Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea
Abstract Obstructive sleep apnea (OSA) is widespread, under-recognized, and under-treated, impacting the health and quality of life for millions. The current gold standard for sleep apnea testing is based on the in-lab sleep study, which is costly, cumbersome, not readily available and represents a...
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2024-12-01
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| Series: | BMC Neuroscience |
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| Online Access: | https://doi.org/10.1186/s12868-024-00913-9 |
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| author | Ross Mandeville Hooman Sedghamiz Perry Mansfield Geoffrey Sheean Chris Studer Derrick Cordice Ghodsieh Ghanbari Atul Malhotra Shamim Nemati Jejo Koola |
| author_facet | Ross Mandeville Hooman Sedghamiz Perry Mansfield Geoffrey Sheean Chris Studer Derrick Cordice Ghodsieh Ghanbari Atul Malhotra Shamim Nemati Jejo Koola |
| author_sort | Ross Mandeville |
| collection | DOAJ |
| description | Abstract Obstructive sleep apnea (OSA) is widespread, under-recognized, and under-treated, impacting the health and quality of life for millions. The current gold standard for sleep apnea testing is based on the in-lab sleep study, which is costly, cumbersome, not readily available and represents a well-known roadblock to managing this huge societal burden. Assessment of neuromuscular function involved in the upper airway using electromyography (EMG) has shown potential to characterize and diagnose sleep apnea, while the development of transmembranous electromyography (tmEMG), a painless surface probe, has made this opportunity practical and highly feasible. However, experience and ability to interpret electrical signals from the upper airway are scarce, and much of the pertinent information within the signal is likely difficult to detect visually. To overcome this issue, we explored the use of transformers, a deep learning (DL) model architecture with attention mechanisms, to model tmEMG data and distinguish between electromyographic signals from a cohort of control, neurogenic, and sleep apnea patients. Our approach involved three strategies to train a generalizable model on a relatively small dataset including, (1) transfer learning using an audio spectral transformer (AST), (2) the use of 6,000 simulated EMG recordings, converted to spectrograms and using standard backpropagation for fine-tuning, and (3) application of regularization to prevent overfitting and enhance generalizability. This DL approach was tested using 177 transoral EMG recordings from a prior study’s database that included six healthy controls, five moderate to severe OSA patients, and five amyotrophic lateral sclerosis (ALS) patients with evidence of bulbar involvement (neurogenic injury). Sensitivity and specificity for classifying neurogenic cases from controls were 98% and 73%, respectively, while classifying OSA from controls were 88% and 64%, respectively. Notably, by averaging the predicted probabilities of each segment for individual patients, the model correctly classified up to 82% of control and OSA patients. These results not only suggest a potential to diagnose OSA patients accurately, but also to identify OSA endotypes that involve neuromuscular pathology, which has major implications for clinical management, patient outcomes, and research. |
| format | Article |
| id | doaj-art-10847379e27242b4b904989af0eb56e1 |
| institution | Kabale University |
| issn | 1471-2202 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Neuroscience |
| spelling | doaj-art-10847379e27242b4b904989af0eb56e12025-01-05T12:09:37ZengBMCBMC Neuroscience1471-22022024-12-0125111110.1186/s12868-024-00913-9Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apneaRoss Mandeville0Hooman Sedghamiz1Perry Mansfield2Geoffrey Sheean3Chris Studer4Derrick Cordice5Ghodsieh Ghanbari6Atul Malhotra7Shamim Nemati8Jejo Koola9Powell Mansfield, Inc.Powell Mansfield, Inc.Powell Mansfield, Inc.Powell Mansfield, Inc.Powell Mansfield, Inc.Powell Mansfield, Inc.Division of Biomedical Informatics, Department of Medicine, University of California San DiegoDivision of Biomedical Informatics, Department of Medicine, University of California San DiegoDivision of Biomedical Informatics, Department of Medicine, University of California San DiegoPowell Mansfield, Inc.Abstract Obstructive sleep apnea (OSA) is widespread, under-recognized, and under-treated, impacting the health and quality of life for millions. The current gold standard for sleep apnea testing is based on the in-lab sleep study, which is costly, cumbersome, not readily available and represents a well-known roadblock to managing this huge societal burden. Assessment of neuromuscular function involved in the upper airway using electromyography (EMG) has shown potential to characterize and diagnose sleep apnea, while the development of transmembranous electromyography (tmEMG), a painless surface probe, has made this opportunity practical and highly feasible. However, experience and ability to interpret electrical signals from the upper airway are scarce, and much of the pertinent information within the signal is likely difficult to detect visually. To overcome this issue, we explored the use of transformers, a deep learning (DL) model architecture with attention mechanisms, to model tmEMG data and distinguish between electromyographic signals from a cohort of control, neurogenic, and sleep apnea patients. Our approach involved three strategies to train a generalizable model on a relatively small dataset including, (1) transfer learning using an audio spectral transformer (AST), (2) the use of 6,000 simulated EMG recordings, converted to spectrograms and using standard backpropagation for fine-tuning, and (3) application of regularization to prevent overfitting and enhance generalizability. This DL approach was tested using 177 transoral EMG recordings from a prior study’s database that included six healthy controls, five moderate to severe OSA patients, and five amyotrophic lateral sclerosis (ALS) patients with evidence of bulbar involvement (neurogenic injury). Sensitivity and specificity for classifying neurogenic cases from controls were 98% and 73%, respectively, while classifying OSA from controls were 88% and 64%, respectively. Notably, by averaging the predicted probabilities of each segment for individual patients, the model correctly classified up to 82% of control and OSA patients. These results not only suggest a potential to diagnose OSA patients accurately, but also to identify OSA endotypes that involve neuromuscular pathology, which has major implications for clinical management, patient outcomes, and research.https://doi.org/10.1186/s12868-024-00913-9Deep learning in medicineAudio spectral transformerSleep apnea diagnosisQuantitative electromyographyTransmembranous EMG |
| spellingShingle | Ross Mandeville Hooman Sedghamiz Perry Mansfield Geoffrey Sheean Chris Studer Derrick Cordice Ghodsieh Ghanbari Atul Malhotra Shamim Nemati Jejo Koola Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea BMC Neuroscience Deep learning in medicine Audio spectral transformer Sleep apnea diagnosis Quantitative electromyography Transmembranous EMG |
| title | Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea |
| title_full | Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea |
| title_fullStr | Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea |
| title_full_unstemmed | Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea |
| title_short | Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea |
| title_sort | deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea |
| topic | Deep learning in medicine Audio spectral transformer Sleep apnea diagnosis Quantitative electromyography Transmembranous EMG |
| url | https://doi.org/10.1186/s12868-024-00913-9 |
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