Recent research indicates a significant association that the severity of fatigue and the autonomic nervous system (ANS) by analyzing the heart rate variability (HRV). In order to detect fatigue, an experiment that provides the subjects with some affective contents that can induce the variety of emotions and ANS was designed in this study. Each subject underwent an affective-content test while wearing a wireless Holter monitor. By analyzing the 20 subjects' HRV episode in the experiment, a new fatigue detection algorithm was established based on six features of the time and frequency domain (TFD) HRV and a neuro-fuzzy network. The six TFD features were used for the 20 subjects, with a reliable accuracy rate of 95%. The proposed algorithm can realize service for affective healthcare applications, such as the monitoring of the fatigability of humans in a ubiquitous environment.
This paper presents a real-time algorithm for a mobile cardiac monitoring system to detect life-threatening arrhythmias. This detection algorithm focuses on two lifethreatening arrhythmias ventricular tachycardia and fibrillation (VT/VF), which are detected through the application of pre-detection processing and main detection processing. In pre-detection processing, applies a statistical method to detect VT/VF. In contrast, a neural fuzzy network is applied to detect VT/VF in main detection processing. The neural fuzzy network's input features are obtained by wavelet transform and several effective extraction methods. This realtime detection algorithm outperform Amann's algorithm, with 92% accuracy and 93% sensitivity. It has been implemented as a cardiac monitoring system in a mobile phone. This system meets heart patient's requirements of early detection and outof-hospital rehabilitation.
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