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.

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.

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.

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.

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).

Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis.

Pneumonia is the cause of death for over a million children each year around the world, largely in resource poor regions such as sub-Saharan Africa and remote Asia. One of the biggest challenges faced by pneumonia endemic countries is the absence of a field deployable diagnostic tool that is rapid, low-cost and accurate. In this paper, we address this issue and propose a method to screen pneumonia based on the mathematical analysis of cough sounds.

Signal shape feature for automatic snore and breathing sounds classification.

Snore analysis techniques have recently been developed for sleep studies. Most snore analysis techniques require reliable methods for the automatic classification of snore and breathing sounds in the sound recording. In this study we focus on this problem and propose an automated method to classify snore and breathing sounds based on the novel feature, 'positive/negative amplitude ratio (PNAR)', to measure the shape of the sound signal.

HMM-based snorer group recognition for Sleep Apnea diagnosis.

This paper presents an Hidden Markov Models (HMM)-based snorer group recognition approach for Obstructive Sleep Apenea diagnosis. It models the spatio-temporal characteristics of different snorer groups belonging to different genders and AHI severity levels. The current experiment includes selecting snore data from subjects, identifying snorer groups based on gender and AHI values (AHI < 15 and AHI > 15), detecting snore episodes, MFCC computation, training and testing HMMs.

ARMA-based spectral bandwidth for evaluation of bowel motility by the analysis of bowel sounds.

Approximately 10%-20% of adults and adolescents suffer from irritable bowel syndrome (IBS) worldwide. IBS is characterized by chronic gastrointestinal dysfunction which may reflect in altered motility. Currently, the diagnosis of IBS is made through expensive invasive radiographic and endoscopic examinations.

Cough sound analysis can rapidly diagnose childhood pneumonia.

Pneumonia annually kills over 1,800,000 children throughout the world. The vast majority of these deaths occur in resource poor regions such as the sub-Saharan Africa and remote Asia. Prompt diagnosis and proper treatment are essential to prevent these unnecessary deaths.

Automatic identification of wet and dry cough in pediatric patients with respiratory diseases.

Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis especially in children.

Artificial neural networks for breathing and snoring episode detection in sleep sounds.

Obstructive sleep apnea (OSA) is a serious disorder characterized by intermittent events of upper airway collapse during sleep. Snoring is the most common nocturnal symptom of OSA. Almost all OSA patients snore, but not all snorers have the disease.

Multi-feature snore sound analysis in obstructive sleep apnea-hypopnea syndrome.

Snoring is the most common symptom of obstructive sleep apnea hypopnea syndrome (OSAHS), which is a serious disease with high community prevalence. The standard method of OSAHS diagnosis, known as polysomnography (PSG), is expensive and time consuming. There is evidence suggesting that snore-related sounds (SRS) carry sufficient information to diagnose OSAHS.

Interhemispheric asynchrony correlates with severity of respiratory disturbance index in patients with sleep apnea.

Obstructive sleep apnea (OSA) hypopnea syndrome is a disorder characterized by airway obstructions during sleep; full obstructions are known as apnea and partial obstructions are called hypopnea. Sleep in OSA patients is significantly disturbed with frequent apnea/hypopnea and arousal events. We illustrate that these events lead to functional asymmetry of the brain as manifested by the interhemispheric asynchrony (IHA) computed using EEG recorded on the scalp.

Nonlinear features for single-channel diagnosis of sleep-disordered breathing diseases.

Studies have shown that algorithms based on single-channel airflow records are effective in screening for sleep-disordered breathing diseases (SDB). In this study, we investigate the diagnostic effectiveness of a classifier trained on a set of features derived from single-channel airflow measurements. The features considered are based on recurrence quantification analysis (RQA) of the measurement time series and are optionally augmented with single measurements of neck circumference and body mass index.

A state transition-based method for quantifying EEG sleep fragmentation.

Sleep fragmentation is the predominant factor causing excessive daytime sleepiness in diseases such as sleep apnea and periodic leg movement syndrome. The reference standard for quantifying sleep fragmentation is the arousal index (ArI), which is defined as the average number of arousals per hour of sleep. Arousal scoring is tedious and subjective resulting in considerable inter- and intra-rater variability.

Investigation of obstructive sleep apnea using nonlinear mode interactions in nonstationary snore signals.

Acoustic studies on snoring sounds have recently drawn attention as a potential alternative to polysomnography in the diagnosis of obstructive sleep apnea (OSA). This paper investigates the feasibility of using nonlinear coupling between frequency modes in snore signals via wavelet bicoherence (WBC) analysis for screening of OSA. Two novel markers (PF1 and PSF), which are frequency modes with high nonlinear coupling strength in their respective WBC spectrum, are proposed to differentiate between apneic and benign snores in same- or both-gender snorers.

Silence-breathing-snore classification from snore-related sounds.

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. The first task in the automatic analysis of snore-related sounds (SRS) is to segment the SRS data as accurately as possible into three main classes: snoring (voiced non-silence), breathing (unvoiced non-silence) and silence.

Localization of an inert region in the brain using modified Levenberg Marquarts neural network.

We tested the localization accuracy of electroencephalograph (EEG) for an inert region in a simulation at sizes ranging from 1 to 8 cm at 1 cm intervals. We used international 10-20 system electrodes placements and three concentric shell model to calculate forward problems. From using the data, neural network could be used to solve inverse problems.