A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection

Liu, Bin; Liu, Jikui; Wang, Guoqing; Huang, Kun; Fan, Li; Zheng, Yang; Luo, Youxi; Zhou, Fengfeng

doi:10.1016/j.compbiomed.2014.08.010

Cited by 112 publications

(38 citation statements)

References 25 publications

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“…Moreover, the methods suggested in [5,[56][57][58][59] used ECG recordings of the multiple leads. However, our method uses only lead-2 ECG recordings, which makes our method less complex than multiple leads methods.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, they proposed threshold based classifier and achieved 97.6% classification accuracy. In [5], an algorithm based on the parametrization of ECG signal is developed. In this algorithm, a 20th order polynomial is fitted with the ECG signal.…”

Section: Discussionmentioning

confidence: 99%

“…Manual identification of the changes in the ECG signals is a difficult task. Only 82% ST-segment elevation in MI subjects may be recognised by the experienced cardiologists [5]. Therefore, an automated identification system for MI patients is needed to facilitate The organization of the remaining sections of the paper is as follows: the dataset used, preprocessing and segmentation of the ECG signals into beats, FAWT, SEnt, and classification methods are provided in Section 2.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Kumar

Pachori

Acharya

2017

Entropy

121

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Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification of the changes introduced by MI is a time-consuming and tedious task, and there is also a possibility of misinterpretation of the changes in the ECG. Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. First, the segmentation of ECG signals into beats is performed. Then, FAWT is applied to each ECG beat, which decomposes them into subband signals. Sample entropy (SEnt) is computed from these subband signals and fed to the random forest (RF), J48 decision tree, back propagation neural network (BPNN), and least-squares support vector machine (LS-SVM) classifiers to choose the highest performing one. We have achieved highest classification accuracy of 99.31% using LS-SVM classifier. We have also incorporated Wilcoxon and Bhattacharya ranking methods and observed no improvement in the performance. The proposed automated method can be installed in the intensive care units (ICUs) of hospitals to aid the clinicians in confirming their diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Kumar

Pachori

Acharya

2017

Entropy

121

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“…However, simply joining features together easily generates redundant information that probably impacts the predictive performance. To validate whether there is redundant information in the joined features, we further applied three well-established feature reduction algorithms: MRMD (Maximal Relevance and Maximal Distance)36, RFE (Recursive Feature Elimination)37, and FSDI (Feature Selection based on Discernibility and Independence of a feature)38, to remove the redundant features from the joined NPPS features, respectively. Their results are presented in Table 2 as well.…”

Section: Resultsmentioning

confidence: 99%

Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine

Xing

Guo

et al. 2017

Sci Rep

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N6-methyladenosine (m6A) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear translocation and translation process. Numerous experiments have been done to successfully characterize m6A sites within sequences since high-resolution mapping of m6A sites was established. However, as the explosive growth of genomic sequences, using experimental methods to identify m6A sites are time-consuming and expensive. Thus, it is highly desirable to develop fast and accurate computational identification methods. In this study, we propose a sequence-based predictor called RAM-NPPS for identifying m6A sites within RNA sequences, in which we present a novel feature representation algorithm based on multi-interval nucleotide pair position specificity, and use support vector machine classifier to construct the prediction model. Comparison results show that our proposed method outperforms the state-of-the-art predictors on three benchmark datasets across the three species, indicating the effectiveness and robustness of our method. Moreover, an online webserver implementing the proposed predictor has been established at http://server.malab.cn/RAM-NPPS/. It is anticipated to be a useful prediction tool to assist biologists to reveal the mechanisms of m6A site functions.

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“…12-lead ECG signals were derived from 20 normal and 20 MI subjects and the reported sensitivity was 85% for the detection of MI subjects. Liu et al [88] fitted a given ECG signal with a 20th order polynomial function to derive new ECG features. They reported 94.4% classification accuracy with J48 decision tree model for the diagnosis of MI.…”

Section: Discussionmentioning

confidence: 99%

Myocardial infarction detection using artificial intelligence and nonlinear features based on synthesis of the standard 12-lead and Frank XYZ leads

Zeng

Lin

Yuan

2020

Preprint

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Nowadays cardiovascular diseases ( CVD ) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention of CVD . Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgment and several electrocardiographic (ECG) or vectorcardiographic ( VCG ) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. This vector is decomposed into a series of proper rotation components ( PRCs ) by using the intrinsic time-scale decomposition ( ITD ) method. Second, four levels discrete wavelet transform ( DWT ) is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies ( db3 ) wavelet function is selected as reference variable for analysis. Third, phase space of the reference variable is reconstructed based on db3 , in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional ( 3D ) phase space reconstruction ( PSR ) together with Euclidean distance (ED) has been utilized to derive features. Fourth, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 98.20 % . The result verifies the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

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A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection

Cited by 112 publications

References 25 publications

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine

Myocardial infarction detection using artificial intelligence and nonlinear features based on synthesis of the standard 12-lead and Frank XYZ leads

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