monitor could be used for the implementation of a portable and wearable Bioimpedance plethysmograph that could be used in applications such as Impedance Cardiography. These results combined with the research done in functional garments and textile electrodes might enable the implementation of PHS applications in a relatively short time from now.

A robust electrode configuration for bioimpedance measurement of respiration

Electrode configuration is an important issue in the continuous measurement of respiration using impedance pneumography (IP). The robust configuration is usually confirmed by comparing the amplitude of the IP signals acquired with different electrode configurations, while the relative change in waveform and the effects of body posture and respiratory pattern are ignored. In this study, the IP signals and respiratory volume are simultaneously acquired from 8 healthy subjects in supine, left lying, right lying and prone postures, and the subjects are asked to perform four respiratory patterns including free breathing, thoracic breathing, abdominal breathing and apnea. The IP signals are acquired with four different chest electrode configurations, and the volume are measured using pneumotachograph (PNT). Differences in correlation and absolute deviation between the IP-derived and PNT-derived respiratory volume are assessed. The influences of noise, respiratory pattern and body posture on the IP signals of different configurations have significant difference (p < 0.05). The robust electrode configuration is found on the axillary midline, which is suitable for long term respiration monitoring.

Retrospective respiration-gated whole-body photoacoustic computed tomography of mice

Photoacoustic tomography (PAT) is an emerging technique that has a great potential for preclinical whole-body imaging. To date, most whole-body PAT systems require multiple laser shots to generate one cross-sectional image, yielding a frame rate of <1  Hz. Because a mouse breathes at up to 3 Hz, without proper gating mechanisms, acquired images are susceptible to motion artifacts. Here, we introduce, for the first time to our knowledge, retrospective respiratory gating for whole-body photoacoustic computed tomography. This new method involves simultaneous capturing of the animal's respiratory waveform during photoacoustic data acquisition. The recorded photoacoustic signals are sorted and clustered according to the respiratory phase, and an image of the animal at each respiratory phase is reconstructed subsequently from the corresponding cluster. The new method was tested in a ring-shaped confocal photoacoustic computed tomography system with a hardware-limited frame rate of 0.625 Hz. After respiratory gating, we observed sharper vascular and anatomical images at different positions of the animal body. The entire breathing cycle can also be visualized at 20  frames/cycle.

A review of signals used in sleep analysis

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.

Development and evaluation of an improved technique for pulmonary function testing using electrical impedance pneumography intended for the diagnosis of chronic obstructive pulmonary disease patients

Spirometry is regarded as the only effective method for detecting pulmonary function test (PFT) indices. In this study, a novel impedance pulmonary function measurement system (IPFS) is developed for directly assessing PFT indices. IPFS can obtain high resolution values and remove motion artifacts through real-time base impedance feedback. Feedback enables the detection of PFT indices using only both hands for convenience. IPFS showed no differences in the sitting, supine, and standing postures during the measurements, indicating that patient posture has no effect on IPFS. Mean distance analysis showed good agreement between the volume and flow signal of IPFS (p < 0.05). PFT indices were detected in subjects to differentiate a chronic obstructive pulmonary disease (COPD) patient group from a normal group. The forced vital capacity (FVC), forced expiratory volume in the first second (FEV₁), FEV_1/FVC, and peak expiratory flow (PEF) in the COPD group were lower than those in the normal group by IPFS (p < 0.05). IPFS is therefore suitable for evaluating pulmonary function in normal and COPD patients. Moreover, IPFS could be useful for periodic monitoring of existing patients diagnosed with obstructive lung disease.

Novel electrode configuration for highly linear impedance pneumography

Impedance pneumography (IP) is a non-invasive respiration measurement technique. Emerging applications of IP in respiratory medicine use the measured signal to monitor pulmonary flow and volume parameters related to airway obstruction during tidal breathing (TB). This requires a high impedance change (ΔZ)-to-lung volume change (ΔV) linearity. Four potential electrode configurations were tested on 10 healthy subjects. Only the novel configuration where the electrodes were placed in both the thorax and the arms yielded a highly linear ΔZ/ΔV in all subjects. The presented electrode configuration may expand the clinical use of IP from the conventional tidal volume estimation to flow measurement.

Tidal breathing flow measurement in awake young children by using impedance pneumography

Characteristics of tidal breathing (TB) relate to lung function and may be assessed even in young children. Thus far, the accuracy of impedance pneumography (IP) in recording TB flows in young children with or without bronchial obstruction has not been evaluated. The aim of this study was to evaluate the agreement between IP and direct flow measurement with pneumotachograph (PNT) in assessing TB flow and flow-derived indices relating to airway obstruction in young children. Tidal flow was recorded for 1 min simultaneously with IP and PNT during different phases of a bronchial challenge test with methacholine in 21 wheezy children aged 3 to 7 years. The agreement of IP with PNT was found to be excellent in direct flow signal comparison, the mean deviation from linearity ranging from 2.4 to 3.1% of tidal peak inspiratory flow. Methacholine-induced bronchoconstriction or consecutive bronchodilation induced only minor changes in the agreement. Between IP and PNT, the obstruction-related tidal flow indices were equally repeatable, and agreement was found to be high, with intraclass correlation coefficients for T PTEF/T E, V PTEF/V E, and parameter S being 0.94, 0.91, and 0.68, respectively. Methacholine-induced changes in tidal flow indices showed significant associations with changes in mechanical impedance of the respiratory system assessed by the oscillometric technique, with the highest correlation found in V PTEF/V E (r = -0.54; P < 0.005 and r = -0.55; P < 0.005 by using IP or PNT, respectively). The results indicate that IP can be considered as a valid method for recording tidal airflow profiles in young children with wheezing disorders.

Noncontact respiratory measurement of volume change using depth camera

In this study, a system is developed to measure human chest wall motion for respiratory volume estimation without any physical contact. Based on depth image sensing technique, respiratory volume is estimated by measuring morphological changes of the chest wall. We evaluated the system and compared with a standard reference device, and the results show strong agreement in respiratory volume measurement [correlation coefficient: r=0.966]. The isovolume test presents small variations of the total respiratory volume during the isovolume maneuver (standard deviation&#60;107 ml). Then, a regional pulmonary measurement test is evaluated by a patient, and the results show visibly difference of pulmonary functional between the diseased and the contralateral sides of the thorax after the thoracotomy. This study has big potential for personal health care and preventive medicine as it provides a novel, low-cost, and convenient way to measure user's respiration volume.

Multilead measurement system for the time-domain analysis of bioimpedance magnitude

Bioimpedance measurement applications range from the characterization of organic matter to the monitoring of biological signals and physiological parameters. Occasionally, multiple bioimpedances measured in different locations are combined in order to solve complex problems or produce enhanced physiological measures. The present multilead bioimpedance measurement methods are mainly focused on electrical impedance tomography. Systems designed to suit other multilead applications are lacking. In this study, a novel multilead bioimpedance measurement system was designed. This was particularly aimed at the time-domain analysis of bioimpedance magnitude. Frequency division multiplexing was used to avoid overlapping between excitation signals; undersampling, to reduce the hardware requirements; and power isolated active current sources, to reduce the electrical interactions between leads. These theoretical concepts were implemented on a prototype device. The prototype was tested on equivalent circuits and a saline tank in order to assess excitation signal interferences and electrical interactions between leads. The results showed that the proposed techniques are functional and the system's validity was demonstrated on a real application, multilead impedance pneumography. Potential applications and further improvements were discussed. It was concluded that the novel approach potentially enables accurate and relatively low-power multilead bioimpedance measurements systems.