Fusion of Heart Rate Variability and Cardiorespiratory Coupling in Patients with Heart Failure

The major syndromes in heart failure are ventricular hypertrophy, decreased ventricular functions, high resting heart rate, rush respiratory activity, and reduced physical activity. The ventricular hypertrophy and decreased ventricular functions can be assessed by the echocardiography. The high resting heart rate is due to over sympathetic drive that further decreases the capability of autonomic modulation. Heart rate variability (HRV) provides a noninvasive means for the autonomic evaluation. The rush respiratory activity but disappeared respiratory rhythm in HRV implies an attenuated cardiorespiratory coupling. The reduced physical activity not only decreases life quality but also deteriorates cardiopulmonary functions. The 24-hour HRV analysis using traditional ECG Holter has a confounding effect in interpreting HRV: it results in a combined result caused by internal physiological condition and daily physical activities. The proposed study is aimed to investigate electrocardiogram-derived-respiration (EDR), HRV and cardiorespiratory coupling in heart failure under a well-controlled experimental protocols and during different physical activities and sleep. In the first year, we will develop EDR algorithms based on different leads and investigate the EDR characteristics of heat failure. In the second year, a light, small-size and patient-comfortable data recorder with parallel recordings of electrocardiogram, respiration activity, and body accelerations will be developed. The developed system will be validated parallel with the clinical examination of sleep apnea and in normal subjects with 24-hour recording. In the third year, the developed system will be applied to heat failure patients before/after clinical therapy. The derived physiological parameters will be conducted to physical-related parameters (daily activity level, apnea-hypopnea index), physical activity- and sleep-related HRV (sympathetic-, parasympathetic-related, nonlinear indexes), cardio-respiratory coupling index, and parameter adaptation to physical activity. These parameters will be analyzed against clinical indexes such as left ventricular end-systolic volume, end-diastolic volume, and ejection fraction, and modeled by a statistical model or a neuro-fuzzy model.

Project ID:PB9907-10762
External Project ID:NSC99-2221-E182-017-MY3