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Adequate characterization of hemodynamic and autonomic responses to physical and mental stress can elucidate underlying mechanisms of cardiovascular disease or anxiety disorders. We developed a physiological signal processing system for analysis of continuously recorded ECG, arterial blood pressure (BP), and respiratory signals using the programming language Matlab. Data collection devices are a 16-channel digital, physiological recorder (Vitaport), a finger arterial pressure transducer (Finapres), and a respiratory inductance plethysmograph (Respitrace). Besides the conventional analysis of the physiological channels, power spectral density and transfer functions of respiration, heart rate, and blood pressure variability are used to characterize respiratory sinus arrhythmia (RSA), 0.10-Hz BP oscillatory activity (Mayer-waves), and baroreflex sensitivity. The arterial pressure transducer waveforms permit noninvasive estimation of stroke volume, cardiac output, and systemic vascular resistance. Time trends in spectral composition of indices are assessed using complex demodulation. Transient dynamic changes of cardiovascular parameters at the onset of stress and recovery periods are quantified using a regression breakpoint model that optimizes piecewise linear curve fitting. Approximate entropy (ApEn) is computed to quantify the degree of chaos in heartbeat dynamics. Using our signal processing system we found distinct response patterns in subgroups of patients with coronary artery disease or anxiety disorders, which were related to specific pharmacological and behavioral factors.
