Arrhythmia Detection Using ECG Signal: A Survey
01 Jan 2020-pp 329-341
TL;DR: Different techniques, databases used in past years for detection of arrhythmia using ECG signal becomes crucial since mortality rate has been increased due to heart diseases.
Abstract: A lot of development is going on in the area of automation in the health care sector for few years. It helps clinicians to accurately diagnose diseases. However, since mortality rate has been increased due to heart diseases, it leads to concentrate on the heart-related diseases and early, accurate diagnosis might help reduce the risk. ECG being the most commonly used technique, ECG signal processing becomes crucial. This survey covers different techniques, databases used in past years for detection of arrhythmia using ECG signal.
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01 Jun 2020TL;DR: This work proposes to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation, and outperforms other recent methods with a large margin in terms of accuracy and specificity.
Abstract: Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term Memory (LSTM) layer, is utilized to recognize arrhythmia. Indeed, the segments centered around R-peaks acquired from the previous step are fed into this network to distinguish various arrhythmias. The LSTM part of the proposed network enables modeling the relation among different heartbeats in a sequence. Experiments on the MIT-BIH databases and Creighton university ventricular tachyarrhythmia show the superiority of our proposed method on arrhythmia detection in comparison with the recent methods proposed for this problem. The performance of the proposed model on test samples is 98.93%, 99.78%, and 99.58% respectively in terms of overall accuracy, sensitivity, and specificity for tackling the problem of 4-class arrhythmia classification. Thus, it outperforms other recent methods with a large margin in terms of accuracy and specificity.
7 citations
Cites background from "Arrhythmia Detection Using ECG Sign..."
...Reference [1] is the most recent literature review of arrhythmia detection using ECG signals....
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02 Sep 2020TL;DR: The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies.
Abstract: High-quality signal processing of an electrocardiogram (ECG) is an urgent problem in present day diagnostics for revealing dangerous signs of cardiovascular diseases and arrhythmias in patients. The used methods and programs of signal analysis and classification work with the arrays of points for mathematical modeling that must be extracted from an image or recording of an electrocardiogram. The aim of this work is developing a method of extracting images of ECG signals into a one-dimensional array. An algorithm is proposed based on sequential color processing operations and improving the image quality, masking and building a one-dimensional array of points using Python tools and libraries with open access. The results of testing samples from the ECG database and comparing images before and after processing show that the signal extraction accuracy is approximately 95 %. In addition, the presented application design is simple and easy to use. The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies.
2 citations
Cites background or methods from "Arrhythmia Detection Using ECG Sign..."
...For example, such signs are used to classify the ECG signals as the beginning and wave displacement, QRS, and the period that are detected by analyzing in the time domain of the signal [1]....
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...Monitoring and timely detecting dangerous arrhythmias in a patient will help preventing the threat of stroke or sudden death from heart failure [1]....
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01 Jan 2022
TL;DR: In this article , data mining methods were introduced to diagnose and monitor health in individuals with various heart diseases, including fetal health diagnosis, arrhythmias, and machine learning data mining angiography.
Abstract: Health monitoring in humans is very important. This monitoring can be done in different people from embryonic period to adulthood. A healthy fetal will lead to a healthy baby. For this purpose, health assessment methods are used from the fetal to adulthood. One of the most common methods of assessing health at different times is to use clinical signs and data. Measuring heart rate, blood pressure, temperature, and other symptoms can help monitor health. However, there are usually errors in human predictions. Data mining is a technique for identifying and diagnosing diseases, categorizing patients in disease management, and finding patterns to diagnose patients more quickly and prevent complications. It could be a great help. Increasing the accuracy of diagnosis, reducing costs, and reducing human resources in the medical sector have been proven by researchers as the benefits of introducing data mining in medical analysis. In this paper, data mining methods will be introduced to diagnose and monitor health in individuals with various heart diseases. Heart disease will be evaluated to make the study more comprehensive, including fetal health diagnosis, arrhythmias, and machine learning data mining angiography. Attempts are made to introduce the relevant database in each disease and to evaluate the desired methods in health monitoring.
References
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TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
Abstract: In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM‘s. The approach is illustrated on a two-spiral benchmark classification problem.
8,811 citations
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TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Abstract: We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.
6,686 citations
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TL;DR: In this article, a discrete cosine transform (DCT) is defined and an algorithm to compute it using the fast Fourier transform is developed, which can be used in the area of digital processing for the purposes of pattern recognition and Wiener filtering.
Abstract: A discrete cosine transform (DCT) is defined and an algorithm to compute it using the fast Fourier transform is developed. It is shown that the discrete cosine transform can be used in the area of digital processing for the purposes of pattern recognition and Wiener filtering. Its performance is compared with that of a class of orthogonal transforms and is found to compare closely to that of the Karhunen-Loeve transform, which is known to be optimal. The performances of the Karhunen-Loeve and discrete cosine transforms are also found to compare closely with respect to the rate-distortion criterion.
4,481 citations
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TL;DR: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats and results are an improvement on previously reported results for automated heartbeat classification systems.
Abstract: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50 000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.
1,449 citations