A novel approach for acoustic estimation of neck fluid volume between men and women.

Obstructive Sleep apnea can be caused by fluid shift from the legs to the neck that narrows the upper airway (UA) and contributes to changes in tracheal sound. Tracheal sound is generated from the turbulent airflow in the pharynx and respiratory airways and it has recently been used to estimate increases in neck fluid volume (NFV). However, tracheal sound is also highly variable among people, especially across the sexes.

Snoring sound classification from respiratory signal.

Snoring is common in the general population and the irregularity could lead to the presence of Obstructive sleep apnea. Diagnosis of OSA could therefore be made by snoring sound analysis. However, there is still a shortage of robust methods to automatically detect snoring sounds without the need to calibrate for every individual.

Nonnegative matrix factorization and sparse representation for the automated detection of periodic limb movements in sleep.

Stroke is a leading cause of death and disability in adults, and incurs a significant economic burden to society. Periodic limb movements (PLMs) in sleep are repetitive movements involving the great toe, ankle, and hip. Evolving evidence suggests that PLMs may be associated with high blood pressure and stroke, but this relationship remains underexplored.

A Review of Sleep Disorder Diagnosis by Electromyogram Signal Analysis.

Sleep and sleep-related problems play a role in a large number of human disorders and affect every field of medicine. It is estimated that 50 to 70 million Americans suffer from a chronic sleep disorder, which hinders their daily life, affects their health, and confers a significant economic burden to society. The negative public health consequences of sleep disorders are enormous and could have long-term effects, including increased risk of hypertension, diabetes, obesity, heart attack, stroke and in some cases death.

Non-negative matrix factorization and sparse representation for sleep signal classification.

Real-life signals such as biomedical signals are non-stationary and random in their pattern, and cannot be characterized by any specific waveform or spectral content. Processing of these natural signals involves consideration of certain significant attributes such as their non-stationary behavior over time, scaling behavior, translation invariance. Due to their random behavior, the existing discriminative methods often fail to provide a reasonable quantification performance, thereby resulting in poor classification rates.

Sleep EMG analysis using sparse signal representation and classification.

The development of automatic sleep based abnormality detection in patient for sleep related problem is a key field in the recent research. However the sleep signals are obtained as long-time recordings and inhibit complex characteristics, making their analysis computationally challenging. As a result, recognition methods that facilitate efficient dimensionality reduction are developed to suit different applications.

Analysis of the electromyogram of rapid eye movement sleep using wavelet techniques.

Quantitative electromyographic (EMG) signal analysis in the frequency domain using classical power spectrum analysis techniques has been well documented over the past decade. Yet due to the nature of EMG, frequency analysis cannot be used to approximate a signal whose properties change over time. To address this problem a time varying feature representation has to be analyzed to extract useful information from the signal.