This study proposed the detection approach for the Congestive Heart Failure (CHF) disease by short-time electrocardiographic monitoring. Recent literature reviews only reported that RR intervals and Heart Rate Variability (HRV) indicate key hidden information to discriminate CHF groups from healthy controls. However what if possible to find certain short-time electrocardiographic monitoring duration to give fast reference advice for CHF diagnoses, has not been well addressed. In this study, commonly applied databases from Phy-sioNet are introduced, in which approximate 20-hour individual Electrocardiogram (ECG) recordings are used. Those signals are first classified into largely variable assessment lengths.
In order to offer a reliable, fast, and offset-free tracking performance for the regulation of heart rate (HR) during treadmill exercise, a two-input single-output (2ISO) control system by simultaneously manipulating both treadmill speed and gradient is proposed. The decentralized integral controllability (DIC) analysis is extended to nonlinear and non-square processes especially for a 2ISO process, namely multi-loop integral controllability (MIC). The proposed multi-loop integral control-based HR regulation by manipulating treadmill speed and gradient is then validated through a comparative treadmill experiment that compares the system performance of the proposed 2ISO MIC control loop with that of single-input single-output (SISO) loops, speed/gradient-to-HR. The experimental validation presents that by simultaneously using two control inputs, the automated system can achieve the fastest HR tracking performance and stay close to the reference HR during steady state, while comparing with two SISO structures, and offer the fault-tolerant ability if the gains of the two multi-loop integral controllers are well tuned. It has a vital implication for the applications of exercise rehabilitation and fitness in relation to the automated control system.
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