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

Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter

Objective.Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF.Approach.This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) pre-processing, (2) separation of crackles from normal breath sounds using the iterative envelope mean fractal dimension filter, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of high-resolution computed tomography images, reviewed by two expert radiologists for the presence or absence of PF, was used as the ground truth for evaluating the PF and non-PF classification performance of the system.Main results.The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC = 0.845, 95% CI 0.739-0.952,p< 0.001; sensitivity = 91.7%; specificity = 59.3%) compares favourably with the averaged performance of the physicians (sensitivity = 83.3%; specificity = 56.25%).Significance.Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease (ILD), the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of ILD.

Digital twin driven electrode optimization for wearable bladder monitoring via bioimpedance

Monitoring fluid intake and output for congestive heart failure (CHF) patients is an essential tool to prevent fluid overload, a principal cause of hospital admissions. Addressing this, bladder volume measurement systems utilizing bioimpedance and electrical impedance tomography have been proposed, with limited exploration of continuous monitoring within a wearable design. Advancing this format, we developed a conductivity digital twin from radiological data, where we performed exhaustive simulations to optimize electrode sensitivity on an individual basis. Our optimized placement demonstrated an efficient proof-of-concept volume estimation that required as few as seven measurement frames while maintaining low errors (CI 95% -1.11% to 1.00%) for volumes ≥100 mL. Additionally, we quantify the impact of ascites, a common confounding condition in CHF, on the bioimpedance signal. By improving monitoring technology, we aim to reduce CHF mortality by empowering patients and clinicians with a more thorough understanding of fluid status.

Advancements in automated classification of chronic obstructive pulmonary disease based on computed tomography imaging features through deep learning approaches

Chronic Obstructive Pulmonary Disease (COPD) represents a global public health issue that significantly impairs patients' quality of life and overall health. As one of the primary causes of chronic respiratory diseases and global mortality, effective diagnosis and classification of COPD are crucial for clinical management. Pulmonary function tests (PFTs) are standard for diagnosing COPD, yet their accuracy is influenced by patient compliance and other factors, and they struggle to detect early disease pathologies. Furthermore, the complexity of COPD pathological changes poses additional challenges for clinical diagnosis, increasing the difficulty for physicians in practice. Recently, deep learning (DL) technologies have demonstrated significant potential in medical image analysis, particularly for the diagnosis and classification of COPD. By analyzing key radiological features such as airway alterations, emphysema, and vascular characteristics in Computed Tomography (CT) scan images, DL enhances diagnostic accuracy and efficiency, providing more precise treatment plans for COPD patients. This article reviews the latest research advancements in DL methods based on principal radiological features of COPD for its classification and discusses the advantages, challenges, and future research directions of DL in this field, aiming to provide new perspectives for the personalized management and treatment of COPD.

A scoping review on advancements in noninvasive wearable technology for heart failure management

Wearables offer a promising solution for enhancing remote monitoring (RM) of heart failure (HF) patients by tracking key physiological parameters. Despite their potential, their clinical integration faces challenges due to the lack of rigorous evaluations. This review aims to summarize the current evidence and assess the readiness of wearables for clinical practice using the Medical Device Readiness Level (MDRL). A systematic search identified 99 studies from 3112 found articles, with only eight being randomized controlled trials. Accelerometery was the most used measurement technique. Consumer-grade wearables, repurposed for HF monitoring, dominated the studies with most of them in the feasibility testing stage (MDRL 6). Only two of the described wearables were specifically designed for HF RM, and received FDA approval. Consequently, the actual impact of wearables on HF management remains uncertain due to limited robust evidence, posing a significant barrier to their integration into HF care.

Recent Advances in the Wearable Devices for Monitoring and Management of Heart Failure

Heart failure (HF) is an acute and degenerative condition with high morbidity and mortality rates. Early diagnosis and treatment of HF can significantly enhance patient outcomes through admission and readmission reduction and improve quality of life. Being a progressive condition, the continuous monitoring of vital signs and symptoms of HF patients to identify any deterioration and to customize treatment regimens can be beneficial to the management of this disease. Recent breakthroughs in wearable technology have revolutionized the landscape of HF management. Despite the potential benefits, the integration of wearable devices into HF management requires careful consideration of technical, clinical, and ethical challenges, such as performance, regulatory requirements and data privacy. This review summarizes the current evidence on the role of wearable devices in heart failure monitoring and management, and discusses the challenges and opportunities in the field.

Wearable Devices Based on Bioimpedance Test in Heart Failure: Clinical Relevance: Systematic Review

Background

Heart failure (HF) represents a frequent cause of hospital admission, with fluid overload directly contributing to decompensations. Bioimpedance (BI), a physical parameter linked to tissue hydration status, holds promise in monitoring congestion and improving prognosis. This systematic review aimed to assess the clinical relevance of BI-based wearable devices for HF fluid monitoring.

Methods

A systematic review of the published literature was conducted in five medical databases (PubMed, Scopus, Cochrane, Web of Science, and Embase) for studies assessing wearable BI-measuring devices on HF patients following PRISMA recommendations on February 4th, 2024. The risk of bias was evaluated using the ROBINS tool.

Results

The review included 10 articles with 535 participants (mean age 66.7 ± 8.9 years, males 70.4%). Three articles identified significant BI value differences between HF patients and controls or congestive vs non-congestive HF patients. Four articles focused on the devices' ability to predict HF worsening-related events, revealing an overall sensitivity of 70.0 (95% CI 68.8-71.1) and specificity of 89.1 (95% CI 88.3-89.9). One article assessed prognosis, showing that R80kHz decrease was related to all-cause-mortality with a hazard ratio (HR) of 5.51 (95% CI 1.55-23.32; p = 0.02) and the composite all-cause-mortality and HF admission with a HR of 4.96 (95% CI 1.82-14.37; p = 0.01).

Conclusions

BI-measuring wearable devices exhibit efficacy in detecting fluid overload and hold promise for HF monitoring. However, further studies and technological improvements are required to optimize their impact on prognosis compared to standard care before they can be routinely implemented in clinical practice.

PROSPERO Registration

The search protocol was registered at PROSPERO (CRD42024509914).

Regional Pulmonary Ventilation Assessment Method and System Based on Impedance Sensing Information from the Pentapulmonary Lobes

Regional lung ventilation assessment is a critical tool for the early detection of lung diseases and postoperative evaluation. Biosensor-based impedance measurements, known for their non-invasive nature, among other benefits, have garnered significant attention compared to traditional detection methods that utilize pressure sensors. However, solely utilizing overall thoracic impedance fails to accurately capture changes in regional lung air volume. This study introduces an assessment method for lung ventilation that utilizes impedance data from the five lobes, develops a nonlinear model correlating regional impedance with lung air volume, and formulates an approach to identify regional ventilation obstructions based on impedance variations in affected areas. The electrode configuration for the five lung lobes was established through numerical simulations, revealing a power-function nonlinear relationship between regional impedance and air volume changes. An analysis of 389 pulmonary function tests refined the equations for calculating pulmonary function parameters, taking into account individual differences. Validation tests on 30 cases indicated maximum relative errors of 0.82% for FVC and 0.98% for FEV1, all within the 95% confidence intervals. The index for assessing regional ventilation impairment was corroborated by CT scans in 50 critical care cases, with 10 validation trials showing agreement with CT lesion localization results.

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.

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Localized Multi-Site Knee Bioimpedance as a Predictor for Knee Osteoarthritis Associated Pain Within Older Adults During Free-Living

The drastic increase in the aging population has increased the prevalence of osteoarthritis in the United States. The ability to monitor symptoms of osteoarthritis (such as pain) within a free-living environment could improve understanding of each person's experiences with this disease and provide opportunities to personalize treatments specific to each person and their experience. In this work, localized knee tissue bioimpedance and self-reports of knee pain were collected from older adults ([Formula: see text]) with and without knee osteoarthritis over 7 days of free-living to evaluate if knee tissue bioimpedance is associated with persons' knee pain experience. Within the group of persons' with knee osteoarthritis increases in 128 kHz per-length resistance and decreases in 40 kHz per-length reactance were associated with increased probability of persons having active knee pain ([Formula: see text] and [Formula: see text]).

System Performance and User Feedback Regarding Wearable Bioimpedance System for Multi-Site Knee Tissue Monitoring: Free-Living Pilot Study With Healthy Adults

Knee-focused wearable devices have the potential to support personalized rehabilitation therapies by monitoring localized tissue alterations related to activities that reduce functional symptoms and pain. However, supporting these applications requires reported data to be reliable and accurate which can be challenging in the unsupervised free-living conditions that wearable devices are deployed. This pilot study has assessed a knee-focused wearable sensor system to quantify 1) system performance (operation, rates of data artifacts, environment impacts) to estimate realistic targets for reliable data with this system and 2) user experiences (comfort, fit, usability) to help inform future designs to increase usability and adoption of knee-focused wearables. Study data was collected from five healthy adult participants over 2 days, with 84.5 and 35.9% of artifact free data for longitudinal and transverse electrode configurations. Small to moderate positive correlations were also identified between changes in resistance, temperature, and humidity with respect to acceleration to highlight how this system can be used to explore relationships between knee tissues and environmental/activity context.