Parkinson disease prediction using intrinsic mode function based features from speech signal

In this study, empirical mode decomposition (EMD) based features are demonstrated to capture the speech characteristics. A new feature, intrinsic mode function cepstral coefficient (IMFCC) is proposed to efficiently represent the characteristics of Parkinson speech. The performances of proposed features are assessed with two different datasets: dataset-1 and dataset-2 each having 20 normal and 25 Parkinson affected peoples. From the results, it is demonstrated that the proposed intrinsic mode function cepstral coefficient feature provides superior classification accuracy in both datasets. There is a significant increase of 10–20% in accuracy compared to the standard acoustic and Mel-frequency cepstral coefficient (MFCC) features.
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research