TITLE:
Critical Evaluation of Linear Dimensionality Reduction Techniques for Cardiac Arrhythmia Classification
AUTHORS:
Rekha Rajagopal, Vidhyapriya Ranganathan
KEYWORDS:
Data Preprocessing, Decision Support Systems, Feature Extraction, Dimensionality Reduction
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.9,
July
28,
2016
ABSTRACT: Embedding the original
high dimensional data in a low dimensional space helps to overcome the curse of
dimensionality and removes noise. The aim of this work is to evaluate the
performance of three different linear dimensionality reduction techniques (DR)
techniques namely principal component analysis (PCA), multi dimensional scaling
(MDS) and linear discriminant analysis (LDA) on classification of cardiac
arrhythmias using probabilistic neural network classifier (PNN). The design
phase of classification model comprises of the following stages: preprocessing
of the cardiac signal by eliminating detail coefficients that contain noise,
feature extraction through daubechies wavelet transform, dimensionality
reduction through linear DR techniques specified, and arrhythmia classification
using PNN. Linear dimensionality reduction techniques have simple geometric
representations and simple computational properties. Entire MIT-BIH arrhythmia
database is used for experimentation. The experimental results demonstrates
that combination of PNN classifier (spread parameter, σ = 0.08) and PCA DR
technique exhibits highest sensitivity and F score of 78.84% and 78.82%
respectively with a minimum of 8 dimensions.