Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD

Inferring forced expiratory volume in 1 second (FEV1) from mobile ECG signals collected during quiet breathing

Objective.Forced expiratory volume in one second (FEV1) is an important metric for patients to track at home for their self-management of asthma and chronic obstructive pulmonary disease (COPD). Unfortunately, the state-of-the-art for measuring FEV1 at home either depends on the patient's physical effort and motivation, or relies on bulky wearable devices that are impractical for long-term monitoring. This paper explores the feasibility of using a machine learning model to infer FEV1 from 270 seconds of a single-lead electrocardiogram (ECG) signal measured on the fingers with a mobile device.Methods.We evaluated the model's inferred FEV1 values against the ground truth of hospital-grade spirometry tests, which were performed by twenty-five patients with obstructive respiratory disease.Results.The model-inferred FEV1 compared to the spirometry-measured FEV1 with a correlation coefficient ofr = 0.73, a mean absolute percentage error of 23% and a bias of -0.08.Conclusions.These results suggest that the ECG signal contains useful information about FEV1, although a larger, richer dataset might be necessary to train a machine learning model that can extract this information with better accuracy.Significance.The benefit of a mobile ECG-based solution for measuring FEV1 is that it would require minimal effort, thus encouraging patient adherence and promoting successful self-management of asthma and COPD.

TriSpectraKAN: a novel approach for COPD detection via lung sound analysis

This study aims to create an automated, accessible, and cost-effective diagnostic tool for chronic obstructive pulmonary disease (COPD). Traditional diagnostic methods are expensive, time-consuming, and require specialized equipment. The proposed TriSpectraKAN model leverages audio-based lung sound features to improve early diagnosis. TriSpectraKAN is a hybrid model combining spectral features and the Kolmogorov-Arnold Network (KAN) to analyze lung sounds using Mel-frequency cepstral coefficients (MFCCs), chromagram, and Mel spectrograms. Each sub-model focuses on a different audio feature, capturing unique sonic signatures. These features are merged through a hybrid network for comprehensive analysis. The model, trained on a COPD dataset, was deployed on a Raspberry Pi for real-time use. TriSpectraKAN achieved 93% accuracy, an F1 score of 0.98, precision of 0.97, and recall of 0.98. This multimodal approach captured a broad range of lung sound features, improving diagnosis accuracy compared to traditional methods. The integration of multiple audio features in TriSpectraKAN enhances COPD diagnosis, demonstrating the potential of AI and machine learning to transform respiratory disease diagnosis through accessible tools.

Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review

BACKGROUND

Chronic Obstructive Pulmonary Disease (COPD) represents a significant global health challenge, placing considerable burdens on healthcare systems. The rise of digital health technologies (DHTs) and artificial intelligence (AI) algorithms offers new opportunities to improve COPD predictive capabilities, diagnostic accuracy, and patient management. This systematic review explores the types of data in COPD under DHTs, the AI algorithms employed for data analysis, and identifies key application areas reported in the literature.

METHODS

A systematic search was conducted in PubMed and Web of Science for studies published up to December 2024 that applied AI algorithms in digital health for COPD management. Inclusion criteria focused on original research utilizing AI algorithms and digital health technologies for COPD, while review articles were excluded. Two independent reviewers screened the studies, resolving discrepancies through consensus.

RESULTS

From an initial pool of 265 studies, 41 met the inclusion criteria. Analysis of these studies highlighted a diverse range of data types and modalities collected from DHTs in the COPD context, including clinical data, patient-reported outcomes, and environmental/lifestyle data. Machine learning (ML) algorithms were employed in 34 studies, and deep learning (DL) algorithms in 16. Support vector machines and boosting were the most frequently used ML models, while deep neural networks (DNN) and convolutional neural networks (CNN) were the most commonly used DL models. The review identified three key application domains for AI in COPD: screening and diagnosis (10 studies), exacerbation prediction (22 studies), and patient monitoring (9 studies). Disease progression prediction was a prevalent focus across three domains, with promising accuracy and performance metrics reported.

CONCLUSIONS

Digital health technologies and AI algorithms have a wide range of applications and promise for COPD management. ML models, in particularly, show great potential in improving digital health solutions for COPD. Future research should focus on enhancing global collaboration to explore the cost-effectiveness and data-sharing capabilities of DHTs, enhancing the interpretability of AI models, and validating these algorithms through clinical trials to facilitate their safe integration into the routine COPD management.

WF-PPG: A Wrist-finger Dual-Channel Dataset for Studying the Impact of Contact Pressure on PPG Morphology

Photoplethysmography (PPG) is a simple optical technique widely used in wearable devices for continuous cardiac health monitoring. However, the quality of PPG signals, particularly their morphology, is influenced by the contact pressure between the skin and the sensor. This variability in signal quality complicates complex tasks that rely on high-quality signals, such as blood pressure and heart rate variability estimation, making them less reliable or even impossible. To address this issue, we present a novel dataset (termed WF-PPG) comprising PPG signals from the wrist measured under varying contact pressures, along with high-quality PPG signals from the fingertip captured simultaneously. Data collection was conducted using a custom device setup capable of precisely adjusting the contact pressure for wrist PPG signals while also recording additional metrics such as contact pressure, electrocardiogram (ECG), blood pressure, and oxygen saturation. WF-PPG is designed to facilitate the analysis of effects of contact pressure on PPG morphology and to support the development and evaluation of advanced data-driven techniques aimed at enhancing the reliability of PPG-based health monitoring.

Designing a Hybrid Energy-Efficient Harvesting System for Head- or Wrist-Worn Healthcare Wearable Devices

Battery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance is a major challenge. Additionally, integrating energy harvesting components without compromising the wearability, comfort, and esthetic design of healthcare devices presents a significant bottleneck. Here, we show that with a meticulous design using small and highly efficient photovoltaic (PV) panels, compact thermoelectric (TEG) modules, and two ultra-low-power BQ25504 DC-DC boost converters, the battery life can increase from 9.31 h to over 18 h. The parallel connection of boost converters at two points of the output allows both energy sources to individually achieve maximum power point tracking (MPPT) during battery charging. We found that under specific conditions such as facing the sun for more than two hours, the device became self-powered. Our results demonstrate the long-term and stable performance of the sensor node with an efficiency of 96%. Given the high-power density of solar cells outdoors, a combination of PV and TEG energy can harvest energy quickly and sufficiently from sunlight and body heat. The small form factor of the harvesting system and the environmental conditions of particular occupations such as the oil and gas industry make it suitable for health monitoring wearables worn on the head, face, or wrist region, targeting outdoor workers.

Tracking Tidal Volume From Holter and Wearable Armband Electrocardiogram Monitoring

A novel method for tracking the tidal volume (TV) from electrocardiogram (ECG) is presented. The method is based on the amplitude of ECG-derived respiration (EDR) signals. Three different morphology-based EDR signals and three different amplitude estimation methods have been studied, leading to a total of 9 amplitude-EDR (AEDR) signals per ECG channel. The potential of these AEDR signals to track the changes in TV was analyzed. These methods do not need a calibration process. In addition, a personalized-calibration approach for TV estimation is proposed, based on a linear model that uses all AEDR signals from a device. All methods have been validated with two different ECG devices: a commercial Holter monitor, and a custom-made wearable armband. The lowest errors for the personalized-calibration methods, compared to a reference TV, were -3.48% [-17.41% / 12.93%] (median [first quartile / third quartile]) for the Holter monitor, and 0.28% [-10.90% / 17.15%] for the armband. On the other hand, medians of correlations to the reference TV were higher than 0.8 for uncalibrated methods, while they were higher than 0.9 for personal-calibrated methods. These results suggest that TV changes can be tracked from ECG using either a conventional (Holter) setup, or our custom-made wearable armband. These results also suggest that the methods are not as reliable in applications that induce small changes in TV, but they can be potentially useful for detecting large changes in TV, such as sleep apnea/hypopnea and/or exacerbations of a chronic respiratory disease.

Application and Prospects of Artificial Intelligence Technology in Early Screening of Chronic Obstructive Pulmonary Disease at Primary Healthcare Institutions in China

Chronic Obstructive Pulmonary Disease (COPD), as one of the major global health threat diseases, particularly in China, presents a high prevalence and mortality rate. Early diagnosis is crucial for controlling disease progression and improving patient prognosis. However, due to the lack of significant early symptoms, the awareness and diagnosis rates of COPD remain low. Against this background, primary healthcare institutions play a key role in identifying high-risk groups and early diagnosis. With the development of Artificial Intelligence (AI) technology, its potential in enhancing the efficiency and accuracy of COPD screening is evident. This paper discusses the characteristics of high-risk groups for COPD, current screening methods, and the application of AI technology in various aspects of screening. It also highlights challenges in AI application, such as data privacy, algorithm accuracy, and interpretability. Suggestions for improvement, such as enhancing AI technology dissemination, improving data quality, promoting interdisciplinary cooperation, and strengthening policy and financial support, aim to further enhance the effectiveness and prospects of AI technology in COPD screening at primary healthcare institutions in China.

Physically Meaningful Surrogate Data for COPD

The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are "data hungry" whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks. To ensure the faithfulness of our domain-aware COPD surrogates, the generated waveforms are examined through both flow waveforms and photoplethysmography (PPG) waveforms (as a proxy for intrathoracic pressure) in terms of duty cycle, sample entropy, FEV1/FVC ratios and flow-volume loops. The proposed simulator operates on healthy subjects and is able to generate FEV1/FVC obstruction ratios ranging from greater than 0.8 to less than 0.2, mirroring values that can observed in the full spectrum of real-world COPD. As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. When training solely on surrogate data, and testing on real-world data, a comparison of true positive rate against false positive rate yields an area under the curve of 0.75, compared with 0.63 when training solely on real-world data.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Data-Driven Personalized Care in Lung Cancer: Scoping Review and Clinical Recommendations on Performance Status and Activity Level of Patients With Lung Cancer Using Wearable Devices

PURPOSE

Performance status (PS) is a crucial assessment for research and clinical practice in lung cancer (LC), including its usage for the assessment of the suitability and toxicity of treatment or eligibility for clinical trials of patients with LC. These PS assessments are subjective and lead to substantial discrepancies between observers. To improve the objectivity of PS assessments, Electronic Activity Monitoring devices (EAMs) are increasingly used in oncology, but how these devices are used for PS assessments in LC is an issue that remains unclear. The goal of this study is to address the challenges and opportunities of the use of digital tools to support PS assessments in patients with LC.

METHODS

The literature review followed PRISMA-ScR methodology. Searches were performed in the ScienceDirect, PsycInfo, ACM, IEEE Xplore, and PubMed databases. Furthermore, a panel discussion was performed to address the clinical use cases.

RESULTS

Thirty-two publications were found. Most of the studies used wrist accelerometry-based wearables (59%) and monitored sleep activity (SA; 28%) or physical activity (PA; 72%). Critical findings include positive usefulness of the use of wearables to categorize moderate-to-vigorous/light PA, which was associated with better sleep and health. In addition, steps and time awake immobile were found to be associated with risk of hospitalization and survival. Use cases identified included the health assessment of patients and clinical research.

CONCLUSION

There are positive experiences in the use of EAM to complement PS assessment in LC. However, there is a need for adapting thresholds to the particularities of patients with LC, for example, differentiating moderate-to-vigorous and light. Moreover, developing methodologies combining PS assessments and the use of EAM adapted to clinical and research practice is needed.

The Principles of Hearable Photoplethysmography Analysis and Applications in Physiological Monitoring-A Review

Not long ago, hearables paved the way for biosensing, fitness, and healthcare monitoring. Smart earbuds today are not only producing sound but also monitoring vital signs. Reliable determination of cardiovascular and pulmonary system information can explore the use of hearables for physiological monitoring. Recent research shows that photoplethysmography (PPG) signals not only contain details on oxygen saturation level (SPO2) but also carry more physiological information including pulse rate, respiration rate, blood pressure, and arterial-related information. The analysis of the PPG signal from the ear has proven to be reliable and accurate in the research setting. (1) Background: The present integrative review explores the existing literature on an in-ear PPG signal and its application. This review aims to identify the current technology and usage of in-ear PPG and existing evidence on in-ear PPG in physiological monitoring. This review also analyzes in-ear (PPG) measurement configuration and principle, waveform characteristics, processing technology, and feature extraction characteristics. (2) Methods: We performed a comprehensive search to discover relevant in-ear PPG articles published until December 2022. The following electronic databases: Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Scopus, Web of Science, and PubMed were utilized to conduct the studies addressing the evidence of in-ear PPG in physiological monitoring. (3) Results: Fourteen studies were identified but nine studies were finalized. Eight studies were on different principles and configurations of hearable PPG, and eight studies were on processing technology and feature extraction and its evidence in in-ear physiological monitoring. We also highlighted the limitations and challenges of using in-ear PPG in physiological monitoring. (4) Conclusions: The available evidence has revealed the future of in-ear PPG in physiological monitoring. We have also analyzed the potential limitation and challenges that in-ear PPG will face in processing the signal.

Hearables: Heart Rate Variability from Ear Electrocardiogram and Ear Photoplethysmogram (Ear-ECG and Ear-PPG)

This work aims to classify physiological states using heart rate variability (HRV) features extracted from electrocardiograms recorded in the ears (ear-ECG). The physiological states considered in this work are: (a) normal breathing, (b) controlled slow breathing, and (c) mental exercises. Since both (b) and (c) cause higher variance in heartbeat intervals, breathing-related features (SpO2 and mean breathing interval) from the ear Photoplethysmogram (ear-PPG) are used to facilitate classification. This work: 1) proposes a scheme that, after initialization, automatically extracts R-peaks from low signal-to-noise ratio ear-ECG; 2) verifies the feasibility of extracting meaningful HRV features from ear-ECG; 3) quantitatively compares several ear-ECG sites; and 4) discusses the benefits of combining ear-ECG and ear-PPG features.

Feasibility of Transfer Learning from Finger PPG to In-Ear PPG

The success of deep learning methods has enabled many modern wearable health applications, but has also highlighted the critical caveat of their extremely data hungry nature. While the widely explored wrist and finger photoplethysmography (PPG) sites are less affected, given the large available databases, this issue is prohibitive to exploring the full potential of novel recording locations such as in-ear wearables. To this end, we assess the feasibility of transfer learning from finger PPG to in-ear PPG in the context of deep learning for respiratory monitoring. This is achieved by introducing an encoder-decoder framework which is set up to extract respiratory waveforms from PPG, whereby simultaneously recorded gold standard respiratory waveforms (capnography, impedance pneumography and air flow) are used as a training reference. Next, the data augmentation and training pipeline is examined for both training on finger PPG and the subsequent fine tuning on in-ear PPG. The results indicate that, through training on two large finger PPG data sets (95 subjects) and then retraining on our own small in-ear PPG data set (6 subjects), the model achieves lower and more consistent test error for the prediction of the respiratory waveforms, compared to training on the small in-ear data set alone. This conclusively demonstrates the feasibility of transfer learning from finger PPG to in-ear PPG, leading to better generalisation across a wide range of respiratory rates.

An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study

Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring.

Adaptive scheduling of acceleration and gyroscope for motion artifact cancelation in photoplethysmography

BACKGROUND AND OBJECTIVE

Recently, various algorithms have been introduced using wrist-worn photoplethysmography (PPG) to provide high accuracy of instantaneous heart rate (HR) estimation, including during high-intensity exercise. Most studies focus on using acceleration and/or gyroscope signals for the motion artifact (MA) reference, which attenuates or cancels out noise from the MA-corrupted PPG signals. We aim to open and pave the path to find an appropriate MA reference selection for MA cancelation in PPG.

METHODS

We investigated how the acceleration and gyroscope reference signals correlate with the MAs of the distorted PPG signals and derived both mathematically and experimentally an adaptive MA reference selection approach. We applied our algorithm to five state-of-the-art (SOTA) methods for the performance evaluation. In addition, we compared the four MA reference selection approaches, i.e. with acceleration signal only, with gyroscope signal only, with both signals, and using our proposed adaptive selection.

RESULTS

When applied to 47 PPG recordings acquired during intensive physical exercise from two different datasets, our proposed adaptive MA reference selection method provided higher accuracy than the other MA selection approaches for all five SOTA methods.

CONCLUSION

Our proposed adaptive MA reference selection approach can be used in other MA cancelation methods and reduces the HR estimation error.

SIGNIFICANCE

We believe that this study helps researchers to address acceleration and gyroscope signals as accurate MA references, which eventually improves the overall performance for estimating HRs through the various algorithms developed by research groups.

Comprehensive Identification of Surface Subsidence Evaluation Grades of Mines in Southwest China

Due to their complex geological structure, it is difficult to systematically analyze the surface subsidence of coal mines in southwest China, and the factors that cause surface subsidence are also different from other coal mines. Focusing on the problem of surface subsidence caused by mining in southwest China’s mines, a grade evaluation system for surface subsidence of southwest mines is constructed based on the analytic hierarchy process, and ten evaluation indicators are established from the perspectives of mining disturbance and geological structure. A matter–element model of surface subsidence based on matter–element extension theory and a cloud model of surface subsidence based on cloud theory are then constructed. A coal mine in Anshun, Guizhou, is taken as an example to calculate the evaluation level of surface subsidence and thus verify the scientificity of extension theory and cloud theory. The results show that the main factors that affect the surface subsidence of southwest mines are the number of coal seam layers, mining height and comprehensive Platt hardness of rock, similar to that of northern plain coal mines. Surface slope and subsidence area are also very important. The comprehensive correlation degree of each grade of the coal mine is −0.29836, 0.192232, −0.1093 and −0.46531, and the coal mine is concluded to be in grade 2. The calculated similarity of the overall index evaluation cloud map of the coal mine and each grade is 0, 0.3453, 0.7872 and 0, respectively. The coal mine is in grade 2, which is a relatively safe state. Consistent with the calculation results of the extension model and in line with the field situation, the extension matter–element model and cloud model built in this paper can verify each other and have a certain scientificity.

Tracking Cognitive Workload in Gaming with In-Ear SpO 2

The feasibility of using in-earSpO 2 to track cognitive workload induced by gaming is investigated. This is achieved by examining temporal variations in cognitive workload through the game Geometry Dash, with 250 trials across 7 subjects. The relationship between performance and cognitive load in Dark Souls III boss fights is also investigated followed by a comparison of the cognitive workload responses across three different genres of game. A robust decrease in in-earSpO 2 is observed in response to cognitive workload induced by gaming, which is consistent with existing results from memory tasks. The results tentatively suggest that in-earSpO 2 may be able to distinguish cognitive workload alone, whereas heart rate and breathing rate respond similarly to both cognitive workload and stress. This study demonstrates the feasibility of low cost wearable cognitive workload tracking in gaming with in-earSpO 2 , with applications to the play testing of games and biofeedback in games of the future.

Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.