Detecting Breathing and Snoring Episodes Using a Wireless Tracheal Sensor-A Feasibility Study

Automatic Heart Rate Detection during Sleep Using Tracheal Audio Recordings from Wireless Acoustic Sensor

BACKGROUND

Heart rate is an essential diagnostic parameter indicating a patient's condition. The assessment of heart rate is also a crucial parameter in the diagnostics of various sleep disorders, including sleep apnoea, as well as sleep/wake pattern analysis. It is usually measured using an electrocardiograph (ECG)-a device monitoring the electrical activity of the heart using several electrodes attached to a patient's upper body-or photoplethysmography (PPG).

METHODS

The following paper investigates an alternative method for heart rate detection and monitoring that operates on tracheal audio recordings. Datasets for this research were obtained from six participants along with ECG Holter (for validation), as well as from fifty participants undergoing a full night polysomnography testing, during which both heart rate measurements and audio recordings were acquired.

RESULTS

The presented method implements a digital filtering and peak detection algorithm applied to audio recordings obtained with a wireless sensor using a contact microphone attached in the suprasternal notch. The system was validated using ECG Holter data, achieving over 92% accuracy. Furthermore, the proposed algorithm was evaluated against whole-night polysomnography-derived HR using Bland-Altman's plots and Pearson's Correlation Coefficient, reaching the average of 0.82 (0.93 maximum) with 0 BPM error tolerance and 0.89 (0.97 maximum) at ±3 BPM.

CONCLUSIONS

The results prove that the proposed system serves the purpose of a precise heart rate monitoring tool that can conveniently assess HR during sleep as a part of a home-based sleep disorder diagnostics process.

ERS technical standards for using type III devices (limited channel studies) in the diagnosis of sleep disordered breathing in adults and children

For more than three decades, type III devices have been used in the diagnosis of sleep disordered breathing in supervised as well as unsupervised settings. They have satisfactory positive and negative predictive values for detecting obstructive and central sleep apnoea in populations with moderately high pre-test probability of symptoms associated with these events. However, standardisation of commercially available type III devices has never been undertaken and the technical specifications can vary widely. None have been subjected to the same rigorous processes as most other diagnostic modalities in the medical field. Although type III devices do not include acquisition of electroencephalographic signals overnight, the minimum number of physical sensors required to allow for respiratory event scoring using standards outlined by the American Academy of Sleep Medicine remains debatable. This technical standard summarises data on type III studies published since 2007 from multiple perspectives in both adult and paediatric sleep practice. Most importantly, it aims to provide a framework for considering current type III device limitations in the diagnosis of sleep disordered breathing while raising research- and practice-related questions aimed at improving our use of these devices in the present and future.

Supine sleep patterns as a part of phenotyping patients with sleep apnea-a pilot study

Purpose

Polysomnography (PSG) is considered the best objective study to diagnose and quantify sleep disorders. However, PSG involves multiple electrodes and is usually performed in a sleep laboratory that in itself may change the physiology of sleep. One of the parameters that can change during PSG is the sleep position, leading to more supine sleep. The aim of this study was to quantify the amount of supine sleep during PSG and compare it to consecutive nights of a home sleep apnea test (HSAT) in the same patients.

Methods

This prospective study evaluated 22 consecutive patients undergoing PSG followed by HSAT. Sleep position was analyzed during PSG and subsequently on 2 to 6 nights (mean 3.7 nights) at home, and the amount of supine sleep was recorded during each night.

Results

Of 22 patients, there were 12 men (55%). The median age was 60.0 years for women and 45.5 years for men. Median proportion of supine sleep during PSG and HSAT was 61% and 26% (p < 0.001), respectively. Four “phenotypes” were identified according to their sleep position during PSG and HSAT, with 5 patients sleeping mainly supine during all nights, 7 patients sleeping mainly non-supine during all nights, 3 patients sleeping in different positions during each night, and 7 patients sleeping supine during PSG but non-supine at home, during HSAT.

Conclusions

There is a higher proportion of supine sleep during PSG compared to home sleep. We identified a subgroup of patients who slept mainly supine during PSG and mainly non-supine during HSAT. PSG may overestimate OSA severity in a specific phenotype of patients.

Tracheal Sound Analysis

Tracheal sound sensors provide multiple respiratory signals that are valuable for studying upper airway characteristics. This chapter reviews the original work and ongoing research on tracheal sound analysis in relation to upper airway obstruction during sleep. Past and current research suggest that being associated with other sleep study recording sensors and advanced signal processing techniques, tracheal sound analysis can extensively contribute to the diagnosis and assessment of sleep-disordered breathing.

Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey

Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

Detection of Volatile Compounds Emitted by Bacteria in Wounds Using Gas Sensors

In this paper we analyze an experiment for the use of low-cost gas sensors intended to detect bacteria in wounds using a non-intrusive technique. Seven different genera/species of microbes tend to be present in most wound infections. Detection of these bacteria usually requires sample and laboratory testing which is costly, inconvenient and time-consuming. The validation processes for these sensors with nineteen types of microbes (1 Candida, 2 Enterococcus, 6 Staphylococcus, 1 Aeromonas, 1 Micrococcus, 2 E. coli and 6 Pseudomonas) are presented here, in which four sensors were evaluated: TGS-826 used for ammonia and amines, MQ-3 used for alcohol detection, MQ-135 for CO₂ and MQ-138 for acetone detection. Validation was undertaken by studying the behavior of the sensors at different distances and gas concentrations. Preliminary results with liquid cultures of 10⁸ CFU/mL and solid cultures of 10⁸ CFU/cm² of the 6 Pseudomonas aeruginosa strains revealed that the four gas sensors showed a response at a height of 5 mm. The ammonia detection response of the TGS-826 to Pseudomonas showed the highest responses for the experimental samples over the background signals, with a difference between the values of up to 60 units in the solid samples and the most consistent and constant values. This could suggest that this sensor is a good detector of Pseudomonas aeruginosa, and the recording made of its values could be indicative of the detection of this species. All the species revealed similar CO₂ emission and a high response rate with acetone for Micrococcus, Aeromonas and Staphylococcus.

Sensor, Signal, and Imaging Informatics in 2017

Objective:  To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.

Methods:  PubMed ^® and Web of Science ^® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.

Results:  The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.

Conclusion:  The growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics.

The use of tracheal sounds for the diagnosis of sleep apnoea

Tracheal sounds have been the subject of many research studies. In this review, we describe the state of the art, original work relevant to upper airways obstruction during sleep, and ongoing research concerning the methods used when analysing tracheal sounds. Tracheal sound sensors are a simple and noninvasive means of measurement and are more reliable than other breathing sensors. Developments in acoustic processing techniques and enhancements in tracheal sound signals over the past decade have led to improvements in the accuracy and clinical relevance of diagnoses based on this technology. Past and current research suggests that they may have a significant role in the diagnosis of obstructive sleep apnoea.

Tracheal sounds analysis may have a significant role in the diagnosis of obstructive sleep apnoeahttp://ow.ly/f7ax30cAcnP