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Cardiac arrhythmias reveal multiple morphologies in time-domain waveforms. Abnormal beats reflect the origin and the conduction path of the ectopic heart activation pulses, including supraventricular, junctional, and ventricular arrhythmias and conduction abnormalities. This paper proposes a method for automated screening of cardiac arrhythmias using the discrete fractional-order integration (DFOI) and the meta-learning-based intelligent classifier. The DFOI process with the specific fractional order is used to extract the QRS features with the finite computations. It can deal with the irregular time-varying signals. The meta-learning-based intelligent classifier is used to identify the abnormal classes, consisting of a primary multilayer network and several agent networks. Since abnormal QRS complexes are multi-waveforms such as ventricular premature contraction, the classifier needs to retrain with new training patterns for meeting new condition in clinical applications. Thus, this model possesses the learning-to-optimization capability for retraining generalized regression neural network using the particle swarm optimization algorithm. Using incremental new training patterns, each inducer is used to track the past learning experiences through several agent networks. This pattern scheme can gradually enhance the accuracy by refining the optimal parameters of each sub-estimator for pattern recognition. Using the arrhythmia database of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database, the proposed intelligent classifier demonstrated greater efficiency and promising results in average accuracy, positive predictivity, and true negative rate for recognizing electrocardiography signals.

Cardiac Arrhythmias Automated Screening Using Discrete Fractional-Order Integration Process and Meta Learning Based Intelligent Classifier