Real-time algorithm for a mobile cardiac monitoring system to detect life-threatening arrhythmias

This paper presents a real-time algorithm for a mobile cardiac monitoring system to detect life-threatening arrhythmias. This detection algorithm focuses on two life-threatening 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 real-time 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 out-of-hospital rehabilitation.