Validity of neural network in sleep apnea

Sleep

Volumn 22, Issue 1, 1999, Pages 105-111

  El Solh, Ali A ^a   Mador, M Jeffrey ^a   Ten Brock, Eric ^a   Shucard, David W ^a   Abul Khoudoud, M ^a   Grant, Brydon J B ^a,b  

^a UNIVERSITY AT BUFFALO   (United States)

^b VA WESTERN NEW YORK HEALTHCARE SYSTEM   (United States)

Author keywords

Apnea hypopnea index; Artificial neural network; c index; Classification and regression tree; Sleep apnea

Indexed keywords

ARTICLE; BREATHING PATTERN; DIAGNOSTIC ACCURACY; HUMAN; MAJOR CLINICAL STUDY; NERVE CELL NETWORK; POLYSOMNOGRAPHY; PRIORITY JOURNAL; SLEEP APNEA SYNDROME;

EID: 0033083328     PISSN: 01618105     EISSN: None     Source Type: Journal    
DOI: 10.1093/sleep/22.1.105     Document Type: Article

References (30)

1. Partinen M, Jamieson A, Guilleminault C. Long-term outcome for obstructive sleep apnea syndrome patients. Chest 1988; 94:1201-4.
2. He J, Kryger MH, Zorick FJ, Conway W, Roth T. Mortality and apnea index in obstructive sleep apnea. Chest 1988; 94:9-14.
3. Maislin G, Pack AI, Kribbs NB, Smith PL, Schwartz AR, Kline LR, Schwab RJ, Dinges DF. A survey screen for prediction of apnea. Sleep 1995; 18:158-66.
4. Kump K, Whalen C, Tishler PV, Bowner I, Ferrette V, Strohl KP, Rosenberg C, Redline S. Assessment of the validity and utility of a sleep symptom questionnaire. Am J Respir Crit Care Med 1994; 150: 735-41.
5. Issa FG, Morrison D, Hadjuk E, Iyer A, Feroah T, Remmers JE. Digital monitoring of sleep-disordered breathing using snoring sound and arterial oxygen saturation. Am Rev Respir Dis 1993;148:1023-9.
6. Series F, Marc I, Cormier Y, La Forge J. Utility of nocturnal home oximetry for case finding in patients with suspected sleep apnea hypopnea syndrome. Ann Intern Med 1993; 119:449-453.
7. Levy P, Pepin J-L, Deschaux-Blanc C, Paramelle B, Brambilla C. Accuracy of oximetry for detection of respiratory disturbances in sleep apnea syndrome. Chest 1996;109:395-9.
8. Hoffstein V, Szalai JP. Predictive value of clinical features in diagnosing obstructive sleep apnea. Sleep 1993;16(2): 118-22.
9. Viner S, Szalai JP, Hoffstein V. Are history and physical examination a good screening test for sleep apnea? Ann Intern Med 1991;115:356-9.
10. Crocker BD, Olson LG, Saunders NA, Hensley MJ, McKeon JL, Allen KM, Gyulay SG. Estimation of the probability of disturbed breathing during sleep before a sleep study. Am Rev Respir Dis 1990;142:14-18.
11. Vaidya AM, Petruzzelli GJ, Walker RP, McGee D, Gopalsami C. Identifying obstructive sleep apnea in patients presenting for laser-assisted uvulopalatoplasty. Laryngoscope 1996; 106:431-37.
12. Dybowski R, Welle P, Chang R, Gant V. Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. Lancet 1996;347:1146-50.
13. Tourassi GD, Carey EF, Sostman HD, Coleman RE. Acute pulmonary embolism: Artificial neural network approach for diagnosis. Radiology 1993;189;555-58.
14. Baxt, WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med 1991;115:843-48.
15. Rechtschaffen A, Kales A, eds. A Manual of Standardized Technology, Techniques and Scoring System for Sleep Stages of Human Subjects. Los Angeles: UCLA Brain Information Service/Brain Research Institute, 1968.
16. ASDA Report. EEG arousals: scoring rules and examples. Sleep 1992;15:173-5.
17. Long WJ, Griffith JL, Selker HP, D'Agostino RB. A comparison of logistic regression to decision tree induction in a medical domain. Comp Biomed Res 1993;26: 74-97.
18. Weiss SM and Kulikowski CA. Neural nets. In: Computer systems that learn. Palo Alto, CA: Morgan Kaufmann, 1991: 81-112.
19. Weiss SM and Kulikowski CA. How to estimate the true performance of a learning system. In: Computer systems that learn. Palo Alto, CA: Morgan Kaufmann, 1991: 17-49.
20. Hanley JA, McNeil BJ. The meaning and use of the area under the receiver operating characteristic (ROC) curve. Radiology 1982;143:29-36.
21. El-Solh AA, and Grant BJB. A comparison of severity of illness scoring systems for critically ill obstetric patients. Chest 1996;110:1299-1304.
22. Dove HR, Dvorchik I, Mitchell S, et al. Predicting outcomes after liver transplantation. Ann Surg 1994; 219: 408-15.
23. Wu Y, Giger ML, Doi K, et al. Artificial networks in mamography: application to decision making in the diagnosis of breasr cancer. Radiology 1993;187:81-87.
24. Clark GM, Hilsenbeck SG, Ravdin PM, et al. Prognostic factors:rationale and methods of analysis and integration. Breast Cancer Res Treat 1994;32:105-12.
25. Chaffin DB, Hoffstein V, Feinsilver SH, Fein AM. The neural network analysis to predict sleep disordered breathing. Chest 1995;108:162S.
26. Hiraiwa A, Shimohara K, Tokunaga Y. EEG topography recognition by neural networks. IEEE Engineering in Medicine and Biology magazine 1990;9:39-42.
27. Shephard JW, Jr. Hypertension, cardiac arrhythmias, myocardial infarction and stroke in relation to obstructive sleep apnea. Clin Chest Med 1992;13:437-458.
28. Dyken ME, Somers VK, Yamada T, Ren ZY, Zimmerman MB. Investigating the relationship between stroke and obstructive sleep apnea. Stroke 1996;27:401-407.
29. Maislin G, Dinges DF, Kribbs NB, Schwab RJ, Smith PL, Schwartz AR, Kline LR, Pack AI. The prevalence of frequent episodes of falling asleep while driving in patients seen at sleep disorders centers. Sleep Res 1994;23:133.
30. Hart A, Wyatt J. Evaluating black-boxes as medical decision aids: issues arising from a study of neural networks. Med Inform 1990;15:229-36.