Impedance Pneumography Signal Research Articles

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.

Adaptive Motion Artifact Reduction in Wearable ECG Measurements Using Impedance Pneumography Signal.

Noise is a common problem in wearable electrocardiogram (ECG) monitoring systems because the presence of noise can corrupt the ECG waveform causing inaccurate signal interpretation. By comparison with electromagnetic interference and its minimization, the reduction of motion artifact is more difficult and challenging because its time-frequency characteristics are unpredictable. Based on the characteristics of motion artifacts, this work uses adaptive filtering, a specially designed ECG device, and an Impedance Pneumography (IP) data acquisition system to combat motion artifacts. The newly designed ECG-IP acquisition system maximizes signal correlation by measuring both ECG and IP signals simultaneously using the same pair of electrodes. Signal comparison investigations between ECG and IP signals under five different body motions were carried out, and the Pearson Correlation Coefficient |r| was higher than 0.6 in all cases, indicating a good correlation. To optimize the performance of adaptive motion artifact reduction, the IP signal was filtered to a 5 Hz low-pass filter and then fed into a Recursive Least Squares (RLS) adaptive filter as a reference input signal. The performance of the proposed motion artifact reduction method was evaluated subjectively and objectively, and the results proved that the method could suppress the motion artifacts and achieve minimal distortion to the denoised ECG signal.

Causality in cardiorespiratory signals in pediatric cardiac patients.

Four different Granger causality-based methods - one linear and three nonlinear (Granger Causality, Kernel Granger Causality, large-scale Nonlinear Granger Causality, and Neural Network Granger Causality) were used for assessment and causal-based quantification of the respiratory sinus arrythmia (RSA) in the group of pediatric cardiac patients, based on the single-lead ECG and impedance pneumography signals (the latter as the tidal volume curve equivalent). Each method was able to detect the dependency (in terms of causal inference) between respiratory and cardiac signals. The correlations between quantified RSA and the demographic parameters were also studied, but the results differ for each method. Clinical relevance- The presented methods (among which NNGC seems to be the most valid) allow for quantification of RSA and study of dependency between tidal volume and RR intervals which may help to better understand association between respiratory and cardiovascular systems in different populations.

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

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

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 % and %. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

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.

Discovery of Causal Paths in Cardiorespiratory Parameters: A Time-Independent Approach in Elite Athletes.

Training of elite athletes requires regular physiological and medical monitoring to plan the schedule, intensity and volume of training, and subsequent recovery. In sports medicine, ECG-based analyses are well-established. However, they rarely consider the correspondence of respiratory and cardiac activity. Given such mutual influence, we hypothesize that athlete monitoring might be developed with causal inference and that detailed, time-related techniques should be preceded by a more general, time-independent approach that considers the whole group of participants and parameters describing whole signals. The aim of this study was to discover general causal paths among cardiac and respiratory variables in elite athletes in two body positions (supine and standing), at rest. ECG and impedance pneumography signals were obtained from 100 elite athletes. The mean heart rate, the root-mean-square difference of successive RR intervals (RMSSD), its natural logarithm (lnRMSSD), the mean respiratory rate (RR), the breathing activity coefficients, and the resulting breathing regularity (BR) were estimated. Several causal discovery frameworks were applied, comprising Generalized Correlations (GC), Causal Additive Modeling (CAM), Fast Greedy Equivalence Search (FGES), Greedy Fast Causal Inference (GFCI), and two score-based Bayesian network learning algorithms: Hill-Climbing (HC) and Tabu Search. The discovery of cardiorespiratory paths appears ambiguous. The main, still mild, rules best supported by data are: for supine - tidal volume causes heart activity variation, which causes average heart activity, which causes respiratory timing; and for standing - normalized respiratory activity variation causes average heart activity. The presented approach allows data-driven and time-independent analysis of elite athletes as a particular population, without considering prior knowledge. However, the results seem to be consistent with the medical background. Causality inference is an interesting mathematical approach to the analysis of biological responses, which are complex. One can use it to profile athletes and plan appropriate training. In the next step, we plan to expand the study using time-related causality analyses.

Derivation of respiration rate from ambulatory ECG and PPG using Ensemble Empirical Mode Decomposition: Comparison and fusion

A new method for extracting the respiratory rate from ECG and PPG obtained via wearable sensors is presented. The proposed technique employs Ensemble Empirical Mode Decomposition in order to identify the respiration “mode” from the noise-corrupted Heart Rate Variability/Pulse Rate Variability and Amplitude Modulation signals extracted from ECG and PPG signals. The technique was validated with respect to a Respiratory Impedance Pneumography (RIP) signal using the mean absolute and the average relative errors for a group ambulatory hospital patients. We compared approaches using single respiration-induced modulations on the ECG and PPG signals with approaches fusing the different modulations. Additionally, we investigated whether the presence of both the simultaneously recorded ECG and PPG signals provided a benefit in the overall system performance. Our method outperformed state-of-the-art ECG- and PPG-based algorithms and gave the best results over the whole database with a mean error of 1.8bpm for 1min estimates when using the fused ECG modulations, which was a relative error of 10.3%. No statistically significant differences were found when comparing the ECG-, PPG- and ECG/PPG-based approaches, indicating that the PPG can be used as a valid alternative to the ECG for applications using wearable sensors. While the presence of both the ECG and PPG signals did not provide an improvement in the estimation error, it increased the proportion of windows for which an estimate was obtained by at least 9%, indicating that the use of two simultaneously recorded signals might be desirable in high-acuity cases where an RR estimate is required more frequently.

Comparison of simple algorithms for estimating respiration rate from electrical impedance pneumography signals in wearable devices

Respiration rate (RR) is considered as a useful parameter in characterizing the health condition of a person. Among the methods used for respiration measurement, Electrical Impedance Pneumography (EIP) can be easily obtained in wearable applications due to the possibility of using the electrocardiography (ECG) electrodes for the EIP measurement. In the fast growing field of wearable devices, having clinically valuable and reliable information along with providing the convenience of the user, is probably the most important and challenging issue. To address the need of small sized devices for ECG (and EIP) measurements, EASI electrode configuration is an acceptable solution. The signals from EASI system not only provide useful information by themselves when directly used for cardiological analyses, but can also be converted to the standard 12-lead ECG information. With aforementioned advantages of EASI system, the question then arises how suitable the electrode locations of the system are for EIP measurements and what algorithms perform better for respiration rate derivation. In this work, we evaluated eight methods for deriving respiration rate from EIP signals measured from 15 subjects (10 males +5 females) in three conditions: standing, walking slowly, and walking fast. The algorithms were autoregressive (AR) modeling (three different approaches), Fast Fourier Transform (FFT), autocorrelation, peak detection and two counting algorithms. Our results show that advanced counting method is the most promising approach among the ones studied in this work. For this algorithm, the concordance correlation coefficients of the respiration rate estimates between EIP and the reference measurement were 0.96, 0.90 and 0.97 for standing, walking with 3 km/h speed, and walking with 6 km/h speed, respectively.

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.

A method for suppressing cardiogenic oscillations in impedance pneumography

The transthoracic electrical impedance signal originates from the cardiac and respiratory functions. In impedance pneumography (IP) the lung function is assessed and the cardiac impedance signal, cardiogenic oscillations (CGOs), is considered an additive noise in the measured signal. In order to accurately determine pulmonary flow parameters from the signal, the CGO needs to be attenuated without distorting the respiratory part of the signal. We assessed the suitability of a filtering technique, originally described by Schuessler et al (1998 Ann. Biomed. Eng. 26 260–7) for an esophageal pressure signal, for CGO attenuation in the IP signal. The technique is based on ensemble averaging the CGO events using the electrocardiogram (ECG) R-wave as the trigger signal. Lung volume is known to affect the CGO waveforms. Therefore we modified the filtering method to produce a lung volume-dependent parametric model of the CGO waveform. A simultaneous recording of ECG, IP and pneumotachograph (PNT) was conducted on 41 healthy, sitting adults. The performance of the proposed method was compared to a low-pass filter and a Savitzky–Golay filter in terms of CGO attenuation and respiratory signal distortion. The method was found to be excellent, exhibiting CGO attenuation of 35.0±12.5 dB (mean±SD) and minimal distortion of the respiratory part of the impedance signal.

Assessment of Pulmonary Flow Using Impedance Pneumography

There is a lack of noninvasive pulmonary function measurement techniques suitable for continuous long-term measurement of tidal breathing in mobile subjects, although tidal breathing analysis has been shown to contain information that relates to the level airway obstruction. This paper is the first to assess the suitability of impedance pneumography (IP) for measurement of continuous pulmonary flow and volume signals instead of only the respiration rate (RR) or tidal volume ( V(T)). We measured pneumotachograph (PNT) and IP signals simultaneously from 20 healthy male subjects in erect, dorsal supine, and lateral supine positions while voluntarily varying V(T). IP was measured using five different impedance lead configurations with electrodes integrated into a textile chest belt. The IP signals were compared with PNT signals to assess agreement of IP with a more well-established measurement method. The pulmonary flow signal waveform agreement was assessed with standard error of measurement (SEM) between the time-differentiated IP signal and the PNT signal as rho = 1-SEM. Additionally, we assessed the agreement of IP and PNT in V(T) estimation and the magnitude of the cardiogenic oscillation present in the impedance signal. The agreement in the pulmonary flow signal waveform shapes was found excellent at all tidal volumes and postures (mean rho > 0.90). The agreement between the PNT-derived and the IP-derived V(T) estimates was very high when IP values were calibrated per subject and posture (mean difference < 3%). The main source of error in visual inspection of the IP signal was the cardiogenic distortion. From the five novel electrode configurations tested, the lateral ones were found clearly better than the anteroposterior ones. IP potentially enables the development of a noninvasive ambulatory measurement device for long-term studies of certain tidal breathing parameters in mobile subjects.

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.

An implantable impedance pneumograph monitor for detection of diaphragm contraction and airway obstruction during diaphragm pacing

Impedance pneumography signals were characterised during diaphragm pacing using stimulating and recording electrodes placed on the abdominal surface of the diaphragm. These measurements were useful for the detection of muscle contraction without confounding effects from stimulus artifacts. Impedance pneumography signals were measured using 23 epimysial electrodes implanted in seven dogs with 1-5 experiments on each electrode. The polarity of the change in impedance associated with diaphragm pacing differed for each recording electrode and its configuration. Thirty-four of 57 cases produced increased impedance, 11 produced decreased impedance and the remaining 12 depended on the level of diaphragm activation. Impedance pneumography signals were useful for detecting complete airway obstruction. The mean difference between the impedance measured during open and obstructed airway conditions was 80% of the open airway impedance signal. The difference between open and obstructed airway impedance measurements was a mean of 2.3 times larger with a recording electrode on the same hemidiaphragm as the stimulating electrode, compared to an electrode placed on the opposite hemidiaphragm (p < 0.05, paired t test, four dogs). In addition, the differences between open and completely obstructed airways were a mean of 2.8 times larger when the second recording electrode was placed on the thorax at the fifth intercostal space, compared to the ninth intercostal space (p < 0.05, two-factor ANOVA, one dog, two replicates). It was concluded that impedance pneumograph circuitry could be incorporated into an existing diaphragm pacer using electrodes placed on the diaphragm to provide valuable measurements of the function of the device.