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2014, Número 1

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Rev Mex Ing Biomed 2014; 35 (1)


Análisis Multicanal de un Sensor no Obstructivo para la Detección del Síndrome de Apnea-Hipopnea del Sueño

Guerrero-Mora G, Palacios-Hernández E, Kortelainen JM, Bianchi AM, Méndez MO

Idioma: Español
Referencias bibliográficas: 31
Paginas: 29-40
Archivo PDF: 1643.25 Kb.


RESUMEN

Este artículo presenta un método no obstructivo para la detección del síndrome de apnea-hipopnea del sueño (SAHS). El flujo respiratorio es medido indirectamente a través de un colchón sensorizado (PBS - Pressure Bed Sensor) que incluye 8 transductores de presión. Mediante la transformada de Hilbert se obtiene la amplitud instantánea de las señales respiratorias y se reduce la información a través del análisis de componentes principales (ACP). Los eventos respiratorios (ERs - apneas/hipopneas) se localizan como una reducción en la amplitud instantánea resultante y se contabilizan en el índice de eventos respiratorios (IER), un índice de severidad similar al oficial apneahypopnea index (AHI). El PBS se analiza agrupando primero la información de pares de canales y después utilizando los 8 canales. Los IER se evalúan comparándolos con el AHI en diferentes niveles de severidad. En el diagnóstico de pacientes sanos y patológicos se obtuvo una sensibilidad, especificidad y exactitud de 92%, 100% y 96% respectivamente, utilizando la información de dos u ocho canales. Con estos resultados podemos proponer el uso del PBS como una alternativa para el diagnóstico del SAHS en ambientes fuera del hospital, ya que no requiere la presencia de un clínico especialista para su uso.


REFERENCIAS (EN ESTE ARTÍCULO)

  1. Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: a population health perspective. American Journal of Respiratory and Critical Care Medicine, 2002; 165(9): 1217-1239.

  2. The report of an American Academy of Sleep Medicine task force. Sleep- Related Breathing Disorders in adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research. Sleep, 1999; 22(5): 667-689.

  3. Patil SP, Schneider H, Schwartz AR, Smith PL. Adult Obstructive Sleep Apnea Pathophysiology and Diagnosis. Chest, 2007; 132(1): 325-337.

  4. Douglas T, Floras JS. Sleep Apnea and Heart Failure, Part I: Obstructive Sleep Apnea. Circulation, 2003; 107:1671-1678

  5. Calvin AD, et al. Obstructive sleep apnea, inflammation, and the metabolic syndrome. Metabolic Syndrome and Related Disorders, 2009; 7(4): 271-277.

  6. Gruber A, Horwood F, Sithole J, Ali NJ, Idris I. Obstructive sleep apnoea is independently associated with the metabolic syndrome but not insulin resistance state. Cardiovascular Diabetology. 2006; 5: 22

  7. Flemons WW, Littner MR, Rowley JA, Gay P, Anderson WM, Hudgel DW, Loube DI. Home Diagnosis of Sleep Apnea: A Systematic Review of the Literature: An Evidence Review Cosponsored by the American Academy of Sleep Medicine, the American College of Chest Physicians, and the American Thoracic Society. Chest, 2003; 124(4): 1543-1579.

  8. Heneghan C, Chua CP, Garvey JF, de Chazal P, Shouldice R, Boyle P, et al. A portable automated assessment tool for sleep apnea using a combined Holteroximeter. Sleep, 2008; 31(10): 1432-1439.

  9. Park S, Jayaran S, Enhancing the quality of life through weareable technology. IEEE Engeenering in Medicine and Biology, 2003; 22(3): 41-48

  10. Anliker U, Ward JA, Lokowicz P, Troster G, Dolveck F, Baer M, Keita F, et al. AMON: A wereable multiparameter medical monitoring and alert system. IEEE Transactios on Information Technology in Biomedicine, 2004; 8(4): 415-427

  11. Alihanka J, Vaahtoranta K, Saarikivi I. A new method for long-term monitoring of the ballistocardiogram, heart rate, and respiration. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 1981; 240(5): R384-R392.

  12. Polo O, Brissaud L, Sales B, Besset A, Billiard M. The validity of the static charge sensitive bed in detecting obstructive sleep apnoeas. European Respiratory Journal, 1988; 1(4): 330-336.

  13. Salmi T, Partinen M, Hyyppä M, Kronholm E. Automatic analysis of static charge sensitive bed (SCSB) recordings in the evaluation of sleep-related apneas. Acta Neurologica Scandinavica, 1986; 74(5): 360-364.

  14. Salmi T, Telakivi T, Partinen M. Evaluation of automatic analysis of SCSB, airflow and oxygen saturation signals in patients with sleep related apneas. Chest, 1989; 96(2): 255-261

  15. Polo O. Partial upper airway obstruction during sleep. Studies with the static charge-sensitive bed (SCSB). Acta Physiologica Scandinavica. Supplementum, 1992; 606:1

  16. Rauhala E, Erkinjuntti M, Pol O. Detection of periodic leg movements with a static charge sensitive bed. Journal of Sleep Research, 1996; 5(4): 246-250.

  17. Shin JH, Chee YJ, Jeong DU, Park KS. Nonconstrained sleep monitoring system and algorithms using air-mattress with balancing tube method. IEEE Transactions on Information Technology in Biomedicine, 2010; 14(1): 147-156.

  18. Kortelainen JM, van Gils M, Pärkkä J. Multichannel Bed Pressure Sensor for Sleep monitoring. Computing in Cardiology, 2012, Krakow, Poland.

  19. Kortelainen JM, Mendez MO, Bianchi AM, Matteucci M, Cerutti S. Sleep staging based on signals acquired through bed sensor. IEEE Transactions on Information Technology in Biomedicine, 2010; 14(3): 776-785.

  20. Oppenheim A, Schafer R, Buck J. Discrete- Time Signal Processing. Prentice Hall, Tercera edición, 2009; 942-955

  21. Pearson K. Principal components analysis. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 1901; 6(2): 559.

  22. Martinez WL, Martinez AR. Exploratory data analysis with MATLAB. CRC Press, 2005; 33-38.

  23. Nakano H, Tanigawa T, Furukawa T, Nishima S. Automatic detection of sleepdisordered breathing from a single-channel airflow record. European Respiratory Journal, 2007; 29(4): 728-736.

  24. Grover, Sukhdev S, Stephen D, Pittman. Automated detection of sleep disordered breathing using a nasal pressure monitoring device. Sleep and Breathing, 2008; 12(4): 339-345

  25. Han J, Shin HB, Jeong DU, Park KS. Detection of apneic events from single channel nasal airflow using 2nd derivative method. Computer Methods and Programs in Biomedicine, 2008; 91(3): 199-207

  26. Cooper B, Veale D, Griffiths C, Gibson G. Value of nocturnal oxygen saturation as a screening test for sleep apnoea. Thorax, 1991; 46(8): 586-588.

  27. Vázquez J, Tsai W, Flemons W, Masuda A, Brant R, Hajduk E, Remmers J. Automated analysis of digital oximetry in the diagnosis of obstructive sleep apnoea. Thorax, 2000; 55(4): 302-307.

  28. Zamarrón C, Gude F, Barcala J, Rodriguez JR, Romero PV. Utility Of Oxygen Saturation And Heart Rate Spectral Analysis Obtained From Pulse Oximetric Recordings In The Diagnosis Of Sleep Apnea Syndrome. Chest, 2003; 123(5): 1567-1576.

  29. Khandoker AH, Gubbi J, Palaniswami M. Automated scoring of obstructive sleep apnea and hypopnea events using shortterm electrocardiogram recordings. IEEE Transactions on Information Technology in Biomedicine, 2009; 13(6): 1057-1067.

  30. Heneghan C, de Chazal P, Ryan S, Chua CP, Doherty L, Boyle P, McNicholas WT. Electrocardiogram recording as a screening tool for sleep disordered breathing. Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine, 2008; 4(3): 223-228.

  31. Mendez M.O, Bianchi AM, Matteucci M, Cerutti S, Penzel T. Sleep apnea screening by autoregressive models from a single ECG lead. IEEE Transactions on Biomedical Engineering, 2009; 56(12): 2838-2850.




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