Diagnosis of Multiclass Tachycardia Beats Using Recurrence Quantification Analysis and Ensemble Classifiers

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

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“…The individual output label corresponds to a segment of the input. Composed output labels cover the full sequence [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

2018

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“…RQA has found many applications in ECG signal analysis [ 123 – 125 ]. Chen et al investigated the effect of the exposure to low-frequency noise of different intensities (for 5 min) on the cardiovascular activities using recurrence plot analysis [ 126 ].…”

Section: Applications Of Nonlinear Dynamical System Analysis Methomentioning

confidence: 99%

A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal

Nayak

Bit

Dey

et al. 2018

Journal of Healthcare Engineering

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Electrocardiogram (ECG) signal analysis has received special attention of the researchers in the recent past because of its ability to divulge crucial information about the electrophysiology of the heart and the autonomic nervous system activity in a noninvasive manner. Analysis of the ECG signals has been explored using both linear and nonlinear methods. However, the nonlinear methods of ECG signal analysis are gaining popularity because of their robustness in feature extraction and classification. The current study presents a review of the nonlinear signal analysis methods, namely, reconstructed phase space analysis, Lyapunov exponents, correlation dimension, detrended fluctuation analysis (DFA), recurrence plot, Poincaré plot, approximate entropy, and sample entropy along with their recent applications in the ECG signal analysis.

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“…The most successful type of deep learning model is convolutional neural networks (CNNs). CNNs were first designed by Fukushima in 1980 (Desai et al, 2016). 47 However, the golden age of CNN started in the last decade.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Convolutional Neural Network Application in Biomedical Signals

Alaskar¹

2018

JCSIT

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Recent improvements in big data and machine learning have enhanced the importance of biomedical signal and image-processing research. One part of machine learning evolution is deep learning networks. Deep learning networks are designed for the task of exploiting compositional structure in data. The golden age of the deep learning network in particular convolutional neural networks (CNNs) began in 2012. CNNs have rapidly become a methodology of optimal choice for analysing biomedical signals. CNNs have been successful in detecting and diagnosing an abnormality in biomedical signals. This paper has three distinct aims. The key primary aim is to provide state of the art knowledge about how deep learning evolved and revolutionized machine learning in the past few years. Second, to critically review the application of deep learning for different biomedical signals analysis and provide a holistic overview of current works of literature. Finally, to discuss the research opportunities with deep learning algorithms in the field of study that can serve as a starting point for new researchers to identify the future research direction in a concise manner.

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Diagnosis of Multiclass Tachycardia Beats Using Recurrence Quantification Analysis and Ensemble Classifiers

Cited by 67 publications

References 50 publications

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal

Convolutional Neural Network Application in Biomedical Signals

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