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Respiratory sound analysis for flow estimation during wakefulness and sleep, and its applications for sleep apnea detection and monitoring

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dc.contributor.supervisor Moussavi, Zahra (Electrical and Computer Engineering) en
dc.contributor.author Yadollahi, Azadeh
dc.date.accessioned 2011-04-15T16:36:19Z
dc.date.available 2011-04-15T16:36:19Z
dc.date.issued 2011-04-15T16:36:19Z
dc.identifier.citation A. Yadollahi.,Z. Moussavi, The Effect of Anthropometric Variations on Acoustical Flow Estimation: Proposing a Novel Approach for Flow Estimation Without the Need for Individual Calibration, IEEE Transaction on Biomedical Engineering, EPub, Jan. 2011. en
dc.identifier.citation A. Yadollahi., E. Giannouli, Z. Moussavi, Sleep apnea monitoring and diag- nosis based on tracheal respiratory sounds and pulse oximetery, Medical and Biological Engineering and Computing, Vol. 48, No. 11, pp. 1087-1097, 2010. en
dc.identifier.citation A. Yadollahi, Z. Moussavi. Acoustical Flow Estimation: Review and Validation, IEEE Engineering in Medicine and Biology Magazine, Vol. 26, No. 1, pp. 56-61, January 2007 en
dc.identifier.citation A. Yadollahi., Z. Moussavi, Automatic Breath and Snore Sounds Classification from Tracheal Sounds Recordings, Medical Engineering and Physics, Vol. 32, pp. 985-990,2010 en
dc.identifier.uri http://hdl.handle.net/1993/4590
dc.description.abstract Tracheal respiratory sounds analysis has been investigated as a non-invasive method to estimate respiratory flow and upper airway obstruction. However, the flow-sound relationship is highly variable among subjects which makes it challenging to estimate flow in general applications. Therefore, a robust model for acoustical flow estimation in a large group of individuals did not exist before. On the other hand, a major application of acoustical flow estimation is to detect flow limitations in patients with obstructive sleep apnea (OSA) during sleep. However, previously the flow--sound relationship was only investigated during wakefulness among healthy individuals. Therefore, it was necessary to examine the flow-sound relationship during sleep in OSA patients. This thesis takes the above challenges and offers innovative solutions. First, a modified linear flow-sound model was proposed to estimate respiratory flow from tracheal sounds. To remove the individual based calibration process, the statistical correlation between the model parameters and anthropometric features of 93 healthy volunteers was investigated. The results show that gender, height and smoking are the most significant factors that affect the model parameters. Hence, a general acoustical flow estimation model was proposed for people with similar height and gender. Second, flow-sound relationship during sleep and wakefulness was studied among 13 OSA patients. The results show that during sleep and wakefulness, flow-sound relationship follows a power law, but with different parameters. Therefore, for acoustical flow estimation during sleep, the model parameters should be extracted from sleep data to have small errors. The results confirm reliability of the acoustical flow estimation for investigating flow variations during both sleep and wakefulness. Finally, a new method for sleep apnea detection and monitoring was developed, which only requires recording the tracheal sounds and the blood's oxygen saturation level (SaO2) data. It automatically classifies the sound segments into breath, snore and noise. A weighted average of features extracted from sound segments and SaO2 signal was used to detect apnea and hypopnea events. The performance of the proposed approach was evaluated on the data of 66 patients. The results show high correlation (0.96,p < 0.0001) between the outcomes of our system and those of the polysomnography. Also, sensitivity and specificity of the proposed method in differentiating simple snorers from OSA patients were found to be more than 91%. These results are superior or comparable with the existing commercialized sleep apnea portable monitors. en
dc.format.extent 2684593 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Respiratory sound analysis en
dc.subject Tracheal Respiratory sounds en
dc.subject Snore Sounds en
dc.subject Sleep Apnea en
dc.subject Acoustical flow estimation en
dc.subject Classification en
dc.subject Portable monitoring en
dc.subject Digital signal processing en
dc.subject obstructive sleep apnea en
dc.subject Polysomnography en
dc.title Respiratory sound analysis for flow estimation during wakefulness and sleep, and its applications for sleep apnea detection and monitoring en
dc.degree.discipline Electrical and Computer Engineering en
dc.contributor.examiningcommittee Alfa, Attahiru S (Electrical and Computer Engineering) Fredberg, Jeffery (Harvard School of Public Health) Pawlak, Mirek (Electrical and Computer Engineering) Stephens, Newman (Physiology) en
dc.degree.level Doctor of Philosophy (Ph.D.) en
dc.description.note May 2011 en


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