Pulse wave amplitude and heart period variability in children with upper airway obstruction.

OBJECTIVE: The aim of this study was to assess cardiovascular autonomic modulation in children with upper airway obstruction (UAO), to compare this modulation to that of non-snoring children and to investigate the effect of adenotonsillectomy (AT). METHODS: ECG and finger photoplethysmographic signals obtained from overnight polysomnographic (PSG) recordings of 31 children with mild-to-moderate UAO and 34 non-snoring children were analysed. The extent of autonomic modulation was assessed by symbolic analysis of heart period (HP), pulse wave amplitude (PWA), and their joint dynamics during non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. RESULTS: Children with UAO showed more frequent patterns of monotonically increasing and decreasing HP in NREM sleep and monotonically increasing and decreasing joint PWA-HP patterns in REM and NREM sleep at baseline compared to controls, even when considering only periods of sleep free of discrete respiratory events. Following AT, HP, and joint PWA-HP dynamics significantly altered towards levels observed in the control group. CONCLUSIONS: In children with mild-to-moderate UAO, cardiovascular autonomic modulation is more prevalent, even during quiet, event-free sleep. AT appears to reverse this pattern.

T-wave morphology can distinguish healthy controls from LQTS patients.

Long QT syndrome (LQTS) is an inherited disorder associated with prolongation of the QT/QTc interval on the surface electrocardiogram (ECG) and a markedly increased risk of sudden cardiac death due to cardiac arrhythmias. Up to 25% of genotype-positive LQTS patients have QT/QTc intervals in the normal range. These patients are, however, still at increased risk of life-threatening events compared to their genotype-negative siblings. Previous studies have shown that analysis of T-wave morphology may enhance discrimination between control and LQTS patients. In this study we tested the hypothesis that automated analysis of T-wave morphology from Holter ECG recordings could distinguish between control and LQTS patients with QTc values in the range 400-450 ms. Holter ECGs were obtained from the Telemetric and Holter ECG Warehouse (THEW) database. Frequency binned averaged ECG waveforms were obtained and extracted T-waves were fitted with a combination of 3 sigmoid functions (upslope, downslope and switch) or two 9th order polynomial functions (upslope and downslope). Neural network classifiers, based on parameters obtained from the sigmoid or polynomial fits to the 1 Hz and 1.3 Hz ECG waveforms, were able to achieve up to 92% discrimination between control and LQTS patients and 88% discrimination between LQTS1 and LQTS2 patients. When we analysed a subgroup of subjects with normal QT intervals (400-450 ms, 67 controls and 61 LQTS), T-wave morphology based parameters enabled 90% discrimination between control and LQTS patients, compared to only 71% when the groups were classified based on QTc alone. In summary, our Holter ECG analysis algorithms demonstrate the feasibility of using automated analysis of T-wave morphology to distinguish LQTS patients, even those with normal QTc, from healthy controls.