Digital processing of speech signals

https://escholarship.org/content/qt54j7t9kw/qt54j7t9kw.pdf?t=oo8rgf

Sleep Apnea: Types, Mechanisms, and Clinical Cardiovascular Consequences

Principles of exercise testing and interpretation

Importance Obstructive sleep apnea (OSA) affects 17% of women and 34% of men in the US and has a similar prevalence in other countries. This review provides an update on the diagnosis and treatment of OSA. Observations The most common presenting symptom of OSA is excessive sleepiness, although this symptom is reported by as few as 15% to 50% of people with OSA in the general population. OSA is associated with a 2- to 3-fold increased risk of cardiovascular and metabolic disease. In many patients, OSA can be diagnosed with home sleep apnea testing, which has a sensitivity of approximately 80%. Effective treatments include weight loss and exercise, positive airway pressure, oral appliances that hold the jaw forward during sleep, and surgical modification of the pharyngeal soft tissues or facial skeleton to enlarge the upper airway. Hypoglossal nerve stimulation is effective in select patients with a body mass index less than 32. There are currently no effective pharmacological therapies. Treatment with positive airway pressure lowers blood pressure, especially in patients with resistant hypertension; however, randomized clinical trials of OSA treatment have not demonstrated significant benefit on rates of cardiovascular or cerebrovascular events. Conclusions and Relevance OSA is common and the prevalence is increasing with the increased prevalence of obesity. Daytime sleepiness is among the most common symptoms, but many patients with OSA are asymptomatic. Patients with OSA who are asymptomatic, or whose symptoms are minimally bothersome and pose no apparent risk to driving safety, can be treated with behavioral measures, such as weight loss and exercise. Interventions such as positive airway pressure are recommended for those with excessive sleepiness and resistant hypertension. Managing asymptomatic OSA to reduce cardiovascular and cerebrovascular events is not currently supported by high-quality evidence.

https://jamanetwork.com/journals/jama/articlepdf/2764461/jama_gottlieb_2020_rv_200002.pdf

Diagnosis and Management of Obstructive Sleep Apnea: A Review

In this guideline on the diagnosis of obstructive sleep apnea in adults, the American College of Physicians recommends a sleep study for patients with unexplained daytime sleepiness, polysomnograph...

http://rossleep.ru/wp-content/uploads/2014/08/Rekomendatsii-ACH.pdf

Management of Obstructive Sleep Apnea in Adults: A Clinical Practice Guideline From the American College of Physicians

Detection of apnea/hypopnea events to calculate an apnea/hypopnea index is obtained by analysis of breathing pattern of a patient from breathing and snore sounds and a finger probe recording the SaO2 signal. A detector analyzes microphone signals to detect breath, snore and noise sounds in response to a detected drop in the SaO2 level greater than 2% and to extract and analyze the breathing sounds from a limited time period starting prior to the drop of the SaO2 signal and ending at least at the end of each drop. Separated time periods are divided phases with snore sounds and those with breathing sounds and an estimated breathing volume adjacent to a snore phase is used to estimate the airflow of the snore phase. The relative and absolute energy and duration of the sound periods is used to classify the sound periods into the three groups of breath, snore and noise.

Sleep apnea monitoring and diagnosis based on pulse oximetery and tracheal sound signals

Apparatus for use detection of apnea includes a microphone mounted in the ear of the patient for detecting breathing sounds and a second external microphone together with an oximetric sensor. A transmitter at the patient compresses and transmits the signals to a remote location where there is provided a detector module for receiving and analyzing the signals to extract data relating to the breathing. The detector uses the entropy or range of the signal to generate an estimate of air flow while extracting extraneous snoring and heart sounds and to analyze the estimate of air flow using Otsu's threshold to detect periods of apnea and/or hypopnea. A display provides data of the detected apnea/hypopnea episodes and related information for a clinician.

Breathing sound analysis for detection of sleep apnea/popnea events

The relationship between respiratory sounds and flow is of great interest for researchers and physicians due to its diagnostic potentials. Due to difficulties and inaccuracy of most of the flow measurement techniques, several researchers have attempted to estimate flow from respiratory sounds. However, all of the proposed methods heavily depend on the availability of different rates of flow for calibrating the model, which makes their use limited by a large degree. In this paper, a robust and novel method for estimating flow using entropy of the band pass filtered tracheal sounds is proposed. The proposed method is novel in terms of being independent of the flow rate chosen for calibration; it requires only one breath for calibration and can estimate any flow rate even out of the range of calibration flow. After removing the effects of heart sounds (which distort the low-frequency components of tracheal sounds) on the calculated entropy of the tracheal sounds, the performance of the method at different frequency ranges were investigated. Also, the performance of the proposed method was tested using 6 different segment sizes for entropy calculation and the best segment sizes during inspiration and expiration were found. The method was tested on data of 10 healthy subjects at five different flow rates. The overall estimation error was found to be 8.3 /spl plusmn/ 2.8% and 9.6 /spl plusmn/ 2.8% for inspiration and expiration phases, respectively.

A robust method for estimating respiratory flow using tracheal sounds entropy

Overnight fluid shift from the legs to the neck and lungs may contribute to the pathogenesis of obstructive sleep apnoea (OSA) and central sleep apnoea (CSA). We hypothesised that exercise training will decrease the severity of OSA and CSA in patients with coronary artery disease (CAD) by decreasing daytime leg fluid accumulation and overnight rostral fluid shift. Patients with CAD and OSA or CSA (apnoea–hypopnoea index >15 events per h) were randomised to 4 weeks of aerobic exercise training or to a control group. Polysomnography, with measurement of leg, thoracic and neck fluid volumes and upper-airway cross-sectional area (UA-XSA) before and after sleep, was performed at baseline and follow-up. 17 patients per group completed the study. Apnoea–hypopnoea index decreased significantly more in the exercise group than in the control group (31.1±12.9 to 20.5±9.4 versus 28.1±13.5 to 27.0±15.1 events per h, p=0.047), in association with a greater reduction in the overnight change in leg fluid volume (579±222 to 466±163 versus 453±164 to 434±141 mL, p=0.04) and by a significantly greater increase in the overnight change in UA-XSA in the exercise group (p=0.04). In patients with CAD and sleep apnoea, exercise training decreases sleep apnoea severity via attenuation of overnight fluid shift and an increase in UA-XSA.

https://erj.ersjournals.com/content/erj/48/1/142.full.pdf

Effects of exercise training on sleep apnoea in patients with coronary artery disease: a randomised trial.

Heart sounds are the main unavoidable interference in lung sound recording and analysis. Hence, several techniques have been developed to reduce or cancel heart sounds (HS) from lung sound records. The first step in most HS cancellation techniques is to detect the segments including HS. This paper proposes a novel method for HS localization using entropy of the lung sounds. We investigated both Shannon and Renyi entropies and the results of the method using Shannon entropy were superior. Another HS localization method based on multiresolution product of lung sounds wavelet coefficients adopted from was also implemented for comparison. The methods were tested on data from 6 healthy subjects recorded at low (7.5 ml/s/kg) and medium (15 ml/s/kg) flow rates. The error of entropy-based method using Shannon entropy was found to be 0.1 /spl plusmn/ 0.4% and 1.0 /spl plusmn/ 0.7% at low and medium flow rates, respectively, which is significantly lower than that of multiresolution product method and those of other methods reported in previous studies. The proposed method is fully automated and detects HS included segments in a completely unsupervised manner.

Azadeh Yadollahi

Papers

Sleep apnea monitoring and diagnosis based on pulse oximetery and tracheal sound signals

Breathing sound analysis for detection of sleep apnea/popnea events

A robust method for estimating respiratory flow using tracheal sounds entropy

Effects of exercise training on sleep apnoea in patients with coronary artery disease: a randomised trial.

A robust method for heart sounds localization using lung sounds entropy