Detection of high-frequency respiratory movements during sleep

Computer Methods and Programs in Biomedicine

Volumn 61, Issue 3, 2000, Pages 171-185

  Aittokallio, T ^a,b   Gyllenberg, M ^a,b   Jarvi J ^a,b   Nevalainen, O ^a,b   Polo, O ^a  

^a UNIVERSITY OF TURKU   (Finland)

^b TURKU CENTRE FOR COMPUTER SCIENCE   (Finland)

Author keywords

Outlier; Segmentation; Sleep breathing disorders

Indexed keywords

COMPUTER AIDED DIAGNOSIS; IMAGE SEGMENTATION; PATTERN RECOGNITION; SIGNAL DETECTION; SLEEP RESEARCH; STATISTICAL METHODS;

HIGH-FREQUENCY RESPIRATORY MOVEMENTS (HFRM); SLEEP BREATHING DISORDERS; STATIC-CHARGE-SENSITIVE BEDS (SCSB);

RESPIRATORY MECHANICS;

ALGORITHM; ARTICLE; AUTOANALYSIS; BREATHING DISORDER; BREATHING MECHANICS; ELECTROENCEPHALOGRAM; FOURIER TRANSFORMATION; HUMAN; LUNG VENTILATION; SENSOR; SLEEP;

ALGORITHMS; FEMALE; HUMANS; MIDDLE AGED; POLYSOMNOGRAPHY; RESPIRATION; RESPIRATORY FUNCTION TESTS; SLEEP;

EID: 0033955074     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-2607(99)00043-7     Document Type: Article

References (22)

1. Polo O. Partial upper airway obstruction during sleep. Acta Physiol. Scand. 145:1992;1-118.
2. Kirjavainen T. High-frequency respiratory movements during sleep. Ann. Univ. Turk. D263:1997;1-99.
3. Barnett V. The identification of multiple outliers. J. Am. Stat. Assoc. 88:1993;782-801. Comment to L. Davies and U. Gather.
4. Kitagawa G. On the use of AIC for the detection of outliers. Technometrics. 21:1979;193-199.
5. Tietjen G.L., Moore R.H. Some Grubbs-type statistics for the detection of several outliers. Technometrics. 14:1972;583-597.
6. Rosner B. On the detection of many outliers. Technometrics. 17:1975;221-227.
7. Fox A.J. Outliers in time series. J. R. Stat. Soc. B43:1972;350-363.
8. I. Chang, Outliers in time series, Ph.D. Dissertation, University of Wisconsin, Madison, 1982.
9. Tsay R.S. Time series model specification in the presence of outliers. J. Am. Stat. Assoc. 81:1986;132-141.
10. Tsay R.S. Outliers, level shifts, and variance changes in time series. Journal of Forecasting. 7:1988;1-20.
11. Chang I., Tiao G.C., Chen C. Estimation of time series parameters in the presence of outliers. Technometrics. 30:1988;193-204.
12. Bruce A.G., Martin R.D. Leave-k-out diagnostics for time series. J. R. Stat. Soc. B51:1989;363-424.
13. Michael D., Houchin J. Automatic EEG analysis: a segmentation procedure based on the autocorrelation function. Electroencephalogr. Clin. Neurophysiol. 46:1979;232-235.
14. Bodenstein G., Praetorius H.M. Feature extraction from the electroencephalogram by adaptive segmentation. Proc. IEEE. 65:1977;642-652.
15. Jansen B.H., Hasman A., Lenten R. Piecewise analysis of EEGs using AR-modeling and clustering. Comp. Biomed. Res. 14:1981;168-178.
16. Alihanka J., Vaahtoranta K., Saarikivi I. A new method for long-term monitoring of the ballistocardiogram, heart-rate, and respiration. Am. J. Physiol. 240:1981;R384-R392.
17. V.K. Rohatgi, Chi-square test of goodness of fit revisited, in: Statistical Inference, Wiley, New York, 1984, pp. 747-750.
18. Beckman R.J., Cook R.D. Outliers. Technometrics. 25:1983;119-149.
19. Rosner B. Percentage points for a generalized ESD many-outlier procedure. Technometrics. 25:1983;165-172.
20. k for normal samples, in: Identification of outliers, Chapman and Hall, London, 1980, pp. 144-147.
21. Makhoul J. Linear prediction: a tutorial review. Proc. IEEE. 63:1975;561-580.
22. Aittokallio T., Nevalainen O., Pursiheimo U., Saaresranta T., Polo O. Classification of nasal inspiratory flow shapes by attributed finite automata. Comp. Biomed. Res. 32:1999;34-55.