Classification of parkinsonian and essential tremor using empirical mode decomposition and support vector machine
Taking into account two types of tremor, namely essential tremor (ET) and Parkinson?s disease (PD), which are often misdiagnosed in clinical practice, a novel approach using singular value decomposition (SVD) to extract the features of intrinsic mode functions (IMFs) and support vector machine (SVM) is proposed to distinguish between them. The hand acceleration signals of 25 voluntary subjects with two conditions were collected and preprocessed. The empirical mode decomposition (EMD) method decomposed the signals into a number of stationary IMFs. The singular value features of the preceding four IMFs were extracted, and the features were inputted to the SVM classifier, which can recognize the two types of tremor. To comparing, we also used the singular value features of discrete wavelet transform (DWT) as input to the SVM. Cross-validation testing results indicated that the accuracy, sensitivity, and specificify of EMD-SVD features extracted are all remarkable higher than that of DWT-SVD method. Due to the accuracy, sensitivity, and specificify could arrive at 98%, 97.5% and 98.33% respectively, thus, practical guiding significance for diagnosing tremor types in clinic is provided.
empirical mode decomposition support vector machine singular value decomposition tremor classification