Low-power wearable respiratory sound sensing

Current technological advancement in asthma care

INTRODUCTION

Asthma is a common chronic respiratory disease affecting 262 million people globally, causing half a million deaths each year. Poor asthma outcomes are frequently due to non-adherence to medication, poor engagement with asthma services, and a lack of objective diagnostic tests. In recent years, technologies have been developed to improve diagnosis, monitoring, and care.

AREAS COVERED

Technology has impacted asthma care with the potential to improve patient outcomes, reduce healthcare costs, and provide personalized management. We focus on current evidence on home diagnostics and monitoring, remote asthma reviews, and digital smart inhalers. PubMed, Ovid/Embase, Cochrane Library, Scopus and Google Scholar were searched in November 2023 with no limit by year of publication.

EXPERT OPINION

Advanced diagnostic technologies have enabled early asthma detection and personalized treatment plans. Mobile applications and digital therapeutics empower patients to manage their condition and improve adherence to treatments. Telemedicine platforms and remote monitoring devices have the potential to streamline asthma care. AI algorithms can analyze patient data and predict exacerbations in proof-of-concept studies. Technology can potentially provide precision medicine to a wider patient group in the future, but further development is essential for implementation into routine care which in itself will be a major challenge.

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database

It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians' workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants. Accurate label is important to achieve a good prediction for adventitious respiratory sound classification problem. A custom-made sound label annotation software (SoundAnn) has been developed to perform sound editing, sound annotation, and quality assurance evaluation. A team of 11 experienced paediatric physicians is involved in the entire process to establish golden standard reference for the dataset. To verify the robustness and accuracy of the classification model, we have investigated the effects of different feature extraction methods and machine learning classifiers on the classification performance of our dataset. As such, we have achieved a score of 75.22%, 61.57%, 56.71%, and 37.84% for the four different classification challenges at the event level and record level.

Investigating Stroke Effects on Respiratory Parameters Using a Wearable Device: A Pilot Study on Hemiplegic Patients

Quantitatively assessing personal health status is gaining increasing attention due to the improvement of diagnostic technology and the increasing occurrence of chronic pathologies. Monitoring physiological parameters allows for retrieving a general overview of the personal health status. Respiratory activity can provide relevant information, especially when pathologies affect the muscles and organs involved in breathing. Among many technologies, wearables may represent a valid solution for continuous and remote monitoring of respiratory activity, thus reducing healthcare costs. The most popular wearables used in this arena are based on detecting the breathing-induced movement of the chest wall. Therefore, their use in patients with impaired chest wall motion and abnormal respiratory kinematics can be challenging, but literature is still in its infancy. This study investigates the performance of a custom wearable device for respiratory monitoring in post-stroke patients. We tested the device on six hemiplegic patients under different respiratory regimes. The estimated respiratory parameters (i.e., respiratory frequency and the timing of the respiratory phase) demonstrated good agreement with the ones provided by a gold standard device. The promising results of this pilot study encourage the exploitation of wearables on these patients that may strongly impact the treatment of chronic diseases, such as hemiplegia.

Predictive performance and impact of algorithms in remote monitoring of chronic conditions: A systematic review and meta-analysis

BACKGROUND

The use of telehealth interventions, such as the remote monitoring of patient clinical data (e.g. blood pressure, blood glucose, heart rate, medication use), has been proposed as a strategy to better manage chronic conditions and to reduce the impact on patients and healthcare systems. The use of algorithms for data acquisition, analysis, transmission, communication and visualisation are now common in remote patient monitoring. However, their use and impact on chronic disease management has not been systematically investigated.

OBJECTIVES

To investigate the use, impact, and performance of remote monitoring algorithms across various types of chronic conditions.

METHODS

A literature search of MEDLINE complete, CINHAL complete, and EMBASE was performed using search terms relating to the concepts of remote monitoring, chronic conditions, and data processing algorithms. Comparable outcomes from studies describing the impact on process measures and clinical and patient-reported outcomes were pooled for a summary effect and meta-analyses. A comparison of studies reporting the predictive performance of algorithms was also conducted using the Youden Index.

RESULTS

A total of 89 articles were included in the review. There was no evidence of a positive impact on healthcare utilisation [OR 1.09 (0.90 to 1.31); P = .35] and mortality [OR 0.83 (0.63 to 1.10); P = .208], but there was a positive effect on generic health status [SDM 0.2912 (0.06 to 0.51); P = .010] and diabetes control [SDM -0.53 (-0.74 to -0.33); P < .001; I² = 15.71] (with two of the three diabetes studies being identified as having a high risk of bias). While the majority of impact studies made use of heuristic threshold-based algorithms (n = 27,87%), most performance studies (n = 36, 62%) analysed non-sequential machine learning methods. There was considerable variance in the quality, sample size and performance amongst these studies. Overall, algorithms involved in diagnosis (n = 22, 47%) had superior performance to those involved in predicting a future event (n = 25, 53%). Detection of arrythmia and ischaemia utilising ECG data showed particularly promising results.

CONCLUSION

The performance of data processing algorithms for the diagnosis of a current condition, particularly those related to the detection of arrythmia and ischaemia, is promising. However, there appears to exist minimal testing in experimental studies, with only two included impact studies citing a performance study as support for the intervention algorithm used. Because of the disconnect between performance and impact studies, there is currently limited evidence of the effect of integrating advanced inference algorithms in remote monitoring interventions. If the field of remote patient monitoring is to progress, future impact studies should address this disconnect by evaluating high performance validated algorithms in robust clinical trials.

CoughMatch - Subject Verification Using Cough for Personal Passive Health Monitoring

Automatic cough detection using audio has advanced passive health monitoring on devices such as smart phones and wearables; it enables capturing longitudinal health data by eliminating user interaction and effort. One major issue arises when coughs from surrounding people are also detected; capturing false coughs leads to significant false alarms, excessive cough frequency, and thereby misdiagnosis of user condition. To address this limitation, in this paper, a method is proposed that creates a personal cough model of the primary subject using limited number of cough samples; the model is used by the automatic cough detection to verify whether the identified coughs match the personal pattern and belong to the primary subject. A Gaussian mixture model is trained using audio features from cough to implement the subject verification method; novel cough embeddings are learned using neural networks and integrated into the model to further improve the prediction accuracy. We analyze the performance of the method using our cough dataset collected by a smart phone in a clinical study. Population in the dataset involves subjects categorized of healthy or patients with COPD or Asthma, with the purpose of covering a wider range of pulmonary conditions. Cross-subject validation on a diverse dataset shows that the method achieves an average error rate of less than 10%, using a personal cough model generated by only 5 coughs from the primary subject.

Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification

This paper presents an energy-optimized electronic performance tracking system (EPTS) device for analyzing the athletic movements of football players. We first develop a tiny battery-operated wearable device that can be attached to the backside of field players. In order to analyze the strategic performance, the proposed wearable EPTS device utilizes the GNSS-based positioning solution, the IMU-based movement sensing system, and the real-time data acquisition protocol. As the life-time of the EPTS device is in general limited due to the energy-hungry GNSS sensing operations, for the energy-efficient solution extending the operating time, in this work, we newly develop the advanced optimization methods that can reduce the number of GNSS accesses without degrading the data quality. The proposed method basically identifies football activities during the match time, and the sampling rate of the GNSS module is dynamically relaxed when the player performs static movements. A novel deep convolution neural network (DCNN) is newly developed to provide the accurate classification of human activities, and various compression techniques are applied to reduce the model size of the DCNN algorithm, allowing the on-device DCNN processing even at the memory-limited EPTS device. Experimental results show that the proposed DCNN-assisted sensing control can reduce the active power by 28%, consequently extending the life-time of the EPTS device more than 1.3 times.

Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments

Cardiorespiratory (CR) signals are crucial vital signs for fitness condition tracking, medical diagnosis, and athlete performance evaluation. Monitoring such signals in real-life settings is among the most widespread applications of wearable computing. We investigate how miniaturized barometers can be used to perform accurate spirometry in a wearable system that is built on off-the-shelf training masks often used by athletes as a training aid. We perform an evaluation where differential barometric pressure sensors are compared concurrently with a digital spirometer, during an experimental setting of clinical forced vital capacity (FVC) test procedures with 20 participants. The relationship between the two instruments is derived by mathematical modeling first, then by various regression methods from experiment data. The results show that the error of FVC vital values between the two instruments can be as low as 2∼3%. Beyond clinical tests, the method can also measure continuous tidal breathing air volumes with a 1∼3% error margin. Overall, we conclude that barometers with millimeter footprints embedded in face mask apparel can perform similarly to a digital spirometer to monitor breathing airflow and volume in pulmonary function tests.

Adapting to Noise in Speech Obfuscation by Audio Profiling Using Generative Models for Passive Health Monitoring

Passive health monitoring has been introduced as a solution for continuous diagnosis and tracking of subjects' condition with minimal effort. This is partially achieved by the technology of passive audio recording although it poses major audio privacy issues for subjects. Existing methods are limited to controlled recording environments and their prediction is significantly influenced by background noises. Meanwhile, they are too compute-intensive to be continuously running on smart phones. In this paper, we implement an efficient and robust audio privacy preserving method that profiles the background audio to focus only on audio activities detected during recording for performance improvement, and to adapt to the noise for more accurate speech segmentation. We analyze the performance of our method using audio data collected by a smart watch in lab noisy settings. Our obfuscation results show a low false positive rate of 20% with a 92% true positive rate by adapting to the recording noise level. We also reduced model memory footprint and execution time of the method on a smart phone by 75% and 62% to enable continuous speech obfuscation.

Asthma Management in the Era of Smart-Medicine: Devices, Gadgets, Apps and Telemedicine

Asthma is the most common chronic pediatric condition. Effective asthma management requires a proactive and inclusive approach that controls the patient's symptoms and prevents recurrence of exacerbations. Clinicians should encourage patients to become involved in their management since self-management approaches have proven to be an effective means for chronic illness treatment. Novel forms of self-monitoring and management are technological interventions. In the last decade, novel technology has been developed and used to improve asthma control since it is a powerful agent that addresses a variety of challenges in chronic disease management such as education, communication and adherence. A myriad of technology-based strategies are available although many of these are not evidence based and further studies are needed to evaluate their efficacy in specific asthma-control endpoints. Herein, authors present a review of current and future technology-based options for asthma management and a comparison between them.

Asthmatic Wheeze Detection From Compressively Sensed Respiratory Sound Spectra

Quantification of wheezing by a sensor system consisting of a wearable wireless acoustic sensor and smartphone performing respiratory sound classification may contribute to the diagnosis, long-term control, and lowering treatment costs of asthma. In such battery-powered sensor system, compressive sensing (CS) was verified as a method for simultaneously cutting down power cost of signal acquisition, compression, and communication on the wearable sensor. Matching real-time CS reconstruction algorithms, such as orthogonal matching pursuit (OMP), have been demonstrated on the smartphone. However, their lossy performance limits the accuracy of wheeze detection from CS-recovered short-term Fourier spectra (STFT), when using existing respiratory sound classification algorithms. Thus, here we present a novel, robust algorithm tailored specifically for wheeze detection from the CS-recovered STFT. The proposed algorithm identifies occurrence and tracks multiple individual wheeze frequency lines using hidden Markov model. The algorithm yields 89.34% of sensitivity, 96.28% of specificity, and 94.91% of accuracy on Nyquist-rate sampled respiratory sounds STFT. It enables for less than 2% loss of classification accuracy when operating over STFT reconstructed by OMP, at the signal compression ratio of up to 4 $\times$ (classification from only 25% signal samples). It features execution speed comparable to referent algorithms, and offers good prospects for parallelism.

Rhinitis: adherence to treatment and new technologies

PURPOSE OF REVIEW

Nonadherence to treatment is a major issue in approximately 50% of patients suffering from chronic diseases. The availability of new technologies could represent a possible way to improve patients' engagement and adherence in a real-life setting. Research and technology tools made available or in process of being made available to patients with allergic diseases and their physicians could potentially improve the management of these disease in daily life by improving adherence. In this review, we sought to outline many of the recent advances in these technological approaches.

RECENT FINDINGS

Short Message Service (SMS) reminder, social networks, wearable devices, mobile applications (Apps), monitoring systems of inhaled device use, often presented as 'serious game' are changing the way of approaching to chronic disease, such as rhinitis, management.

SUMMARY

Studies of the role played by various technologies in improving adherence to treatment in rhinitis are still limited as compared with other diseases such as asthma, but the results are encouraging. Further studies in this area may lead to the discovery of novel management approaches that is easy to be integrated in patients' daily life.

Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population

BACKGROUND

Respiratory diseases in children are a common reason for physician visits. A diagnostic difficulty arises when parents hear wheezing that is no longer present during the medical consultation. Thus, an outpatient objective tool for recognition of wheezing is of clinical value.

METHOD

We developed a wheezing recognition algorithm from recorded respiratory sounds with a Smartphone placed near the mouth. A total of 186 recordings were obtained in a pediatric emergency department, mostly in toddlers (mean age 20 months). After exclusion of recordings with artefacts and those with a single clinical operator auscultation, 95 recordings with the agreement of two operators on auscultation diagnosis (27 with wheezing and 68 without) were subjected to a two phase algorithm (signal analysis and pattern classifier using machine learning algorithms) to classify records.

RESULTS

The best performance (71.4% sensitivity and 88.9% specificity) was observed with a Support Vector Machine-based algorithm. We further tested the algorithm over a set of 39 recordings having a single operator and found a fair agreement (kappa=0.28, CI95% [0.12, 0.45]) between the algorithm and the operator.

CONCLUSIONS

The main advantage of such an algorithm is its use in contact-free sound recording, thus valuable in the pediatric population.