Arrhythmia Classification with Reduced Features by Linear Discriminant Analysis

Lee, J.; Park, K.L.; Song, Mansuk; Lee, K.J.

doi:10.1109/iembs.2005.1616623

Cited by 12 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The last 20 minutes of the remaining 23 records, containing 28114 normal beats and 4633 abnormal beats, are used to test the classification performance. Following three performance measures are employed to evaluate the performance of this algorithm and experimental result is listed in Table 1. correct classified normal beats total normal beats spe = (8) correct classified abnormal beats total abnormal beats sen = (9) correct classified beats total beats acc = (10) …”

Section: Resultsmentioning

confidence: 99%

A novel abnormal ECG beats detection method

Wang

Zheng

et al. 2010

2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE)

View full text Add to dashboard Cite

Automatic detection of life threatening abnormal beats in electrocardiogram (ECG) signal is of importance in many healthcare applications. The ECG beat signal variations in both shape and time impose great challenges to automatic detection tasks. To address those challenges and for high accuracy automatic detection, we present here a two stage abnormal beats detection algorithm. Normal and abnormal beat types are represented by templates which are selected from training data using clustering. Multiple templates for each beat type well represent the distribution of the data and allow nonlinearity of the discrimination. Critical features are extracted for both templates and incoming beat signals in discrete wavelet transform domain. The template matching is carried out using time invariant dynamic time warping (DTW). For those the DTW distance cannot provide sharp discrimination, an additional stage of verification is invoked to further check among those types having small distance with the incoming beat, based on ECG wave's time interval features. The algorithm is tested on a large data set (23 records containing 28114 normal beats and 4633 abnormal beats) and achieves an accuracy of 97.24%.

show abstract

Section: Resultsmentioning

confidence: 99%

A novel abnormal ECG beats detection method

Wang

Zheng

et al. 2010

2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE)

View full text Add to dashboard Cite

show abstract

“…The automatic detection of arrhythmias using the ECG can be done through several ways (here are some examples [4], [5], [6], [7], [8], [9], [10], [11], [12], [13]). One of the most popular approaches is based on machine learning.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Arrhythmias Using Wavelets and Self-Organized Artificial Neural Networks

Rogal¹,

Neto²,

Vinicius³

et al. 2009

2009 Ninth International Conference on Intelligent Systems Design and Applications

View full text Add to dashboard Cite

The arrhythmias or abnormal rhythms of the heart are common cardiac riots and may cause serious risks to the life of people, being one of the main causes on deaths. These deaths could be avoided if a previous monitoring of these arrhythmias were carried out, using the Electrocardiogram (ECG) exam. The continuous monitoring and the automatic detection of arrhythmias of the heart may help specialists to perform a faster diagnostic. The main contribution of this work is to show that self-organized artificial neural networks (ANNs), as the ART2, can be applied in arrhythmias automatic detection, working with Wavelet transforms for feature extraction. The self-organized ANN allows, at any time, the inclusion of other groups of arrhythmias, without the need of a new complete training phase. The paper presents the results of practical experimentations.

show abstract

“…They also used PCA as a method to select and extract features from the ECG signal. Lee et al (2005) applied linear discriminant analysis to 17 input features, based on wavelet coefficients, to reduce the feature dimension from 17 to 4, for arrhythmia detection usage. Then, a multi-layer perceptrons classifier was applied to detect six types of arrhythmia beats from the newly constructed four dimensional input features.…”

Section: Introductionmentioning

confidence: 99%

An improved procedure for detection of heart arrhythmias with novel pre‐processing techniques

et al. 2011

View full text Add to dashboard Cite

The objective of this study is to develop an algorithm to detect and classify six types of electrocardiogram (ECG) signal beats including normal beats (N), atrial pre-mature beats (A), right bundle branch block beats (R), left bundle branch block beats (L), paced beats (P), and pre-mature ventricular contraction beats (PVC or V) using a neural network classifier. In order to prepare an appropriate input vector for the neural classifier several pre-processing stages have been applied. Initially, a signal filtering method is used to remove the ECG signal baseline wandering. Continuous wavelet transform is then applied in order to extract features of the ECG signal. Next, principal component analysis is used to reduce the size of the data. A well-known neural network architecture called the multi-layered perceptron neural network is then utilized as the final classifier to classify each ECG beat as one of six groups of signals under study. Finally, the MIT-BIH database is used to evaluate the proposed algorithm, resulting in 99.5% sensitivity, 99.66% positive predictive accuracy and 99.17% total accuracy.

show abstract

Arrhythmia Classification with Reduced Features by Linear Discriminant Analysis

Cited by 12 publications

References 10 publications

A novel abnormal ECG beats detection method

A novel abnormal ECG beats detection method

Automatic Detection of Arrhythmias Using Wavelets and Self-Organized Artificial Neural Networks

An improved procedure for detection of heart arrhythmias with novel pre‐processing techniques

Contact Info

Product

Resources

About