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Oresko, J.J., Jin, Z., Huang, S., Sun, Y., Duschl, H. and Cheng, A.C. (2010) A Wearable Smart Phone Based Platform for Real Time Cardiovascular Disease Detection via Electrocardiogram Processing. IEEE Transactions on Information Technology in Biomedicine, 14, 734-740.
http://dx.doi.org/10.1109/TITB.2010.2047865

has been cited by the following article:

• 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.