Acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea
Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that is common worldwide and potentially life-threatening; however, many affected individuals remain undiagnosed and untreated. This research aims to innovate on a simple, cost-saving, and reliable approach to diagnose OSA via the a...
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| Format: | Thesis |
| Language: | English |
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2010
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| Online Access: | https://hdl.handle.net/10356/20868 |
| _version_ | 1826122020909219840 |
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| author | Ng, Andrew Keong |
| author2 | Koh Tong San |
| author_facet | Koh Tong San Ng, Andrew Keong |
| author_sort | Ng, Andrew Keong |
| collection | NTU |
| description | Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that is common worldwide and potentially life-threatening; however, many affected individuals remain undiagnosed and untreated. This research aims to innovate on a simple, cost-saving, and reliable approach to diagnose OSA via the acquisition and analysis of snore signals, with an intention to mass screen for OSA. This thesis attempts to achieve the research aim through: (1) the implementation of a robust and user-friendly acquisition system for snore signals, along with recommendations for measurement standards; (2) the development of an advanced wavelet-driven preprocessing system that efficiently integrates both snore signal enhancement and snore activity detection; (3) the identification of effective snore-based OSA diagnostic markers, including formant frequencies (82.5–100% sensitivity, 82.0–95.0% specificity), wavelet bicoherence peaks (82.5–100% sensitivity, 83.3–100% specificity), and psychoacoustic metrics (72.0–78.0% sensitivity, 91.2–92.0% specificity), which accurately classify apneic and benign snores in same- and both-gender patient groups (p-value < 0.0001); (4) the formulation of regression models that are indicative of OSA severity; (5) the investigation of physiological-anatomical-acoustical relationships of snores via source-filter modeling; and (6) the successful generation of natural-sounding synthetic snores using a novel snore source flow model. |
| first_indexed | 2024-10-01T05:41:40Z |
| format | Thesis |
| id | ntu-10356/20868 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:41:40Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/208682023-07-04T16:46:56Z Acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea Ng, Andrew Keong Koh Tong San School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that is common worldwide and potentially life-threatening; however, many affected individuals remain undiagnosed and untreated. This research aims to innovate on a simple, cost-saving, and reliable approach to diagnose OSA via the acquisition and analysis of snore signals, with an intention to mass screen for OSA. This thesis attempts to achieve the research aim through: (1) the implementation of a robust and user-friendly acquisition system for snore signals, along with recommendations for measurement standards; (2) the development of an advanced wavelet-driven preprocessing system that efficiently integrates both snore signal enhancement and snore activity detection; (3) the identification of effective snore-based OSA diagnostic markers, including formant frequencies (82.5–100% sensitivity, 82.0–95.0% specificity), wavelet bicoherence peaks (82.5–100% sensitivity, 83.3–100% specificity), and psychoacoustic metrics (72.0–78.0% sensitivity, 91.2–92.0% specificity), which accurately classify apneic and benign snores in same- and both-gender patient groups (p-value < 0.0001); (4) the formulation of regression models that are indicative of OSA severity; (5) the investigation of physiological-anatomical-acoustical relationships of snores via source-filter modeling; and (6) the successful generation of natural-sounding synthetic snores using a novel snore source flow model. DOCTOR OF PHILOSOPHY (EEE) 2010-02-18T04:23:54Z 2010-02-18T04:23:54Z 2010 2010 Thesis Ng, A. K. (2010). Acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/20868 10.32657/10356/20868 en 253 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics Ng, Andrew Keong Acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea |
| title | Acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea |
| title_full | Acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea |
| title_fullStr | Acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea |
| title_full_unstemmed | Acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea |
| title_short | Acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea |
| title_sort | acquisition and analysis of snore signals for diagnosis of obstructive sleep apnea |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics |
| url | https://hdl.handle.net/10356/20868 |
| work_keys_str_mv | AT ngandrewkeong acquisitionandanalysisofsnoresignalsfordiagnosisofobstructivesleepapnea |