Computer-based analysis of heart rate variability signal for detection of sleep disordered breathing in children

A computer-based analysis system was developed to display and analyze heart rate variability (HRV). ECG, oxygen saturation and respiratory signals (airflow, abdominal and thoracic movements), were used as raw data. The heart rate variability signal was derived from ECG by applying a Hilbert transform-based algorithm for reliable QRS complex detection. Following the guidelines suggested by the Task Force of the European Society of Cardiology and the North American Society for Pacing and Electrophysiology, appropriate time-domain and frequency-domain methods were used for HRV signal analysis. Autoregressive modeling of the HRV power spectrum was achieved by implementing the Burg algorithm. Three main spectral features were clearly distinguished in the heart rate variability signal spectrum from polysomnographic recordings of different sleep stages and were correlated with respiratory parameters. The integrated graphical user interface was developed using LabView and the signal processing algorithms were implemented using Matlab application programs. In this paper we present an overview of the system and analyze pilot data for two children undergoing nocturnal polysomnography. The pilot data demonstrated that the HRV analysis system may potentially distinguish between periods of normal and sleep disordered breathing (SDB) in children.