Entity

Time filter

Source Type


Azarbarzin A.,University of Manitoba | Moussavi Z.,University of Manitoba | Moussavi Z.,Telecommunication Research Labs TRLabs
Medical and Biological Engineering and Computing | Year: 2013

In a multidimensional feature space, the snoring sounds can extend from a very compact cluster to highly distinct clusters. In this study, we investigated the cause of snoring sound's variation within the snorers. It is known that a change in body position and sleep stage can affect snoring during sleep but it is unclear whether positional, sleep state, and blood oxygen level variations cause the snoring sounds to have different characteristics, and if it does how significant that effect would be. We extracted 12 characteristic features from snoring sound segments of 57 snorers and transformed them into a 4-D feature space using principal component analysis (PCA). Then, they were grouped based on the body position (side, supine, and prone), sleep stage (NREM, REM, and Arousal), and blood oxygen level (Normal and Desaturation). The probability density function of the transformed features was calculated for each class of categorical variables. The distance between the class-densities were calculated to determine which of these parameters affects the snoring sounds significantly. Analysis of Variance (ANOVA) was run for each categorical variable. The results show that the positional change has the highest effect on the snoring sounds; it results in forming distinct clusters of snoring sounds. Also, sleep state and blood oxygen level variation have been found to moderately affect the snoring sounds. © 2012 International Federation for Medical and Biological Engineering. Source


Yadollahi A.,University of Manitoba | Yadollahi A.,Telecommunication Research Labs TRLabs | Moussavi Z.,University of Manitoba | Moussavi Z.,Telecommunication Research Labs TRLabs
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2011

Tracheal respiratory sound analysis is a simple, inexpensive and non-invasive way to study the pathology of the upper airways. Recently, it has attracted considerable attention for acoustical flow estimation and investigation of obstruction in the upper airways. Obstructive sleep apena (OSA) is characterized by periods of reduction or complete cessation of airflow during sleep. However, the flow-sound relationship is highly variable among OSA and non-OSA individuals; it also changes for the same person at different body postures and during wake and sleep. In this study we recorded respiratory sound and flow from 93 non-OSA individuals as well as 13 OSA patients during sleep and wake. We investigated the statistical correlation between the flow-sound model parameters and anthropometric features in the non-OSA group. The results have shown that gender, height and smoking are the most significant factors that affect the model parameters. We compared the flow-sound relationship in OSA and non-OSA groups in the sitting position while awake. We also examined the variations in the model parameters in OSA patients during sleep and wake in the recumbent position. The results show that the model parameters are different in the two groups even when accounted for height, gender and position. In OSA group, the model parameters change from wake to sleep, even at the same position. The variations in the model parameters can be used to investigate the characteristics of upper airways and examine the factors that can lead to the upper airways obstruction during sleep. © 2011 IEEE. Source


Yadollahi A.,University of Manitoba | Yadollahi A.,Telecommunication Research Labs TRLabs | Moussavi Z.,University of Manitoba | Moussavi Z.,Telecommunication Research Labs TRLabs
Medical Engineering and Physics | Year: 2010

In this study respiratory sound signals were recorded from 23 patients suspect of obstructive sleep apnea, who were referred for the full-night sleep lab study. The sounds were recorded with two microphones simultaneously: one placed over trachea and one hung in the air in the vicinity of the patient. During recording the sound signals, patients' Polysomnography (PSG) data were also recorded simultaneously. An automatic method was developed to classify breath and snore sound segments based on their energy, zero crossing rate and formants of the sound signals. For every sound segment, the number of zero crossings, logarithm of the signal's energy and the first formant were calculated. Fischer Linear Discriminant was implemented to transform the 3-dimensional (3D) feature set to a 1-dimensional (1D) space and the Bayesian threshold was applied on the transformed features to classify the sound segments into either snore or breath classes. Three sets of experiments were implemented to investigate the method's performance for different training and test data sets extracted from different neck positions. The overall accuracy of all experiments for tracheal recordings were found to be more than 90% in classifying breath and snore sounds segments regardless of the neck position. This implies the method's accuracy is insensitive to patient's position; hence, simplifying data analysis for an entire night recording. The classification was also performed on sounds signals recorded simultaneously with an ambient microphone and the results were compared with those of the tracheal recording. © 2010. Source


Yadollahi A.,University of Manitoba | Yadollahi A.,Telecommunication Research Labs TRLabs | Giannouli E.,University of Manitoba | Moussavi Z.,University of Manitoba | Moussavi Z.,Telecommunication Research Labs TRLabs
Medical and Biological Engineering and Computing | Year: 2010

Sleep apnea is a common respiratory disorder during sleep, which is described as a cessation of airflow to the lungs that lasts at least for 10 s and is associated with at least 4% drop in blood's oxygen saturation level (SaO2). The current gold standard method for sleep apnea assessment is full-night polysomnography (PSG). However, its high cost, inconvenience for patients, and immobility have persuaded researchers to seek simple and portable devices to detect sleep apnea. In this article, we report on developing a new method for sleep apnea detection and monitoring, which only requires two data channels: tracheal breathing sounds and the pulse oximetery (SaO2 signal). It includes an automated method that uses the energy of breathing sounds signals to segment the signals into sound and silent segments. Then, the sound segments are classified into breath, snore, and noise segments. The SaO2 signal is analyzed automatically to find its rises and drops. Finally, a weighted average of different features extracted from breath segments, snore segments and SaO2 signal are used to detect apnea and hypopnea events. The performance of the proposed approach was evaluated on the data of 66 patients recorded simultaneously with their full-night PSG study, and the results were compared with those of the PSG. The results show high correlation (0.96, P < 0.0001) between the outcomes of our system and those of the PSG. Also, the proposed method has been found to have sensitivity and specificity values of more than 91% in differentiating simple snorers from obstructive sleep apnea patients. © 2010 International Federation for Medical and Biological Engineering. Source

Discover hidden collaborations