Interhemispheric asynchrony of NREM EEG at the beginning and end of sleep describes evening vigilance performance in patients undergoing diagnostic polysomnography.

Obstructive sleep apnea (OSA) is associated with deficits in vigilance. This work explored the temporal patterns of OSA-related events during sleep and vigilance levels measured by the psychomotor vigilance test (PVT) in patients undergoing polysomnography (PSG) for suspected OSA.The PVT was conducted prior to in-laboratory PSG for 80 patients suspected of having OSA.

Association between early stage N2 sleep spindle burst characteristics and vigilance groups: an observational study on patients from a tertiary sleep centre.

Obstructive sleep apnoea (OSA) is associated with impaired vigilance. This paper examines the hypothesis that sleep spindle (Sp) characteristics during nocturnal sleep can be mapped to vigilance deficits measured by the psychomotor vigilance task (PVT) in patients with OSA.The PVT was performed prior to In-laboratory Polysomnography for 250 patients.

A smartphone-based algorithm comprising cough analysis and patient-reported symptoms identifies acute exacerbations of asthma: a prospective, double blind, diagnostic accuracy study.

Early and accurate recognition of asthma exacerbations reduces the duration and risk of hospitalization. Current diagnostic methods depend upon patient recognition of symptoms, expert clinical examination, or measures of lung function. Here, we aimed to develop and test the accuracy of a smartphone-based diagnostic algorithm that analyses five cough events and five patient-reported features (age, fever, acute or productive cough and wheeze) to detect asthma exacerbations.

Diagnostic Errors Are Common in Acute Pediatric Respiratory Disease: A Prospective, Single-Blinded Multicenter Diagnostic Accuracy Study in Australian Emergency Departments.

Diagnostic errors are a global health priority and a common cause of preventable harm. There is limited data available for the prevalence of misdiagnosis in pediatric acute-care settings. Respiratory illnesses, which are particularly challenging to diagnose, are the most frequent reason for presentation to pediatric emergency departments.

Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users.

Diagnosing community-acquired pneumonia via a smartphone-based algorithm: a prospective cohort study in primary and acute-care consultations.

Background: Community-acquired pneumonia (CAP) is an essential consideration in patients presenting to primary care with respiratory symptoms; however, accurate diagnosis is difficult when clinical and radiological examinations are not possible, such as during telehealth consultations.

Aim: To develop and test a smartphone-based algorithm for diagnosing CAP without need for clinical examination or radiological inputs.

Design And Setting: A prospective cohort study using data from participants aged >12 years presenting with acute respiratory symptoms to a hospital in Western Australia.

The diagnosis of respiratory disease in children using a phone-based cough and symptom analysis algorithm: The smartphone recordings of cough sounds 2 (SMARTCOUGH-C 2) trial design.

The diagnosis of acute respiratory diseases in children can be challenging, and no single objective diagnostic test exists for common pediatric respiratory diseases. Previous research has demonstrated that ResAppDx, a cough sound and symptom-based analysis algorithm, can identify common respiratory diseases at the point of care. We present the study protocol for SMARTCOUGH-C 2, a prospective diagnostic accuracy trial of a cough and symptom-based algorithm in a cohort of children presenting with acute respiratory diseases.

Diagnosing Chronic Obstructive Airway Disease on a Smartphone Using Patient-Reported Symptoms and Cough Analysis: Diagnostic Accuracy Study.

Background: Rapid and accurate diagnosis of chronic obstructive pulmonary disease (COPD) is problematic in acute care settings, particularly in the presence of infective comorbidities.

Objective: The aim of this study was to develop a rapid smartphone-based algorithm for the detection of COPD in the presence or absence of acute respiratory infection and evaluate diagnostic accuracy on an independent validation set.

Methods: Participants aged 40 to 75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis, or emphysema were recruited into the study.

Overnight airway obstruction severity prediction centered on acoustic properties of smart phone: validation with esophageal pressure.

Objective: Obstructive sleep apnea is characterized by a number of airway obstructions. Esophageal pressure manometry (EPM) based estimation of consecutive peak to trough differences (ΔPes) is the gold standard method to quantify the severity of airway obstructions. However, the procedure is rarely available in sleep laboratories due to invasive nature.

Polysomnographic risk factors for vigilance-related cognitive decline and obstructive sleep apnea.

Purpose: Cognitive decline (CD) and obstructive sleep apnea (OSA) are often comorbid. Some modifiable risk factors (RF) for CD are also associated with OSA. Diagnostic polysomnography (PSG) measures these RF and may identify at risk patients prior to the onset of CD.

Smart Phone based Snoring Sound analysis to Identify Upper Airway Obstructions.

Obstructive sleep apnea (OSA) is characterized by upper airway obstructions known as apnea/hypopnea events. Narrowing of the upper airway during or near the vicinity of apnea/hypopnea causes the spectrum of the snores to shift to higher frequencies. Using an instrumentation quality wideband (WB) microphone (4Hz-100kHz), we previously demonstrated that potentially diagnostically useful frequency shifts could be detected even in regions beyond the human hearing range.

An Investigation of Critical Frequency Sub-bands of Snoring Sounds for OSA Diagnosis.

Obstructive Sleep Apnea (OSA) is a result of upper airway narrowing during sleep. The upper airway characteristics are likely to manifest in the acoustic characteristics of snoring sounds as snoring is a result of upper airway structure vibrations. In previous studies, researchers have used different regions of the frequency spectrum to diagnose OSA and determine sites of obstruction as well.

Stratifying asthma severity in children using cough sound analytic technology.

Asthma is a common childhood respiratory disorder characterized by wheeze, cough and respiratory distress responsive to bronchodilator therapy. Asthma severity can be determined by subjective, manual scoring systems such as the Pulmonary Score (PS). These systems require significant medical training and expertise to rate clinical findings such as wheeze characteristics, and work of breathing.

A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children.

Background: The differential diagnosis of paediatric respiratory conditions is difficult and suboptimal. Existing diagnostic algorithms are associated with significant error rates, resulting in misdiagnoses, inappropriate use of antibiotics and unacceptable morbidity and mortality. Recent advances in acoustic engineering and artificial intelligence have shown promise in the identification of respiratory conditions based on sound analysis, reducing dependence on diagnostic support services and clinical expertise.

Correction to: Exhaustive mathematical analysis of simple clinical measurements for childhood pneumonia diagnosis.

In the original publication of the article "Declaration of conflict of interest" were not included. The following text is given below.

Corrections to "Automatic Croup Diagnosis Using Cough Sound Recognition".

Presents corrections to shareholder information from this paper, "Automatic croup diagnosis using cough sound recognition," (Sharan, R.V., et al), IEEE Trans.

Corrigendum: Predicting spirometry readings using cough sound features and regression (2018 Physiol. Meas. 39 095001).

The purpose of this submission is to provide missing information to complete the conflict of interest statement associated with the article. The statements provided here augment the already provided information rather than replace it.

Predicting spirometry readings using cough sound features and regression.

Objective: Spirometry is a commonly used method of measuring lung function. It is useful in the definitive diagnosis of diseases such as asthma and chronic obstructive pulmonary disease (COPD). However, spirometry requires cooperative patients, experienced staff, and repeated testing to ensure the consistency of measurements.

Automatic Croup Diagnosis Using Cough Sound Recognition.

Objective: Croup, a respiratory tract infection common in children, causes an inflammation of the upper airway restricting normal breathing and producing cough sounds typically described as seallike "barking cough." Physicians use the existence of barking cough as the defining characteristic of croup. This paper aims to develop automated cough sound analysis methods to objectively diagnose croup.

Snore Sound Analysis Can Detect the Presence of Obstructive Sleep Apnea Specific to NREM or REM Sleep.

Study Objectives: Severities of obstructive sleep apnea (OSA) estimated both for the overall sleep duration and for the time spent in rapid eye movement (REM) and non-rapid eye movement (NREM) sleep are important in managing the disease. The objective of this study is to investigate a method by which snore sounds can be analyzed to detect the presence of OSA in NREM and REM sleep.

Methods: Using bedside microphones, snoring and breathing-related sounds were acquired from 91 patients with OSA (35 females and 56 males) undergoing routine diagnostic polysomnography studies.

Cough sound analysis for diagnosing croup in pediatric patients using biologically inspired features.

This paper aims to diagnose croup in children using cough sound signal classification. It proposes the use of a time-frequency image-based feature, referred as the cochleagram image feature (CIF). Unlike the conventional spectrogram image, the cochleagram utilizes a gammatone filter which models the frequency selectivity property of the human cochlea.

Considerations of handheld respiratory rate estimation via a stabilized Video Magnification approach.

Respiratory rate can be a vital indicator of illness; however, tracking this is a non-trivial process. Phase-based Eulerian Video Magnification (EVM) is an exciting spatiotemporal video processing approach able to reveal subtle breathing motions within video sequences; however, its results are variant to large motions and camera blur. In the case of camera motion, a compensation strategy of stabilizing (without smoothing) the video has the may reduce estimation error in handheld cases.

Automatic picking of snore events from overnight breath sound recordings.

Snoring is one of the earliest symptoms of Obstructive Sleep Apnea (OSA). However, the unavailability of an objective snore definition is a major obstacle in developing automated snore analysis system for OSA screening. The objectives of this paper is to develop a method to identify and extract snore sounds from a continuous sound recording following an objective definition of snore that is independent of snore loudness.

Exhaustive mathematical analysis of simple clinical measurements for childhood pneumonia diagnosis.

Background: Pneumonia is the leading cause of mortality for children below 5 years of age. The majority of these occur in poor countries with limited access to diagnosis. The World Health Organization (WHO) criterion for pneumonia is the de facto method for diagnosis.

Hidden Markov modelling of intra-snore episode behavior of acoustic characteristics of obstructive sleep apnea patients.

Obstructive sleep apnea (OSA) is a breathing disorder that can cause serious medical consequences. It is caused by full (apnea) or partial (hypopnea) obstructions of the upper airway during sleep. The gold standard for diagnosis of OSA is the polysomnography (PSG).