Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device.

: Respiratory rate (RR) is a clinical measure of breathing frequency, a vital metric for clinical assessment. However, the recording and documentation of RR are considered to be extremely poor due to the limitations of the current approaches to measuring RR, including capnography and manual counting. We conducted a validation of the automatic RR measurement capability of AcuPebble RE100 (Acurable, London, UK) against a gold-standard capnography system and a type-III cardiorespiratory polygraphy system in two independent prospective and retrospective studies.

A novel computational signal processing framework towards multimodal vital signs extraction using neck-worn wearable devices.

Pulse rate (PR) and respiratory rate (RR) are two of the most important vital signs. Monitoring them would benefit from easy-to-use technologies. Hence, wearable devices would, in principle, be ideal candidates for such systems.

Validation of Tracheal Sound-Based Respiratory Effort Monitoring for Obstructive Sleep Apnoea Diagnosis.

Respiratory effort is considered important in the context of the diagnosis of obstructive sleep apnoea (OSA), as well as other sleep disorders. However, current monitoring techniques can be obtrusive and interfere with a patient's natural sleep. This study examines the reliability of an unobtrusive tracheal sound-based approach to monitor respiratory effort in the context of OSA, using manually marked respiratory inductance plethysmography (RIP) signals as a gold standard for validation.

A comparative study in class imbalance mitigation when working with physiological signals.

Class imbalance is a common challenge that is often faced when dealing with classification tasks aiming to detect medical events that are particularly infrequent. Apnoea is an example of such events. This challenge can however be mitigated using class rebalancing algorithms.

Feature evaluation of accelerometry signals for cough detection.

Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings.

Validation of a Wearable Medical Device for Automatic Diagnosis of OSA against Standard PSG.

Study Objective: The objective of this study was to assess the accuracy of automatic diagnosis of obstructive sleep apnea (OSA) with a new, small, acoustic-based, wearable technology (AcuPebble SA100), by comparing it with standard type 1 polysomnography (PSG) diagnosis.

Material And Methods: This observational, prospective study was carried out in a Spanish hospital sleep apnea center. Consecutive subjects who had been referred to the hospital following primary care suspicion of OSA were recruited and underwent in-laboratory attended PSG, together with the AcuPebble SA100 device simultaneously overnight from January to December 2022.

A Wireless Power Transfer Mattress Based System for Perpetually Operating Physiological Monitoring Wearables.

This article presents a novel wireless power mattress-based system architecture tailored to guarantee continuous energy for in-home environment healthcare wearables intended to be used in the context of patients who would benefit from long-term monitoring of specific physiological biomarkers. The design demonstrates that it is possible to transfer over 20 mW at a primary-secondary distance of 20.7 cm, whilst still keeping within all FCC/ICNIRP safety regulations, using the proposed simplified beamforming-controlled power transfer multi-input single-output system.

Artifacts classification and apnea events detection in neck photoplethysmography signals.

The novel pulse oximetry measurement site of the neck is a promising location for multi-modal physiological monitoring. Specifically, in the context of respiratory monitoring, in which it is important to have direct information about airflow. The neck makes this possible, in contrast to common photoplethysmography (PPG) sensing sites.

Proof-of-concept of a mattress based power harvesting system architecture suitable for wireless physiological monitoring systems.

This work investigates the feasibility of having a mattress based wireless power transfer system with transfer efficiency such that the received power could potentially be enough to fully power up wearable systems intended to provide some level of continuous physiological monitoring; hence eliminating the need for users to ever have to recharge the systems. The novel architecture proposed in this work, to optimise power transfer efficiency against angular misalignment typical of non-static use is based on a non-coupling coil structure combined with a magnetic beamforming scheme. The coil system also incorporates a non-coupling relay array to overcome the significant loss in power transfer efficiency associated to increasing distances between transmitters and receivers.

Automatic Identification of Snoring and Groaning Segments in Acoustic Recordings.

Sleep-related breathing disorders have severe impact on the quality of lives of those suffering from them. These disorders present with a variety of symptoms, out of which snoring and groaning are very common. This paper presents an algorithm to identify and classify segments of acoustic respiratory sound recordings that contain both groaning and snoring events.

Performance Evaluation of Embedded Image Classification Models Using Edge Impulse for Application on Medical Images.

This work explores the possibility of applying edge machine learning technology in the context of portable medical image diagnostic systems. This was done by evaluating the performance of two machine learning (ML) algorithms, that are widely used on medical images, embedding them into a resource-constraint Nordic nrf52840 microcontroller. The first model was based on transfer learning of the MobileNetVI architecture.

Sleep Posture Detection Using an Accelerometer Placed on the Neck.

Sleep position monitoring is key when attempting to address posture triggered sleep disorders. Many studies have explored sleep posture detection from a dedicated physical sensing channel exploiting optimum body locations, such as the torso; or alternatively non-contact approaches. But, little work has been done to try to detect sleep position from a body location which, whilst being suboptimal for that purpose, does however allow for better extraction of more critical biomarkers from other sensing modalities, making possible multi-modal monitoring in certain clinical applications.

A Multilayer Monte Carlo Analysis of Optical Interactions in Reflectance Neck Photoplethysmography.

This paper presents a multilayer Monte Carlo model of a healthy human neck to investigate the light-tissue interaction during different perfusion states within its dermal layer. Whilst there is great interest in advancing wearable technologies for medical applications, and non-invasive techniques like photoplethysmography (PPG) have been studied in detail, research has focused on more conventional body regions like the finger, wrist, and ear. Alternatively, the neck could offer access to additional physiological parameters which other body regions are unsuitable for.

Assessing the Feasibility of Acoustic Based Seizure Detection.

Objective: Long-term monitoring of epilepsy patients outside of hospital settings is impractical due to the complexity and costs associated with electroencephalogram (EEG) systems. Alternative sensing modalities that can acquire, and automatically interpret signals through easy-to-use wearable devices, are needed to help with at-home management of the disease. In this paper, a novel machine learning algorithm is presented for detecting epileptic seizures using acoustic physiological signals acquired from the neck using a wearable device.

Accuracy and usability of AcuPebble SA100 for automated diagnosis of obstructive sleep apnoea in the home environment setting: an evaluation study.

Objectives: Obstructive sleep apnoea (OSA) is a heavily underdiagnosed condition, which can lead to significant multimorbidity. Underdiagnosis is often secondary to limitations in existing diagnostic methods. We conducted a diagnostic accuracy and usability study, to evaluate the efficacy of a novel, low-cost, small, wearable medical device, AcuPebble_SA100, for automated diagnosis of OSA in the home environment.

Effects of denoising strategies on R-wave detection in ECG analysis.

The use of ECG in cardiovascular health monitoring is well established. The signal is collected using specialised equipment, capturing the electrical discharge properties of the human heart. This produces a well-structured signal trace, which can be characterised through its peaks and troughs.

Towards Automatic Identification of Epileptic Recordings in Long-term EEG Monitoring.

Electroencephalogram (EEG) is a crucial tool in the diagnosis and management of epilepsy. The process of analyzing EEG is time consuming leading to the development of seizure detection algorithms to aid its analysis. This approach is limited since it requires seizures to occur during monitoring periods and can often lead to misdiagnosis in cases where seizure occurrence is rare.

Analysis of the factors affecting the adoption and compliance of the NHS COVID-19 mobile application: a national cross-sectional survey in England.

Objectives: To conduct an independent study investigating how adults perceive the usability and functionality of the 'National Health Service (NHS) COVID-19' application (app). This study aims to highlight strengths and provide recommendations to improve adoption of future contact tracing developments.

Design: A 60-item, anonymous online questionnaire, disseminated through social media outlets and email lists by a team from Imperial College London.

Characterization of Artifact Signals in Neck Photoplethysmography.

Objective: The neck is a very attractive measurement location for multimodal physiological monitoring, since it offers the possibility of extracting clinically relevant parameters, which cannot be obtained from other body locations, such as lung volumes. It is for this reason that obtaining PPG from the neck would be of interest. PPG signals, however, are very susceptible to artifacts which greatly compromise their quality.

Extracting the Jugular Venous Pulse from Anterior Neck Contact Photoplethysmography.

The jugular venous pulse (JVP) is the reference physiological signal used to detect right atrial and central venous pressure (CVP) abnormalities in cardio-vascular diseases (CVDs) diagnosis. Invasive central venous line catheterization has always been the gold standard method to extract it reliably. However, due to all the risks it entails, novel non-invasive approaches, exploiting distance cameras and lasers, have recently arisen to measure the JVP at the external and internal jugular veins.

Automatic Cough Detection in Acoustic Signal using Spectral Features.

Cough is a common symptom that manifests in numerous respiratory diseases. In chronic respiratory diseases, such as asthma and COPD, monitoring of cough is an integral part in managing the disease. This paper presents an algorithm for automatic detection of cough events from acoustic signals.

An Approach for Automatic Identification of Fundamental and Additional Sounds from Cardiac Sounds Recordings.

This paper presents an approach for automatic segmentation of cardiac events from non-invasive sounds recordings, without the need of having an auxiliary signal reference. In addition, methods are proposed to subsequently differentiate cardiac events which correspond to normal cardiac cycles, from those which are due to abnormal activity of the heart. The detection of abnormal sounds is based on a model built with parameters which are obtained following feature extraction from those segments that were previously identified as normal fundamental heart sounds.

Heart Rate Extraction from Novel Neck Photoplethysmography Signals.

This paper demonstrates for the first time how heart rate (HR) can be extracted from novel neck photoplethysmography (PPG). A novel algorithm is presented, which when tested in neck PPG signals recorded from 9 subjects at different respiratory rates, obtained good precision with respect to gold standard ECG signals. Mean absolute error (MAE), standard deviation error (SDAE) and root-mean-square error (RMSE) resulted in 1.

A Novel Algorithm for HRV Estimation from Short-Term Acoustic Recordings at Neck.

Heart rate variability (HRV) is an important noninvasive parameter to monitor the activity of the autonomic nervous system. This paper proposes an algorithm to analyze HRV by processing the acoustic data, recorded by placing a small, wearable sensor on the suprasternal notch (at neck) of an adult subject, primarily intended to record breathing sounds. The method used an empirical data analysis approach of the Hilbert-Huang transform (HHT) to construct an instantaneous energy envelope and segment the cardiac cycle by detecting S1 and S2 sounds using the K-means algorithm.

Evaluation of Mel-Frequency Cepstrum for Wheeze Analysis.

Monitoring of wheezes is an integral part of managing Chronic Respiratory Diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD). Recently, there is a growing interest in automatic detection of wheezes and the use of Mel-Frequency Cepstral Coefficients (MFCC) have been shown to achieve encouraging detection performance. While the successful use of MFCC for identifying wheezes has been demonstrated, it is not clear which MFCC coefficients are actually useful for detecting wheezes.