ECG Heartbeat Classification Using Ensemble of Efficient Machine Learning Approaches on Imbalanced Datasets

Ahamed, Md. Atik; Hasan, Kazi Amit; Monowar, Khan Fashee; Mashnoor, Nowfel; Hossain, Md. Ali

doi:10.1109/icaict51780.2020.9333534

Cited by 20 publications

(7 citation statements)

References 17 publications

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“…In [48] and ensemble learning approach was applied to the same ECG dataset which is used in this paper and another much smaller data source with the aim of improving prediction accuracy. The system described is not fully automated since it requires careful tuning while it relies on grid-search to tune an SVM from the ensemble.…”

Section: Results For Ecg Forecastsmentioning

confidence: 99%

VAMPIRE: vectorized automated ML pre-processing and post-processing framework for edge applications

Daher

Ferrari²,

Muselli

et al. 2022

Computing

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Machine learning techniques aim to mimic the human ability to automatically learn how to perform tasks through training examples. They have proven capable of tasks such as prediction, learning and adaptation based on experience and can be used in virtually any scientific application, ranging from biomedical, robotic, to business decision applications, and others. However, the lack of domain knowledge for a particular application can make feature extraction ineffective or even unattainable. Furthermore, even in the presence of pre-processed datasets, the iterative process of optimizing Machine Learning parameters, which do not translate from one domain to another, maybe difficult for inexperienced practitioners. To address these issues, we present in this paper a Vectorized Automated ML Pre-processIng and post-pRocEssing framework, approximately named (VAMPIRE), which implements feature extraction algorithms capable of converting large time-series recordings into datasets. Also, it introduces a new concept, the Activation Engine, which is attached to the output of a Multi Layer Perceptron and extracts the optimal threshold to apply binary classification. Moreover, a tree-based algorithm is used to achieve multi-class classification using the Activation Engine. Furthermore, the internet of things gives rise to new applications such as remote sensing and communications, so consequently applying Machine Learning to improve operation accuracy, latency, and reliability is beneficial in such systems. Therefore, all classifications in this paper were performed on the edge in order to reach high accuracy with limited resources. Moreover, forecasts were applied on three unrelated biomedical datasets, and on two other pre-processed urban and activity detection datasets. Features were extracted when required, and training and testing were performed on the Raspberry Pi remotely, where high accuracy and inference speed were achieved in every experiment. Additionally, the board remained competitive in terms of power consumption when compared with a laptop which was optimized using a Graphical Processing Unit.

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Section: Results For Ecg Forecastsmentioning

confidence: 99%

VAMPIRE: vectorized automated ML pre-processing and post-processing framework for edge applications

Daher

Ferrari²,

Muselli

et al. 2022

Computing

View full text Add to dashboard Cite

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“…Ahmed et al [ 29 ] proposed an ensemble of different machine learning models, including a kNN, decision tree, artificial neural network (ANN), support vector machine (SVM), and LSTM. The hard voting method was used for ensembling.…”

Section: Related Workmentioning

confidence: 99%

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets

Sattar,

Mumtaz,

Qadir

et al. 2024

Sensors

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ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

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“…This paper concludes that ConvXGB shows promise in monitoring patients and identifying various heart diseases and severe CVD syndromes, such as myocardial infarction and arrhythmia. P. Ahmed et al [13]. Achieved accurate heartbeat classification using machine learning on enriched ECG datasets (PTB and MIT-BIH Arrhythmia Diagnostic ECG).…”

Section: Related Workmentioning

confidence: 99%

Wavelet Scattering Transform for ECG Cardiovascular Disease Classification

D. S. Aabdalla,

Vasumathi

2024

IJAIA

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Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this field is increasingly on prediction, with a growing dependence on machine learning techniques. This study aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet database by employing machine learning (ML). The study proposed several multi-class classification models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral features, and wavelet scattering, to extract features and capture unique characteristics from the ECG dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease. Furthermore, it could prove to be a valuable resource for future medical research projects aimed at improving the diagnosis and treatment of cardiovascular diseases.

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ECG Heartbeat Classification Using Ensemble of Efficient Machine Learning Approaches on Imbalanced Datasets

Cited by 20 publications

References 17 publications

VAMPIRE: vectorized automated ML pre-processing and post-processing framework for edge applications

VAMPIRE: vectorized automated ML pre-processing and post-processing framework for edge applications

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets

Wavelet Scattering Transform for ECG Cardiovascular Disease Classification

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