Detection of ST segment deviation episodes in ECG using KLT with an ensemble neural classifier

Afsar, Fayyaz A.; Arif, Muhammad; Yang, Jie

doi:10.1088/0967-3334/29/7/004

Cited by 29 publications

(16 citation statements)

References 29 publications

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“…If we can predict an ischemic syndrome as early as possible, we will be able to prevent more severe heart disease such as myocardial infarction [8,9]. …”

Section: Discussionmentioning

confidence: 99%

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Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

2012

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BackgroundMyocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome in electrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophic disease. To this end, we propose a new method, which employs wavelets and simple feature selection.MethodsFor training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in 90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method based on the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used for differentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalized and signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point. We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiers to those features.ResultsWe evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods. Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemic ST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively. The SVM classifier detects 355 ischemic ST episodes.ConclusionsWe proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removing baseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and feature extraction from morphology of ECG waveforms explicitly. It was shown that the number of selected features were sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposed KDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require any numerical values of the parameters to be supplied in advance. In the case of the SVM classifier, one has to select a single parameter.

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“…If we can predict an ischemic syndrome as early as possible, we will be able to prevent more severe heart disease such as myocardial infarction [8,9]. …”

Section: Discussionmentioning

confidence: 99%

“…Maglaveras et al used neural network optimized with a backpropagation algorithm [8]. Afsar et al used Karhunen-Loève transform to find feature values, and classified an input ECG by using a neural network [9]. Papaloukas et al used artificial neural network which was trained by Bayesian regularization method [10].…”

Section: Introductionmentioning

confidence: 99%

Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

2012

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“…Heuristic-based methods using different transforms have been proposed as QRS detection techniques in [34][35][36][37]. The best Sensitivity of 99.95% is achieved by the DWT based windowing method presented by [38][39][40][41]. A delineation algorithm [42] in conjunction with the DWT based windowing method has outperformed QRS, P, and T-wave detection evaluated on multiple databases with a Sensitivity of 99.84%.…”

Section: ) Traditional Signal Processing 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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“…One of the earliest introductions of machine learning into cardiovascular medicine involved reading and interpreting ECG tracings, a technology that has now been incorporated into everyday clinical practice 18 and has benefited from the development of convolutional neural networks that allow detection and classification of arrhythmias. [19][20][21] Machine learning has since been applied to various tasks in cardiovascular and thoracic imaging including segmentation, characterization, quantification, lung nodule detection and measurement, and lung cancer prognosis and treatment. Many of these applications have been consolidated in prior reviews, [22][23][24] and we provide a survey of those relevant to Architecture of a cascaded system of multiple neural networks, each building upon the outputs of a preceding network.…”

Section: Applications In Cardiovascular and Thoracic Imagingmentioning

confidence: 99%

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging

Retson

Besser

Sall³

et al. 2019

Journal of Thoracic Imaging

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Advances in technology have always had the potential and opportunity to shape the practice of medicine, and in no medical specialty has technology been more rapidly embraced and adopted than radiology. Machine learning and deep neural networks promise to transform the practice of medicine, and, in particular, the practice of diagnostic radiology. These technologies are evolving at a rapid pace due to innovations in computational hardware and novel neural network architectures. Several cutting-edge postprocessing analysis applications are actively being developed in the fields of thoracic and cardiovascular imaging, including applications for lesion detection and characterization, lung parenchymal characterization, coronary artery assessment, cardiac volumetry and function, and anatomic localization. Cardiothoracic and cardiovascular imaging lies at the technological forefront of radiology due to a confluence of technical advances. Enhanced equipment has enabled computed tomography and magnetic resonance imaging scanners that can safely capture images that freeze the motion of the heart to exquisitely delineate fine anatomic structures. Computing hardware developments have enabled an explosion in computational capabilities and in data storage. Progress in software and fluid mechanical models is enabling complex 3D and 4D reconstructions to not only visualize and assess the dynamic motion of the heart, but also quantify its blood flow and hemodynamics. And now, innovations in machine learning, particularly in the form of deep neural networks, are enabling us to leverage the increasingly massive data repositories that are prevalent in the field. Here, we discuss developments in machine learning techniques and deep neural networks to highlight their likely role in future radiologic practice, both in and outside of image interpretation and analysis. We discuss the concepts of validation, generalizability, and clinical utility, as they pertain to this and other new technologies, and we reflect upon the opportunities and challenges of bringing these into daily use.

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Detection of ST segment deviation episodes in ECG using KLT with an ensemble neural classifier

Cited by 29 publications

References 29 publications

Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

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

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging

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