Neural Network Based Classification of Myocardial Infarction: A Comparative Study of Wavelet and Fourier Transforms

Al-Naima, Fawzi M.; Al-Timemy, Ali H.

doi:10.5772/7533

Cited by 8 publications

(6 citation statements)

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“…They proposed a probabilistic NN approach to discriminate the difference between a normal ECG signal and an arrhythmia affected signal with an accuracy of 96.5% classification rate. Similarly, Naima and Timemy used discrete WT denoising procedure on ECG data collected from two hospitals in Bagdad [13]. Their discrete WT-NN classifier with six neurons in the hidden layer detected acute MI with 95% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Noise corruption can generate similar morphologies to the ECG waveform, reducing the discriminating power of heartbeat patterns, and increasing the rate of false alarms for cardiac monitors [9]. Therefore, a large number of NN approaches for ECG classification have included signal preprocessing for noise reduction, using a wavelet transformer (WT) [6,[11][12][13], nonlinear cubic spline interpolation (CSI) [14], fast Fourier transformation (FFT) [15] or band-pass filters [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Noise-tolerant modular neural network system for classifying ECG signal

Ochoa¹,

Mena

Felix³

et al. 2019

IJCAI

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Millions of electrocardiograms (ECG) are interpreted every year, requiring specialized training for accurate interpretation. Because automated and accurate classification ECG signals will improve early diagnosis of heart condition, several neural network (NN) approaches have been proposed for classifying ECG signals. Current strategies for a critical step, the preprocessing for noise removal, are still unsatisfactory. We propose a modular NN approach based on artificial noise injection, to improve the generalization capability of the resulting model. The NN classifier initially performed a fairly accurate recognition of four types of cardiac anomalies in simulated ECG signals with minor, moderate, severe, and extreme noise, with an average accuracy of 99.2%, 95.1%, 91.4%, and 85.2% respectively. Ultimately we discriminated normal and abnormal heartbeat patterns for single lead of raw ECG signals, obtained 95.7% of overall accuracy and 99.5% of Precision. Therefore, the propose approach is a useful tool for the detection and diagnosis of cardiac abnormalities.Povzetek: V članku je opisana metoda modularnih nevronskim mrež za prepoznavanje šumnih ECG signalov. Materials and methodsThe NN classification comprises five stages: (i) simulation of ECG signal, (ii) extraction of features that indicate cardiac abnormalities, (iii) computer generation of normal and abnormal heartbeat patterns, (iv) artificial noise injection, and (v) cardiac rhythm classification on simulated and real ECG signals.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noise-tolerant modular neural network system for classifying ECG signal

Ochoa¹,

Mena

Felix³

et al. 2019

IJCAI

View full text Add to dashboard Cite

show abstract

“…Common solutions, such as the low-pass linear-phase filter, high-pass linear-phase filter, median filter, and mean median filter, are usually used for such de-noising task. Classical feature extraction approaches, such as continuous wavelet transform (CWT) [ 25 ], S-Transform (ST), discrete Fourier transform (DFT), principal component analysis (PCA), Daubechies wavelet (Db4) [ 26 ], and independent component analysis (ICA) [ 27 ] can then be applied. Researchers in [ 28 ] used three machine-learning based algorithms, namely, Discrete Wavelet Transform (DWT) [ 29 ], Empirical Mode Decomposition (EMD) [ 29 ] and Discrete Cosine Transform (DCT) to obtain coefficients from ECG signals.…”

Section: Related Workmentioning

confidence: 99%

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

Zhang

Cao

et al. 2018

Sensors

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By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

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“…3 layer FFNN with back propagation algorithm is used as classifier. F.Naima and A.Timemy [10] have extracted R location and RR interval using Db4 discrete wavelet transform. FFNN trained with back propagation algorithm is used.…”

Section: Issues In Ecg Classificationmentioning

confidence: 99%

“…Feature extraction techniques used by researchers are DWT [5,8], CWT [8], DCT [8], Db4 [6,10], Pan-Tompkins algorithm [14] etc. For feature extraction using wavelet, decomposition levels used are 2, 3, 4 or 8.…”

Section: B Feature Extraction Techniquesmentioning

confidence: 99%

Classification of ECG signals using machine learning techniques: A survey

Jambukia

Dabhi

Prajapati

2015

2015 International Conference on Advances in Computer Engineering and Applications

186

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Classification of electrocardiogram (ECG) signals plays an important role in diagnoses of heart diseases. An accurate ECG classification is a challenging problem. This paper presents a survey of ECG classification into arrhythmia types. Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. Different classifiers are available for ECG classification. Amongst all classifiers, artificial neural networks (ANNs) have become very popular and most widely used for ECG classification. This paper discusses the issues involved in ECG classification and presents a detailed survey of preprocessing techniques, ECG databases, feature extraction techniques, ANN based classifiers, and performance measures to address the mentioned issues. Furthermore, for each surveyed paper, our paper also presents detailed analysis of input beat selection and output of the classifiers.

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Neural Network Based Classification of Myocardial Infarction: A Comparative Study of Wavelet and Fourier Transforms

Cited by 8 publications

References 0 publications

Noise-tolerant modular neural network system for classifying ECG signal

Noise-tolerant modular neural network system for classifying ECG signal

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

Classification of ECG signals using machine learning techniques: A survey

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