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Robust algorithm for arrhythmia classification in ECG using extreme learning machine
Robust algorithm arrhythmia classification ECG extreme learning machine
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2010/2/25
The experimental results of the entire MIT-BIH arrhythmia database demonstrate that the performances of the proposed algorithm are 98.00% in terms of average sensitivity, 97.95% in terms of average sp...
Workload Control Using the Real-time Extraction of Respiratory Sinus Arrhythmia
heart rate variability respiratory sinus arrhythmia real-time signal processing mental workload
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2009/6/9
Respiratory sinus arrhythmia (RSA) is well known as a noninvasive index of cardiac vagal activity, which has an important role in maintaining homeostasis. In this study, we improved the signal process...
Life-threatening ventricular arrhythmia recognition by nonlinear descriptor
Life-threatening ventricular arrhythmia recognition nonlinear descriptor
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2010/3/1
Background
Ventricular tachycardia (VT) and ventricular fibrillation (VF) are ventricular cardiac arrhythmia that could be catastrophic and life threatening. Correct and timely detection of VT or VF ...
Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running
arrhythmia simulation quantitative cellular automata modeling and parallel running
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2010/3/1
Quantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This pr...
Cardiac arrhythmia classification using autoregressive modeling
Cardiac arrhythmia classification autoregressive modeling
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2010/3/1
AR modeling results showed that an order of four was sufficient for modeling the ECG signals. The accuracy of detecting NSR, APC, PVC, SVT, VT and VF were 93.2% to 100% using the GLM based classificat...