ECG and EEG Pattern Classifications and Dimensionality Reduction with Laplacian Eigenmaps

Monica Fira,Liviu Goras

doi:10.14569/ijacsa.2020.0110306

Abstract

In this paper, we investigate the effect of dimensionality reduction using Laplacian Eigenmap (LE) in the case of several classes of electroencephalogram (EEG) and electrocardiographic (ECG) signals. Classification results based on a boosting method for EEG signals exhibiting P300 wave and k-nearest neighbour for ECG signals belonging to 8 classes are computed and compared. For EEG signals, the difference between the rate of classification in the original and reduced space with LE is relatively small, only several percent (maximum 10% for the 3 – dimensional space), and the original EEG signals belonging to a 128-dimensional space. This means that, for classification purposes the dimensionality of EEG signals can be reduced without significantly affecting the global and local arrangement of data. Moreover, for EEG signals that are collected at high frequencies, a first stage of data preprocessing can be done by reducing the dimensionality. For ECG signals, for segmentation with and without centering of the R wave, there is a slight decrease in the classification rate at small data sizes. It is found that for an initial dimensionality of 301 the size of the signals can be reduced to 30 without significantly affecting the classification rate. Below this dimension there is a decrease of the classification rate but still the results are very good even for very small dimensions, such as 3. It has been found that the classification results in the reduced space are remarkable close to those obtained for the initial spaces even for small dimensions.

Highlights

Manifold learning is a class of methods aimed at evidencing low-dimensional manifolds embedded in a highdimensional ambient space
Starting from the results obtained in our paper [20], in which we used the EEG signal to verify the preservation of the neighbourhoods in the reduced space with compressed sensed (CS), using the same test data we check if the reduced dimensionality data with Laplacian Eigenmaps (LE) keeps its neighbours
The remarkable result reported in this paper is the fact that dimensionality reduction for EEG and ECG signals using LE does not affect significantly the classification rate even for rather small dimensions

Summary

Introduction

Manifold learning is a class of methods aimed at evidencing low-dimensional manifolds embedded in a highdimensional ambient space. Whether linear manifold learning does not result in a good lowdimensional representation of high-dimensional data, it might happen that data lie on or close a nonlinear manifold so that more powerful non-linear dimensionality reduction by preserving the local structure of the input data can be applied. If data stay on a low-dimensional nonlinear manifold, it has been shown that usual methods will adjust automatically, and better learning rates may be obtained even if one understands little about the manifold form [1,2,3,4]. Starting from the above considerations regarding the nature of signals, manifolds and supervised learning, we asked the question that if for a class of real data we can reduce the size of the signals and if a supervised classification obtains similar results on the real, original data space and on the reduced space [6]

Objectives

Results

Conclusion