Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients
Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study aimed to investigate the utility of high-resolution cervical auscultation (HRCA) signals in assessing swallowing functionality of patients using feed...
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2024-01-01
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| author | Farnaz Khodami Amanda S. Mahoney James L. Coyle Ervin Sejdic |
| author_facet | Farnaz Khodami Amanda S. Mahoney James L. Coyle Ervin Sejdic |
| author_sort | Farnaz Khodami |
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| description | Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study aimed to investigate the utility of high-resolution cervical auscultation (HRCA) signals in assessing swallowing functionality of patients using feeding tubes. HRCA, capturing swallowing vibratory and acoustic signals, has been explored as a surrogate for videofluoroscopy image analysis in previous research. In this study, we analyzed HRCA signals recorded from patients with NG tubes to identify swallowing kinematic events within this group of subjects. Machine learning architectures from prior research endeavors, originally designed for participants without NG tubes, were fine-tuned to accomplish three tasks in the target population: estimating the duration of upper esophageal sphincter opening, estimating the duration of laryngeal vestibule closure, and tracking the hyoid bone. The convolutional recurrent neural network proposed for the first task predicted the onset of upper esophageal sphincter opening and closure for 67.61% and 82.95% of patients, respectively, with an error margin of fewer than three frames. The hybrid model employed for the second task successfully predicted the onset of laryngeal vestibule closure and reopening for 79.62% and 75.80% of patients, respectively, with the same error margin. The stacked recurrent neural network identified hyoid bone position in test frames, achieving a 41.27% overlap with ground-truth outputs. By applying established algorithms to an unseen population, we demonstrated the utility of HRCA signals for swallowing assessment in individuals with NG tubes and showcased the generalizability of algorithms developed in our previous studies. Clinical impact: This study highlights the promise of HRCA signals for assessing swallowing in patients with NG tubes, potentially improving diagnosis, management, and care integration in both clinical and home healthcare settings. |
| format | Article |
| id | doaj-art-58b88cce34ef40228d947ed1b37c7477 |
| institution | Kabale University |
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| language | English |
| publishDate | 2024-01-01 |
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| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-58b88cce34ef40228d947ed1b37c74772024-11-26T00:00:19ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722024-01-011271172010.1109/JTEHM.2024.349789510752547Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube PatientsFarnaz Khodami0https://orcid.org/0009-0009-8239-2397Amanda S. Mahoney1https://orcid.org/0000-0002-5928-5544James L. Coyle2https://orcid.org/0000-0002-5627-5623Ervin Sejdic3https://orcid.org/0000-0003-4987-8298Department of Electrical and Computer Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, CanadaDepartment of the Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USADepartment of the Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USADepartment of Electrical and Computer Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, CanadaPatients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study aimed to investigate the utility of high-resolution cervical auscultation (HRCA) signals in assessing swallowing functionality of patients using feeding tubes. HRCA, capturing swallowing vibratory and acoustic signals, has been explored as a surrogate for videofluoroscopy image analysis in previous research. In this study, we analyzed HRCA signals recorded from patients with NG tubes to identify swallowing kinematic events within this group of subjects. Machine learning architectures from prior research endeavors, originally designed for participants without NG tubes, were fine-tuned to accomplish three tasks in the target population: estimating the duration of upper esophageal sphincter opening, estimating the duration of laryngeal vestibule closure, and tracking the hyoid bone. The convolutional recurrent neural network proposed for the first task predicted the onset of upper esophageal sphincter opening and closure for 67.61% and 82.95% of patients, respectively, with an error margin of fewer than three frames. The hybrid model employed for the second task successfully predicted the onset of laryngeal vestibule closure and reopening for 79.62% and 75.80% of patients, respectively, with the same error margin. The stacked recurrent neural network identified hyoid bone position in test frames, achieving a 41.27% overlap with ground-truth outputs. By applying established algorithms to an unseen population, we demonstrated the utility of HRCA signals for swallowing assessment in individuals with NG tubes and showcased the generalizability of algorithms developed in our previous studies. Clinical impact: This study highlights the promise of HRCA signals for assessing swallowing in patients with NG tubes, potentially improving diagnosis, management, and care integration in both clinical and home healthcare settings.https://ieeexplore.ieee.org/document/10752547/High-resolution cervical auscultation signalshyoid bone trackinglaryngeal vestibule closuresupper esophageal openingvideofluoroscopic swallowing study |
| spellingShingle | Farnaz Khodami Amanda S. Mahoney James L. Coyle Ervin Sejdic Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients IEEE Journal of Translational Engineering in Health and Medicine High-resolution cervical auscultation signals hyoid bone tracking laryngeal vestibule closures upper esophageal opening videofluoroscopic swallowing study |
| title | Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients |
| title_full | Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients |
| title_fullStr | Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients |
| title_full_unstemmed | Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients |
| title_short | Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients |
| title_sort | elevating patient care with deep learning high resolution cervical auscultation signals for swallowing kinematic analysis in nasogastric tube patients |
| topic | High-resolution cervical auscultation signals hyoid bone tracking laryngeal vestibule closures upper esophageal opening videofluoroscopic swallowing study |
| url | https://ieeexplore.ieee.org/document/10752547/ |
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