An Experiment On ECG Classification Using Back-propagation Neural Network

Tsai, Yuh-Show; Hung, B.N.; Tung, Shin-Lun

doi:10.1109/iembs.1990.691840

Cited by 14 publications

(9 citation statements)

References 2 publications

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“…There have been several proposed methods for ECG signal classification in the past, ranging from artificial neural networks (ANN) [5], [6] to linear discriminant analysis. Previous works in classification of heart arrhythmia using BbNNs have been documented here [3], [4].…”

Section: Literature Review and Related Workmentioning

confidence: 99%

“…Feature extraction can be done through various methods, such as discrete wavelet transform (DWT) [6], hermitian transform [11], principle component analysis (PCA) [3], fast fourier transform (FFT) [5], and many more. Hermite polynomial evaluation of the ECG signal is able to cluster the ECG complex in a very efficient and accurate manner, and is selected for use in this work.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

“…In previous works [5], [6], artificial neural networks (ANN) have been successfully implemented to classify various kinds of ECG based features. Also, most types of neural networks are computationally intensive, and cannot be implemented in embedded software without sacrificing real time execution [7].…”

Section: Introductionmentioning

confidence: 99%

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Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals

Nambiar

Khalil-Hani

Marsono

2012

2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences

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Heart arrhythmia is a fairly common medical condition, in which abnormal electrical activity occurs in the heart. However, it can be life threatening if left untreated or undiagnosed. This paper introduces an improved method to classify heart arrhythmia from electrocardiogram (ECG) signals using Block-based Neural Networks (BbNN). BbNNs are used in the hardware implementation of this problem due to its regular block based structure, relatively fast computational speeds, and lower resource consumption. The training mechanism for evolving BbNNs used in the work utilizes Genetic Algorithm (GA), but is able to handle larger sets of training data more efficiently due to an implementation of a novel multithreaded fitness evaluation approach. The ECG heartbeat dataset is taken from the MIT-BIH arrhythmia database, and feature extraction is done using the evaluation of Hermite polynomials on the preprocessed ECG signal. The proposed BbNN system-on-chip (SoC) shows high accuracy in its arrhythmia classification, with an average accuracy of 99.64% for all tested patient records.

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Section: Literature Review and Related Workmentioning

confidence: 99%

Section: Literature Review and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals

Nambiar

Khalil-Hani

Marsono

2012

2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences

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“…In the past few decades, several innovative methods for automated arrhythmia detection have been proposed [8] in order to assist monitoring the ECG signal [9]. These methods are based on transformation of Wavelets, RBF Neural Networks, self-organizing feature map [12] and fuzzy logic techniques.…”

Section: Related Research Workmentioning

confidence: 99%

Neural Network based indicative ECG classification

Gupta

Thomas

Kumar

et al. 2014

2014 5th International Conference - Confluence the Next Generation Information Technology Summit (Confluence)

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The Electrocardiogram (ECG) is undoubtedly the most used biological signal in the clinical world and it is a means for detection of several cardiac abnormalities. Pattern recognition, diagnostic classification of ECGs constitutes an interesting application of Artificial Neural Networks (ANNs). This paper illustrates the ability of a feed-forward back propagation using Neural Network for classify unknown ECG waveforms keen on one of the 4 discrete class. Out of the 4 classes, 3 of them correspond to abnormal ECG signals and 1 represents the healthy group. In addition, the Neural Network model developed has the option to categorize unknown ECG input signals as unclassified, since it represents an unknown pathology. Preliminary results are obtained using data from 4 different Physiobank ECG database.

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“…A number of algorithms have been developed in enhancing intelligent classification of the ECG signals [2][3][4][5]. Among them, the ANN-based ECG waveform discrimination algorithms, with classification rates comparable to those of the expert cardiologists, have been widely adopted by ECG signal professionals [6][7][8]3].…”

Section: Introductionmentioning

confidence: 99%

A multi-stage neural network classifier for ECG events

Hosseini

Reynolds²,

Powers³

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-In this paper, a multi-stage network including two multilayer perceptron (MLP) and one self organizing map (SOM) networks is presented. The input of the network is a combination of independent features and the compressed ElectroCardioGram (ECG) data. The proposed network as a form of data fusion, performs better than using the raw data or individual features. We classified six common ECG waveforms using ten ECG records of the MIT/BIH arrhythmia database. An average recognition rate of 0.883 was achieved within a short training and testing time.

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An Experiment On ECG Classification Using Back-propagation Neural Network

Cited by 14 publications

References 2 publications

Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals

Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals

Neural Network based indicative ECG classification

A multi-stage neural network classifier for ECG events

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