Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform.

ABSTRACT: Feature extraction is an important part of data processing that provides a basis for more complicated tasks such as classification or clustering. Recently many approaches for signal feature extraction were created. However, plenty of proposed methods are based on convolutional neural networks. This class of models requires a high amount of computational power to train and deploy and large dataset. Our work introduces a novel feature extraction method that uses wavelet transform to provide additional information in the Independent Component Analysis mixing matrix. The goal of our work is to combine good performance with a low inference cost. We used the task of Electrocardiography (ECG) heartbeat classification to evaluate the usefulness of the proposed approach. Experiments were carried ou

SUBMITTER: Topolski M 

PROVIDER: S-EPMC8675669 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature