A robust method for diagnosis of morphological arrhythmias based on Hermitian model of higher-order statistics

BACKGROUND: Electrocardiography (ECG) signal is a primary criterion for medical practitioners to diagnose heart diseases. The development of a reliable, accurate, non-invasive and robust method for arrhythmia detection could assists cardiologists in the study of patients with heart diseases. This paper provides a method for morphological heart arrhythmia detection which might have different shapes in one category and also different morphologies in relation to the patients. The distinctive property of this method in addition to accuracy is the robustness of that, in presence of Gaussian noise, time and amplitude shift. METHODS: In this work 2(nd), 3(rd )and 4(th )order cumulants of the ECG beat are calculated and modeled by linear combinations of Hermitian basis functions. Then, the parameters of each cumulant model are used as feature vectors to classify five different ECG beats namely as Normal, PVC, APC, RBBB and LBBB using 1-Nearest Neighborhood (1-NN) classifier. Finally, after classifying each model, a final decision making rule apply to these specified classes and the type of ECG beat is defined. RESULTS: The experiment was applied for a set of ECG beats consist of 9367 samples in 5 different categories from MIT/BIH heart arrhythmia database. The specificity of 99.67% and the sensitivity of 98.66% in arrhythmia detection are achieved which indicates the power of the algorithm. Also, the accuracy of the system remained almost intact in the presence of Gaussian noise, time shift and amplitude shift of ECG signals. CONCLUSIONS: This paper presents a novel and robust methodology in morphological heart arrhythmia detection. The methodology based on the Hermite model of the Higher-Order Statistics (HOS). The ability of HOS in suppressing morphological variations of different class-specific arrhythmias and also reducing the effects of Gaussian noise, made HOS, suitable for detection morphological heart arrhythmias. The proposed method exploits these properties in conjunction with Hermitian model to perform an efficient and reliable classification approach to detect five morphological heart arrhythmias. And the time consumption of this method for each beat is less than the period of a normal beat.