Nonlinear dynamics analysis of heart rate variability signal to detect sleep disordered breathing in children

Rohit A Krishnam, University of Texas at El Paso


This thesis evaluates the significance of various Non-linear Dynamics Analyses to study the heart rate variability in children with sleep disordered breathing. For short term data according to the guidelines of the European Task Force, ECG data was collected from children who were diagnosed with Sleep Apnea. For this data, the presence of non-stationarity in the derived HRV signal was determined from the calculated local Hurst Exponent. A visual display tool, Poincaré plots and Approximate Entropy was used to show the presence of correlation in the data. Long term datasets were collected during nocturnal sleep of young children who were recommended to the Sleep Lab for suspicion of Sleep Apnea. The ECG data was collected over a period 8 hours; the derived HRV signal was then divided into its corresponding sleep stages with the aid of the collected Sleep Hypnogram values, measured at epochs of 30 seconds. The scaling exponents using Detrended Fluctuation Analysis and the ApEn were calculated for each sleep stage. The tests were carried out on datasets collected from children between the age of 1 and 17 years. Two sample subjects, whose data was recorded and collected for different sleep stages and breathing patterns were considered for short term analysis and 7 sample subjects (after dividing their respective sleep stages) were considered for long term analysis to calculate the Non-linear dynamics parameters. The accuracy rate of ApEn is approximately 72% for both long term and short term data sets. The accuracy rate of Alpha derived from Detrended Fluctuation Analysis for long term correlations is 57%. ^

Subject Area

Engineering, Biomedical|Health Sciences, Public Health

Recommended Citation

Krishnam, Rohit A, "Nonlinear dynamics analysis of heart rate variability signal to detect sleep disordered breathing in children" (2005). ETD Collection for University of Texas, El Paso. AAI1430259.