Titulo:
Caracterización de sonidos deglutorios adquiridos mediante auscultación cervical en sujetos sanos y con disfagia.
.
Sumario:
La deglución es un acto de alta complejidad neuromuscular debido a que intervienen más de 30 pares musculares y 5 pares craneales en un periodo corto de tiempo. Esta se divide en 4 etapas: pre oral, oral, orofaríngea y esofágica, una alteración en el desarrollo normal de alguna de estas fases puede desarrollar un síntoma secundario a enfermedades neuromusculares y neurogénicas que se conoce como disfagia, esta puede traer consigo muchas dificultades para quien la padece, entre esta neumonía bronquial, desnutrición, deshidratación o incluso la muerte por asfixia. La identificación de características que ayuden a reconocer dicho síntoma, además de describir correctamente el proceso deglutorio, es de gran importancia ya que los métodos existen... Ver más
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2022-06-01
3831 pp. 1
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Revista EIA - 2022
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Caracterización de sonidos deglutorios adquiridos mediante auscultación cervical en sujetos sanos y con disfagia. Characterization of swallowing sounds through Cervical Auscultation in healthy and dysphagic subjects. La deglución es un acto de alta complejidad neuromuscular debido a que intervienen más de 30 pares musculares y 5 pares craneales en un periodo corto de tiempo. Esta se divide en 4 etapas: pre oral, oral, orofaríngea y esofágica, una alteración en el desarrollo normal de alguna de estas fases puede desarrollar un síntoma secundario a enfermedades neuromusculares y neurogénicas que se conoce como disfagia, esta puede traer consigo muchas dificultades para quien la padece, entre esta neumonía bronquial, desnutrición, deshidratación o incluso la muerte por asfixia. La identificación de características que ayuden a reconocer dicho síntoma, además de describir correctamente el proceso deglutorio, es de gran importancia ya que los métodos existentes son invasivos. La auscultación cervical es una técnica mediante la cual se puede obtener información del cierre glótico en el proceso deglutorio por medio de señales de audio, y que puede ser analizada de manera off line. El objetivo de este estudio es evaluar diferentes métodos de caracterización de señales de auscultación cervical y desarrollar un modelo de aprendizaje automático con sonidos de eventos deglutorios segmentados de forma manual para clasificar entre sujetos de control y pacientes con disfagia orofaríngea. Los resultados mostraron, con una exactitud máxima de 75 %, que por medio de señales de auscultación cervical es posible identificar sujetos con disfagia, de igual manera se logró identificar que la potencia media de los segmentos deglutorios fue la característica con mejor rendimiento (curva ROC) y una distribución diferente entre clases según la prueba de U-Mann-Whitney, para discriminar entre sanos y pacientes en diferentes actividades deglutorias. Swallowing is an act of high neuromuscular complexity due to the involvement of more than 30 muscle pairs and 5 cranial pairs that occurs in a short period of time. This activity is divided into 4 stages: pre-oral, oral, oropharyngeal and esophageal, an alteration in the normal development of this process can develop a symptom secondary to neuromuscular and neurogenic diseases that is known as dysphagia. Dysphagia can bring many difficulties for those who suffer from it including bronchial pneumonia, malnutrition, dehydration or even death by asphyxia. The identification of characteristics that help recognize this symptom, in addition to correctly describing the swallowing process is of great importance since the existing methods are invasive. Cervical auscultation is a technique by which information about the gothic closure can be obtained in the swallowing process using audio signals, and which can be analyzed offline. The aim of this study is to evaluate different methods of characterization of cervical auscultation signals and develop a machine learning model with manually segmented swallowing event sounds to classify between control subjects and patients with oropharyngeal dysphagia. The results showed, with a maximum accuracy of 75% that by means of signs is cervical auscultation it is possible to identify dysphageal subjects. In the same way it was possible to identify that the average potency of the swallowing segments was the feature with the best score (ROC curve) and that exists a different distribution between classes in this characteristic according to the U-Mann-whitney test to discriminate between healthy and pathologic subjects during different swallowing activities. Betancur Rengifo, Juan Pablo Restrepo Uribe, Juan Pablo Pérez Giraldo, Estefania Orozco Duque, Andrés Auscultación cervical clasificación disfagia extracción de características procesamiento de señales exactitud rendimiento dificultades invasivos aprendizaje de máquina Cervical auscultation classification dysphagia feature extraction signal processing accuracy score difficulties invasive machine learning 19 38 Núm. 38 , Año 2022 : Tabla de contenido Revista EIA No. 38 Artículo de revista Journal article 2022-06-01 00:00:00 2022-06-01 00:00:00 2022-06-01 application/pdf Fondo Editorial EIA - Universidad EIA Revista EIA 1794-1237 2463-0950 https://revistas.eia.edu.co/index.php/reveia/article/view/1579 10.24050/reia.v19i38.1579 https://doi.org/10.24050/reia.v19i38.1579 spa https://creativecommons.org/licenses/by-nc-nd/4.0 Revista EIA - 2022 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0. 3831 pp. 1 12 A. Sasegbon and S. Hamdy, “The anatomy and physiology of normal and abnormal swallowing in oropharyngeal dysphagia,” Neurogastroenterology and Motility, vol. 29, no. 11, pp. 1–15, 2017, doi: 10.1111/nmo.13100. C. Donohue, S. Mao, E. Sejdić, and J. L. Coyle, “Tracking Hyoid Bone Displacement During Swallowing Without Videofluoroscopy Using Machine Learning of Vibratory Signals,” Dysphagia, vol. 36, no. 2, pp. 259–269, 2021, doi: 10.1007/s00455-020-10124-z. C. Donohue, Y. Khalifa, S. Perera, E. Sejdić, and J. L. Coyle, “A Preliminary Investigation of Whether HRCA Signals Can Differentiate Between Swallows from Healthy People and Swallows from People with Neurodegenerative Diseases,” Dysphagia, vol. 36, no. 4, pp. 635–643, 2021, doi: 10.1007/s00455-020-10177-0. C. Rebrion et al., “High-Resolution Cervical Auscultation Signal Features Reflect Vertical and Horizontal Displacements of the Hyoid Bone during Swallowing,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 7, no. October 2018, 2019, doi: 10.1109/JTEHM.2018.2881468. D. G. Smithard, “Dysphagia : A Geriatric Giant ? The Normal Swallow,” iMedPub Journals, vol. 2, no. 1:5, pp. 1–7, 2016, doi: 10.21767/2471-299X.1000. G. P. Andreu, “Del Envejecimiento,” Rev Cubana Invest Biomed, vol. 22, no. 1, pp. 58–67, 2003. I. Cefac Brasil Bolzan et al., “Contribution of the cervical auscultation in clinical assessment of the oropharyngeal dysphagia,” Revista CEFAC, vol. 15, no. 2, pp. 455–465, 2013, [Online]. Available: http://www.redalyc.org/articulo.oa?id=169326445023 I. Marchesan, “Deglución — Diagnóstico y Posibilidades Terapéuticas,” Espacio logopedico, pp. 1–12, 2002, [Online]. Available: https://s3.amazonaws.com/academia.edu.documents/34860642/deglucion.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1509723251&Signature=%2BUvBMiXkcg84moKyDMSSH1Or7LU%3D&response-content-disposition=inline%3B filename%3DDeglucion.pdf J. M. Dudik, I. Jestrović, B. Luan, J. L. Coyle, and E. Sejdić, “A comparative analysis of swallowing accelerometry and sounds during saliva swallows,” BioMedical Engineering Online, vol. 14, no. 1, pp. 1–15, 2015, doi: 10.1186/1475-925X-14-3. J. Pablo and R. Uribe, “Automatic swallowing analysis based on accelerometry and surface electromyography,” 2021. L. J. Lazareck and Z. M. K. Moussavi, “Classification of normal and dysphagic swallows by acoustical means,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 12, pp. 2103–2112, 2004, doi: 10.1109/TBME.2004.836504. O. Ortega, A. Martín, and P. Clavé, “Diagnosis and Management of Oropharyngeal Dysphagia Among Older Persons, State of the Art,” Journal of the American Medical Directors Association, vol. 18, no. 7, pp. 576–582, 2017, doi: 10.1016/j.jamda.2017.02.015. Q. He et al., “The Association of High Resolution Cervical Auscultation Signal Features With Hyoid Bone Displacement During Swallowing,” IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol. 27, no. 9, pp. 1810–1816, 2019, doi: 10.1109/TNSRE.2019.2935302. S. B. Leder, C. T. Sasaki, and M. I. Burrell, “Fiberoptic endoscopic evaluation of dysphagia to identify silent aspiration,” Dysphagia, vol. 13, no. 1, pp. 19–21, 1998, doi: 10.1007/PL00009544. S. Roldan-Vasco, S. Restrepo-Agudelo, Y. Valencia-Martinez, and A. Orozco-Duque, “Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG,” Journal of Electromyography and Kinesiology, vol. 43, no. October, pp. 193–200, 2018, doi: 10.1016/j.jelekin.2018.10.004. T. Warnecke et al., “Levodopa responsiveness of dysphagia in advanced Parkinson’s disease and reliability testing of the FEES-Levodopa-test,” Parkinsonism and Related Disorders, vol. 28, pp. 100–106, 2016, doi: 10.1016/j.parkreldis.2016.04.034. Y. Khalifa, C. Donohue, J. L. Coyle, and E. Sejdic, “Upper Esophageal Sphincter Opening Segmentation with Convolutional Recurrent Neural Networks in High Resolution Cervical Auscultation,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 2, pp. 493–503, 2021, doi: 10.1109/JBHI.2020.3000057. Y. Khalifa, J. L. Coyle, and E. Sejdić, “Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings,” Scientific Reports, vol. 10, no. 1, pp. 1–13, 2020, doi: 10.1038/s41598-020-65492-1. Y. Sánchez-Cardona, A. Orozco-Duque, and S. Roldán-Vasco, “Characterization and classification of cervical auscultation signals acquired with stethoscope for automatic detection of swallowing sound,” Revista Mexicana de Ingenieria Biomedica, vol. 39, no. 2, pp. 205–216, 2018, doi: 10.17488/RMIB.39.2.6. S. Miyagi, S. Sugiyama, K. Kozawa, S. Moritani, S. I. Sakamoto, and O. Sakai, “Classifying dysphagic swallowing sounds with support vector machines,” Healthcare (Switzerland), vol. 8, no. 2, pp. 1–12, 2020, doi: 10.3390/healthcare8020103. https://revistas.eia.edu.co/index.php/reveia/article/download/1579/1484 info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 http://purl.org/redcol/resource_type/ART info:eu-repo/semantics/publishedVersion http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Text Publication |
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UNIVERSIDAD EIA |
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Colombia |
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Revista EIA |
| title |
Caracterización de sonidos deglutorios adquiridos mediante auscultación cervical en sujetos sanos y con disfagia. |
| spellingShingle |
Caracterización de sonidos deglutorios adquiridos mediante auscultación cervical en sujetos sanos y con disfagia. Betancur Rengifo, Juan Pablo Restrepo Uribe, Juan Pablo Pérez Giraldo, Estefania Orozco Duque, Andrés Auscultación cervical clasificación disfagia extracción de características procesamiento de señales exactitud rendimiento dificultades invasivos aprendizaje de máquina Cervical auscultation classification dysphagia feature extraction signal processing accuracy score difficulties invasive machine learning |
| title_short |
Caracterización de sonidos deglutorios adquiridos mediante auscultación cervical en sujetos sanos y con disfagia. |
| title_full |
Caracterización de sonidos deglutorios adquiridos mediante auscultación cervical en sujetos sanos y con disfagia. |
| title_fullStr |
Caracterización de sonidos deglutorios adquiridos mediante auscultación cervical en sujetos sanos y con disfagia. |
| title_full_unstemmed |
Caracterización de sonidos deglutorios adquiridos mediante auscultación cervical en sujetos sanos y con disfagia. |
| title_sort |
caracterización de sonidos deglutorios adquiridos mediante auscultación cervical en sujetos sanos y con disfagia. |
| title_eng |
Characterization of swallowing sounds through Cervical Auscultation in healthy and dysphagic subjects. |
| description |
La deglución es un acto de alta complejidad neuromuscular debido a que intervienen más de 30 pares musculares y 5 pares craneales en un periodo corto de tiempo. Esta se divide en 4 etapas: pre oral, oral, orofaríngea y esofágica, una alteración en el desarrollo normal de alguna de estas fases puede desarrollar un síntoma secundario a enfermedades neuromusculares y neurogénicas que se conoce como disfagia, esta puede traer consigo muchas dificultades para quien la padece, entre esta neumonía bronquial, desnutrición, deshidratación o incluso la muerte por asfixia. La identificación de características que ayuden a reconocer dicho síntoma, además de describir correctamente el proceso deglutorio, es de gran importancia ya que los métodos existentes son invasivos. La auscultación cervical es una técnica mediante la cual se puede obtener información del cierre glótico en el proceso deglutorio por medio de señales de audio, y que puede ser analizada de manera off line. El objetivo de este estudio es evaluar diferentes métodos de caracterización de señales de auscultación cervical y desarrollar un modelo de aprendizaje automático con sonidos de eventos deglutorios segmentados de forma manual para clasificar entre sujetos de control y pacientes con disfagia orofaríngea. Los resultados mostraron, con una exactitud máxima de 75 %, que por medio de señales de auscultación cervical es posible identificar sujetos con disfagia, de igual manera se logró identificar que la potencia media de los segmentos deglutorios fue la característica con mejor rendimiento (curva ROC) y una distribución diferente entre clases según la prueba de U-Mann-Whitney, para discriminar entre sanos y pacientes en diferentes actividades deglutorias.
|
| description_eng |
Swallowing is an act of high neuromuscular complexity due to the involvement of more than 30 muscle pairs and 5 cranial pairs that occurs in a short period of time. This activity is divided into 4 stages: pre-oral, oral, oropharyngeal and esophageal, an alteration in the normal development of this process can develop a symptom secondary to neuromuscular and neurogenic diseases that is known as dysphagia. Dysphagia can bring many difficulties for those who suffer from it including bronchial pneumonia, malnutrition, dehydration or even death by asphyxia. The identification of characteristics that help recognize this symptom, in addition to correctly describing the swallowing process is of great importance since the existing methods are invasive. Cervical auscultation is a technique by which information about the gothic closure can be obtained in the swallowing process using audio signals, and which can be analyzed offline. The aim of this study is to evaluate different methods of characterization of cervical auscultation signals and develop a machine learning model with manually segmented swallowing event sounds to classify between control subjects and patients with oropharyngeal dysphagia. The results showed, with a maximum accuracy of 75% that by means of signs is cervical auscultation it is possible to identify dysphageal subjects. In the same way it was possible to identify that the average potency of the swallowing segments was the feature with the best score (ROC curve) and that exists a different distribution between classes in this characteristic according to the U-Mann-whitney test to discriminate between healthy and pathologic subjects during different swallowing activities.
|
| author |
Betancur Rengifo, Juan Pablo Restrepo Uribe, Juan Pablo Pérez Giraldo, Estefania Orozco Duque, Andrés |
| author_facet |
Betancur Rengifo, Juan Pablo Restrepo Uribe, Juan Pablo Pérez Giraldo, Estefania Orozco Duque, Andrés |
| topicspa_str_mv |
Auscultación cervical clasificación disfagia extracción de características procesamiento de señales exactitud rendimiento dificultades invasivos aprendizaje de máquina |
| topic |
Auscultación cervical clasificación disfagia extracción de características procesamiento de señales exactitud rendimiento dificultades invasivos aprendizaje de máquina Cervical auscultation classification dysphagia feature extraction signal processing accuracy score difficulties invasive machine learning |
| topic_facet |
Auscultación cervical clasificación disfagia extracción de características procesamiento de señales exactitud rendimiento dificultades invasivos aprendizaje de máquina Cervical auscultation classification dysphagia feature extraction signal processing accuracy score difficulties invasive machine learning |
| citationvolume |
19 |
| citationissue |
38 |
| citationedition |
Núm. 38 , Año 2022 : Tabla de contenido Revista EIA No. 38 |
| publisher |
Fondo Editorial EIA - Universidad EIA |
| ispartofjournal |
Revista EIA |
| source |
https://revistas.eia.edu.co/index.php/reveia/article/view/1579 |
| language |
spa |
| format |
Article |
| rights |
https://creativecommons.org/licenses/by-nc-nd/4.0 Revista EIA - 2022 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0. info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 |
| references |
A. Sasegbon and S. Hamdy, “The anatomy and physiology of normal and abnormal swallowing in oropharyngeal dysphagia,” Neurogastroenterology and Motility, vol. 29, no. 11, pp. 1–15, 2017, doi: 10.1111/nmo.13100. C. Donohue, S. Mao, E. Sejdić, and J. L. Coyle, “Tracking Hyoid Bone Displacement During Swallowing Without Videofluoroscopy Using Machine Learning of Vibratory Signals,” Dysphagia, vol. 36, no. 2, pp. 259–269, 2021, doi: 10.1007/s00455-020-10124-z. C. Donohue, Y. Khalifa, S. Perera, E. Sejdić, and J. L. Coyle, “A Preliminary Investigation of Whether HRCA Signals Can Differentiate Between Swallows from Healthy People and Swallows from People with Neurodegenerative Diseases,” Dysphagia, vol. 36, no. 4, pp. 635–643, 2021, doi: 10.1007/s00455-020-10177-0. C. Rebrion et al., “High-Resolution Cervical Auscultation Signal Features Reflect Vertical and Horizontal Displacements of the Hyoid Bone during Swallowing,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 7, no. October 2018, 2019, doi: 10.1109/JTEHM.2018.2881468. D. G. Smithard, “Dysphagia : A Geriatric Giant ? The Normal Swallow,” iMedPub Journals, vol. 2, no. 1:5, pp. 1–7, 2016, doi: 10.21767/2471-299X.1000. G. P. Andreu, “Del Envejecimiento,” Rev Cubana Invest Biomed, vol. 22, no. 1, pp. 58–67, 2003. I. Cefac Brasil Bolzan et al., “Contribution of the cervical auscultation in clinical assessment of the oropharyngeal dysphagia,” Revista CEFAC, vol. 15, no. 2, pp. 455–465, 2013, [Online]. Available: http://www.redalyc.org/articulo.oa?id=169326445023 I. Marchesan, “Deglución — Diagnóstico y Posibilidades Terapéuticas,” Espacio logopedico, pp. 1–12, 2002, [Online]. Available: https://s3.amazonaws.com/academia.edu.documents/34860642/deglucion.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1509723251&Signature=%2BUvBMiXkcg84moKyDMSSH1Or7LU%3D&response-content-disposition=inline%3B filename%3DDeglucion.pdf J. M. Dudik, I. Jestrović, B. Luan, J. L. Coyle, and E. Sejdić, “A comparative analysis of swallowing accelerometry and sounds during saliva swallows,” BioMedical Engineering Online, vol. 14, no. 1, pp. 1–15, 2015, doi: 10.1186/1475-925X-14-3. J. Pablo and R. Uribe, “Automatic swallowing analysis based on accelerometry and surface electromyography,” 2021. L. J. Lazareck and Z. M. K. Moussavi, “Classification of normal and dysphagic swallows by acoustical means,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 12, pp. 2103–2112, 2004, doi: 10.1109/TBME.2004.836504. O. Ortega, A. Martín, and P. Clavé, “Diagnosis and Management of Oropharyngeal Dysphagia Among Older Persons, State of the Art,” Journal of the American Medical Directors Association, vol. 18, no. 7, pp. 576–582, 2017, doi: 10.1016/j.jamda.2017.02.015. Q. He et al., “The Association of High Resolution Cervical Auscultation Signal Features With Hyoid Bone Displacement During Swallowing,” IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol. 27, no. 9, pp. 1810–1816, 2019, doi: 10.1109/TNSRE.2019.2935302. S. B. Leder, C. T. Sasaki, and M. I. Burrell, “Fiberoptic endoscopic evaluation of dysphagia to identify silent aspiration,” Dysphagia, vol. 13, no. 1, pp. 19–21, 1998, doi: 10.1007/PL00009544. S. Roldan-Vasco, S. Restrepo-Agudelo, Y. Valencia-Martinez, and A. Orozco-Duque, “Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG,” Journal of Electromyography and Kinesiology, vol. 43, no. October, pp. 193–200, 2018, doi: 10.1016/j.jelekin.2018.10.004. T. Warnecke et al., “Levodopa responsiveness of dysphagia in advanced Parkinson’s disease and reliability testing of the FEES-Levodopa-test,” Parkinsonism and Related Disorders, vol. 28, pp. 100–106, 2016, doi: 10.1016/j.parkreldis.2016.04.034. Y. Khalifa, C. Donohue, J. L. Coyle, and E. Sejdic, “Upper Esophageal Sphincter Opening Segmentation with Convolutional Recurrent Neural Networks in High Resolution Cervical Auscultation,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 2, pp. 493–503, 2021, doi: 10.1109/JBHI.2020.3000057. Y. Khalifa, J. L. Coyle, and E. Sejdić, “Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings,” Scientific Reports, vol. 10, no. 1, pp. 1–13, 2020, doi: 10.1038/s41598-020-65492-1. Y. Sánchez-Cardona, A. Orozco-Duque, and S. Roldán-Vasco, “Characterization and classification of cervical auscultation signals acquired with stethoscope for automatic detection of swallowing sound,” Revista Mexicana de Ingenieria Biomedica, vol. 39, no. 2, pp. 205–216, 2018, doi: 10.17488/RMIB.39.2.6. S. Miyagi, S. Sugiyama, K. Kozawa, S. Moritani, S. I. Sakamoto, and O. Sakai, “Classifying dysphagic swallowing sounds with support vector machines,” Healthcare (Switzerland), vol. 8, no. 2, pp. 1–12, 2020, doi: 10.3390/healthcare8020103. |
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