No abstract

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.

Spectral analysis was carried out on the R-Wave Attenuation (RWA) trend and Heart Rate Variability (HRV) series, derived from the polysomnographic Electrocardiogram (ECG) of the subjects with and without Cheyne Stokes Breathing. Nocturnal polysomnography was performed on 16 Normal subjects and 7 subjects with Cheyne Stokes Breathing (CSB) patients. The polysomnographic ECG data was divided into fifteen minute epochs for analysis. These epochs are processed to obtain the RWA. Hilbert Transform based algorithm [4] was used for QRS detection. Power spectrum of RWA and HRV are computed for each clip by using Welch's averaged periodogram method. HRV is sensitive to REM sleep as well and hence not specific to sleep apnea [12]. Hence the parameters derived from HRV alone cannot be used as diagnostic markers. Hence a combined detection scheme which uses parameters derived from RWA and HRV power spectrum is used in the proposed method to increase detection accuracy. This method produced a sensitivity of 84.75% and specificity of 87.03% in the training set and sensitivity of 85.78% and a specificity of 87.19% in the test set.

High cost of diagnostic studies to detect sleep disordered breathing and lack of availability of certified sleep laboratories in all inhabited areas make investigation of alternative methods of detecting sleep disordered breathing attractive. This study aimed to explore the possibility of discerning obstructive sleep apnea (OSA) from Cheyne-Stokes respiration (CSR) using overnight electrocardiography (ECG). Polysomnographic and ECG signals were acquired from the 13 OSA and 7 CSR volunteer subjects. Two signals: R-Wave Attenuation (RWA) and Heart Rate Variability (HRV) series were derived from the ECG. Using frequency domain analysis, various frequency bands in the power spectrum of RWA and HRV signals were identified that showed sensitivity to OSA and CSR events. A three-stage algorithm was developed to detect and differentiate OSA events from CSR events using RWA and HRV analysis. To test the algorithm, the ECG data was divided into fifteen minute epochs for analysis. Seventy two epochs containing OSA and 72 with CSR events were selected. 48 OSA clips and 48 CSR clips were randomly selected to form the training set. The remaining 24 clips in each category formed the test set. This method produced an average sensitivity of 95.83% and specificity of 79.16% in the training set and sensitivity of 87.5% and a specificity of 75% in the test set.