Spectral entropy and deep convolutional neural network for ECG beat classification

Asgharzadeh-Bonab, Akbar; Amirani, Mehdi Chehel; Mehri, Alaeddin

doi:10.1016/j.bbe.2020.02.004

Cited by 48 publications

(26 citation statements)

References 42 publications

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“…These recordings were digitized at 360 samples per second per channel with 11bit resolution over a 10 mV range. Also, the MIT-BIH Arrhythmia database contains complex combinations of rhythm, morphological variation and noise that can be expected to provide multiple challenges for arrhythmia analysis [18,23,24].…”

Section: Data Setsmentioning

confidence: 99%

A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

Sumesh¹,

Yearwood²,

Huda³

et al. 2022

Computers, Materials &Amp; Continua

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Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global training methodology against an individual training methodology and identifying a gap between them. We provide our investigation and comparative study on these methods and model with standard classification techniques with basic morphological features and Heart Rate Variability (HRV) that may aid real time application. This approach helps in analyzing and evaluating the performance of different techniques and can suggests adoption of a best model identification with efficient technique and efficient attribute set for real-time systems.

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Section: Data Setsmentioning

confidence: 99%

A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

Sumesh¹,

Yearwood²,

Huda³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…It has a wide variety of applications such as biometrics authentication, object detection, classification, compression, image classification, and other computer vision related technology fields. Deep learning has great potential of applications in cardiology such as ECG arrhythmia detection with Deep-CNN [71], [72], [74], [76], [77], [79], [80], Robust Deep Dictionary Language (RDDL) [73], Deep Brief Network with Restricted Boltzmann Machine (DBN+RBM) [75] and Deep Neural Network (DNN) [78]. MI detection is performed with Deep-CNN [81] and Deep Neural Network (DNN) [82] while detecting heartbeats is performed by DNN in [83].…”

Section: ) Traditional Ecg Classification Approachesmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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“…In this paper, ECG classification using a CNN is presented to address these shortcomings. CNNs are a type of hierarchical artificial neural networks (ANNs) [ 19 , 20 ] that use downsampling and convolutional layers to alternately mimic the human visual cortex cells.…”

Section: Introductionmentioning

confidence: 99%

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing

Mohana

Yakkala

Vimalnath³

et al. 2022

Journal of Healthcare Engineering

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Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.

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Spectral entropy and deep convolutional neural network for ECG beat classification

Cited by 48 publications

References 42 publications

A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing

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