Use of modified sample entropy measurement to classify ventricular tachycardia and fibrillation

A study of ventricular fibrillation (VF) and ventricular tachycardia (VT) was undertaken using modified sample entropy analysis. Sample entropy (SampEn) is believed to provide the quantitative information about the complexity of experimental data that is corrupted by noise, short in data length. However, the similarity definition of vectors is based on a Heaviside function, of which the boundary is discontinuous and hard, which may cause the problems in the validity and accuracy of SampEn. To deal with the encountered problems, a modified sample entropy (mSampEn) based on fuzzy membership function was proposed. Its performances on characterizing VF and VT signals, as well as several simulated time series, demonstrate that mSampEn can more efficiently measure the complexity of time series. It is shown that, as a criteria for discriminating between VF and VT, mSampEn provides a significantly (p < 0.0001) higher (99%) accuracy rate than the standard one.