Classification of Heartbeats based on Linear Discriminant Analysis and Artificial Neural Network

Abstract

In this paper, we proposed a heartbeat classification algorithm based on linear discriminant analysis and artificial neural network. For the input of classifier, we extracted 275 input features from the first derivative signal of ECG signal and RR interval information and it was reduced to be 6 by LDA. To evaluate the performance of the proposed algorithm, we compared the result of the proposed algorithm with that of fuzzy inference system classifier. MIT-BIH Arrhythmia database were used as test and learning data. The performance of the proposed algorithm was 97.49% for sensitivity, 97.91% for specificity and 96.36% for accuracy. For the extraction of features, the first derivative signal of ECG is used only so that the real-time implementation of this algorithm was possible. And, on account of the reduction of feature dimensionality, the time cost for learning and testing can be expected.

Microarray Data Classification using Artificial Neural Network

“…ANN has input, output and hidden layer. It is also used in hand moment classification [13], heartbeat classification [14] and electrocardiogram signal classification [15]. The input and output patterns with complex relationship are found in the nonlinear statistical data modeling tools.…”

Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

“…ANN have been used alone or in combination with other classifiers for ECG classification [12,33,34,59,60,74,149], as summarized in Table 5.…”

“…The large variety of features used to represent ECG can be grouped in terms of features used for (a) arrhythmia detection [6,14,[57][58][59][60][61][62][63][64][65][66][67], (b) rhythm analysis [15,[22][23][24][25][26][27][68][69][70], and (c) human identification . Classification methods range from ANN [12,[59][60][33][34], SVM [61-62, 64-65, 67-68, 70-72], linear discriminant analysis (LDA) [73][74], k-nearest-neighbour (k-NN) [75], mixture of experts (MOE) [76], Bayesian networks [77], Kalman Filtering (KF) [78][79][80][81][82][83][84][85][86][87], Hidden Markov Models (HMM) [88][89][90][91]…”

“…The main challenges for an ECG-based computer-aided-diagnosis system are ECG pre-processing (noise and artifacts removal), QRS detection, heartbeat classification, rhythm analysis, and disease diagnosis. This review does not consider pre-processing and QRS detection as they are very well established areas; instead, it is focussed on pattern recognition methodologies applied to heartbeat classification which is still an area of intense research [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][94][95][96][97]. This review does not consider pattern recognition approaches to ECG noise removal, QRS detection, and ECG segmentation (detection of the main wave-boundaries).…”

Análise de sinais de ECG com o uso de wavelets e redes neurais em FPGA

“…As cardiopatias adotadas foram o bloqueio de ramo esquerdo, bloqueio de ramo direito e contração ventricular prematura. A escolha das cardiopatias foi baseada nos trabalhos vistos na literatura (Ilic, 2007) (Song and Lee, 2005). A presença de um pulso normal ou com determinada cardiopatia é identificada por siglas como se segue:…”