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.

Mid-Activity and At-Home Wearable Bioimpedance Elucidates an Interpretable Digital Biomarker of Muscle Fatigue.

OBJECTIVE: Muscle health and decreased muscle performance (fatigue) quantification has proven to be an invaluable tool for both athletic performance assessment and injury prevention. However, existing methods estimating muscle fatigue are infeasible for everyday use. Wearable technologies are feasible for everyday use and can enable discovery of digital biomarkers of muscle fatigue. Unfortunately, the current state-of-the-art wearable systems for muscle fatigue tracking suffer from either low specificity or poor usability. METHODS: We propose using dual-frequency bioimpedance analysis (DFBIA) to non-invasively assess intramuscular fluid dynamics and thereby muscle fatigue. A wearable DFBIA system was developed to measure leg muscle fatigue of 11 individuals during a 13-day protocol consisting of exercise and unsupervised at-home portions. RESULTS: We derived a digital biomarker of muscle fatigue, fatigue score, from the DFBIA signals that was able to estimate the percent reduction in muscle force during exercise with repeated-measures Pearson's r = 0.90 and mean absolute error (MAE) of 3.6%. This fatigue score also estimated delayed onset muscle soreness with repeated-measures Pearson's r = 0.83 and MAE = 0.83. Using at-home data, DFBIA was strongly associated with absolute muscle force of participants (n = 198, p < 0.001). CONCLUSION: These results demonstrate the utility of wearable DFBIA for non-invasively estimating muscle force and pain through the changes in intramuscular fluid dynamics. SIGNIFICANCE: The presented approach may inform development of future wearable systems for quantifying muscle health and provide a novel framework for athletic performance optimization and injury prevention.

Wearable Mid-Activity Measurement of Lower Limb Electrical Bioimpedance Estimates Vertical Ground Reaction Force Features.

In recent years, wearable mid-activity electrical bioimpedance (EBI) sensing has been used to non-invasively track changes in edema and swelling levels within human joints. While the physiological origin of the changes in mid-activity EBI measurements have been demonstrated, EBI waveform patterns during activity have not been explored. In this work, we present a novel approach to extract waveform features from EBI measurements during gait to estimate the changes in vertical ground reaction forces (vGRF) corresponding to fatigue. Wearable EBI and vGRF data were measured from six healthy subjects during an asymmetric fatiguing protocol. For the exercised leg, the first peak of vGRF corresponding to the initial phase of simple support, decreased significantly and the loading rate increased significantly between the beginning and the end of the protocol. No significant change in these parameters were observed for the control leg. The first peak of vGRF and loading rate during the protocol (15 walking sessions) were correlated to the multi-frequency EBI features with mean Pearson's r=0.81 and r=0.777, respectively. The results of this proof-of-concept study demonstrate the feasibility of estimating biomechanical parameters during activity with wearable EBI. Clinical Relevance - The proposed wearable system and associated signal processing could enable convenient tracking of changes in vGRFs during daily living activities, allowing physiotherapists and doctors to remotely monitor the progress and adherence of their patients and thereby reducing the number of clinical visits.

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.

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.