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The authors have developed a method to discriminate life-threatening ventricular arrhythmias by observing the QRS complex of the electrocardiogram (ECG) in each heartbeat. Changes in QRS complexes due to rhythm origination and conduction path were observed with the Fourier transform, and three kinds of rhythms were discriminated by a neural network. In this paper, the potential of the authors' method for clinical uses and real-time detection was examined using human surface ECGs and intracardiac electrograms (EGMs). The method achieved high sensitivity and specificity (>0.98) in discrimination of supraventricular rhythms from ventricular ones. The authors also present a hardware implementation of the algorithm on a commercial single-chip CPU.

Rate responsive cardiac pacemakers in which pacing rate is adjusted by hemodynamic and metabolic demands have been developed in recent years. Although different parameters such as body activity, QT interval, respiration rate, body temperature are used to presume metabolic demand, it is suspected that no single parameter can provide accurate estimate of metabolic demand, and hence rate responsive pacemaker by combined parameters is proposed. This study is aimed to investigate physiological parameters during exercise so as to obtain reliable pacing control algorithm. Twenty healthy volunteers were assessed by a traded treadmill exercise according to the Bruce protocol, and hart rate, QT interval, body temperatures (forehead deep body temperature, rectal, tympanic, oral and axillary temperatures) and respiration rate were monitored. The results showed that correlation coefficient between heart rate and QT interval was 0.979, and that between hart rate and respiration rate was 0.969. Hysteresis was observed when these parameters were plotted against hart rate through a whole exercise protocol.