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Critical Evaluation of Linear Dimensionality Reduction Techniques for Cardiac Arrhythmia Classification
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rajagopal, Rekha Ranganathan, Vidhyapriya |
| Copyright Year | 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://file.scirp.org/pdf/CS_2016072811062632.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Anterior descending branch of left coronary artery Artificial cardiac pacemaker Artificial neural network Biological Neural Networks Bounding interval hierarchy Coefficient Computation Curse of dimensionality Data (computing) Daubechies wavelet Dimensionality reduction Dimensions Exhibits as Topic Feature extraction Image scaling Linear discriminant analysis Multidimensional scaling Naive Bayes classifier Noise-induced hearing loss Nonlinear system Numerous Population Parameter Preprocessor Principal component analysis Probabilistic neural network Time complexity Wavelet transform perineuronal net (cell component) |
| Content Type | Text |
| Resource Type | Article |
