Abstract :
The authors´ objective was to determine the accuracy of a simple, visual heart rate (HR) tachogram-based method for identifying significant obstructive sleep apnea hypopnea syndrome (OSAHS). N=35 HR tachograms, 10 min/line, 1 hr/page, were generated from machine-generated beat-to-beat RR interval data provided by PhysioNet and extracted using the WFDB software package. Each tachogram was analyzed for the presence of cyclic variation of HR (CVHR), i.e., visible, cyclic, rapid increases and subsequent decreases in HR. Studies with >10 CVHR events in 1 hr and >100 minutes of CVHR during the entire night were scored as “severe”, otherwise they were scored as “none. Of the 30 studies that were known to have either severe OSAHS or none, 96.6% were correctly scored using this simple method. In conclusion, these results suggest that HR tachograms, which can easily be generated as a part of routine Holter scanning, may identify patients with previously undetected OSAHS, permitting two diagnostic tests for the effort and cost of one
Keywords :
electrocardiography; feature extraction; medical signal detection; medical signal processing; sleep; 1 h; 100 min; PhysioNet; WFDB software package; cyclic variation; diagnostic tests; electrodiagnostics; entire night; machine-generated beat-to-beat RR interval data; obstructive sleep apnea hypopnea syndrome detection; routine Holter scanning; simple visual heart rate tachogram-based method; Data mining; Heart rate; Heart rate detection; Hospitals; Laboratories; Medical diagnostic imaging; Myocardium; Sleep apnea; Software packages; Testing;
