ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition

Abdalla, Fakheraldin Y. O.; Wu, Longwen; Ullah, Hikmat; Ren, Guoyu; Noor, Alam; Zhao, Yaqin

doi:10.1007/s11760-019-01479-4

Cited by 33 publications

(15 citation statements)

References 29 publications

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“…Then, ANN was adopted to apply the feature vector using them and classify five different arrhythmia heartbeats downloaded from PhysioNet in the MIT-BIH database. The performance of the CEEMDAN and ANN was better than all existing methods, where the sensitivity (SEN) is 99.7%, specificity (SPE) is 99.9%, ACC is 99.9%, and receiver operating characteristic (ROC) is 01.0% [46].…”

Section: Annmentioning

confidence: 87%

“…Here, data used for the analysis of ECG signals are from the MIT database [45]. Abdalla et al (2019) presented that approach was developed based on the non-linearity and nonstationary decomposition methods due to the nature of the ECG signal. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to obtain intrinsic mode functions (IMFs).…”

Section: Annmentioning

confidence: 99%

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A Review Study for Electrocardiogram Signal Classification

Abdulla

Al-Ani

2020

UHD J SCI TECH

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An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.

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Section: Annmentioning

confidence: 87%

Section: Annmentioning

confidence: 99%

A Review Study for Electrocardiogram Signal Classification

Abdulla

Al-Ani

2020

UHD J SCI TECH

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“…Similarly, Abdalla [6] used ANN for classifying arrhythmia and are compared with the proposed hybrid spatial temporal feature extraction. The extracted hybrid features are fed to SAE classifier and the results obtained is tabulated in the [16], and LSTM-AE [17] that are used for comparison with the existing methods.…”

Section: Comparative Analysismentioning

confidence: 99%

“…The heart beat is composed of P wave, T wave and QRS complex that represent time domain features to process atria and ventricular depolarization and repolarisation [5]. For each of the cardiac condition, the magnitude of ECG arrhythmia is distinct from where the features are extracted and these features are used for classifying types of arrhythmia [6].In this study, 5 different types of ECG arrhythmia beats are classified as these 5 irregular waveforms are severe and would result as an heart attack, so the present research classifies Arrhythmia into Normal Sinus Rhythm (N), Left Bundle Branch Block (LBBB or L), Right Bundle Branch Block(RBBB or R),Premature Ventricular Contraction (V), and Atrial Premature Beat (A)Rhythm [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Arrhythmia Detection using a Hybrid Spatial-Temporal Feature Extraction Method and Stacked Auto Encoder

Sharmila¹

2021

IJIES

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Cardiovascular disease generally refers to conditions that involve narrowed or blocked blood vessels which can lead to a heart attack, chest pain or stroke. The rate of mortality has been increased among cardiac patients and early detection of the heart disease is important to prevent death. The heart disease can generally be identified at an early stage by analysing irregular heart beat rhythms. The occurrence of abnormal heart beats is known as arrhythmia and the expert system is introduced for detection of arrhythmia. The Electro Cardio Gram (ECG) is considered to be a standard non-invasive diagnosis for Arrhythmia detection and classification of ECG signals was performed for the arrhythmia detection by the researchers in the existing methods. The existing Long Short Term Memory (LSTM)-Auto Encoder (AE) method was failed in selecting prior features to perform feature extraction and also gave rise to overfitting problems. The high level features were need to tackle the issue occurred during data imbalance which gave rise to misclassification of ECG heartbeats. In order to overcome aforementioned issue, the proposed hybrid spatial temporal feature extraction technique extracts salient features and eliminates the exhausted handcrafted features. Initially the ECG signals are acquired from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database. The ECG signals were undergone for the pre-processing stage using butter worth filter. The segmentation of the QRS complex is an important task as the QRS spike in signal determines the abnormality in heart beat using Teager Energy Operator (TEO). The spatial features that provide locations of various natural or artificial boundaries or shapes visualize spatial data of the ECG signals. The temporal features are the time domain features that include Energy of Signal, Zero Crossing Rate, and Maximum Amplitude parameters. These parameters from both spatial and temporal feature forms a hybrid spatial temporal features that are extracted from the QRS complex and are fed to the layers of Deep Neural Network (DNN) based Stacked Auto Encoder (SAE) The hybrid spatial-temporal features determines the type of heart rhythm into 5 prominent classes of Arrhythmia.

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“…Automatically generated features (such as deep learning features) can be more informative than the expert features. In particular, there have been noticeable successes in the problem of automatic recognition of cardiac diseases using sparse representation of ECG [17], using deep learning generated features [18], [19], combination of artificial intelligence methods and linear and non-linear decomposition [20], different feature extraction methods with machine learning algorithms [21], different end-to-end ECG deep learning classifiers, e.g. [23], [24], etc.…”

Section: Introductionmentioning

confidence: 99%

LUDB: A New Open-Access Validation Tool for Electrocardiogram Delineation Algorithms

et al. 2020

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We report Lobachevsky University Database (LUDB) of ECG signals, an open tool for validating ECG delineation algorithms, that is superior to the existing publicly available data bases in several aspects. LUDB contains 200 recordings of 10-second 12-lead electrocardiograms (ECG) from different subjects, representative of a variety of signal morfologies. The boundaries and peaks of QRS complexes and P and T waves are manually annotated by cardiologists for all recordings and independently for each lead, and all records received an expert classification by abnormalities. We present a case study for the recently proposed wavelet-based algorithm and the broadly used ecg-kit tool, and demonstrate the advantage of multi-lead ECG data analysis. LUDB contributes to the diversity of public databases employed in developing and validating novel ECG analysis algorithms, including the most advanced based on deep learning neural networks.

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ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition

Cited by 33 publications

References 29 publications

A Review Study for Electrocardiogram Signal Classification

A Review Study for Electrocardiogram Signal Classification

Multi-Class Arrhythmia Detection using a Hybrid Spatial-Temporal Feature Extraction Method and Stacked Auto Encoder

LUDB: A New Open-Access Validation Tool for Electrocardiogram Delineation Algorithms

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