Analysis of features for myocardial infarction and healthy patients based on wavelet

Pereira, Hope; Daimiwal, Nivedita

doi:10.1109/casp.2016.7746158

Cited by 9 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While in [26] author used Butterworth filter without specifying frequency and order of the filter. Low-pass filtering is also been employed using moving average filters in [19,26]. After pre-processing the ECG signals, feature extraction is done to find the MI and Ischemia.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly Wavelet Transform, Fourier Transform, Principal Component Analysis (PCA) and reconstructed phase space (RPS) theory can be used in feature extraction [47]. Features extraction from wavelet transform, wavelet db4 [31], db6 [30] and db9 [19] is also useful in the identification of MI. EMD is used for extraction of feature in [31].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Roopa¹,

Harish²

2020

IJIMAI

View full text Add to dashboard Cite

Cardio-vascular diseases are one of the foremost causes of mortality in today's world. The prognosis for cardiovascular diseases is usually done by ECG signal, which is a simple 12-lead Electrocardiogram (ECG) that gives complete information about the function of the heart including the amplitude and time interval of P-QRS-T-U segment. This article recommends a novel approach to identify the location of thrombus in culprit artery using the Information Fuzzy Network (IFN). Information Fuzzy Network, being a supervised machine learning technique, takes known evidences based on rules to create a predicted classification model with thrombus location obtained from the vast input ECG data. These rules are well-defined procedures for selecting hypothesis that best fits a set of observations. Results illustrate that the recommended approach yields an accurateness of 92.30%. This novel approach is shown to be a viable ECG analysis approach for identifying the culprit artery and thus localizing the thrombus.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Roopa¹,

Harish²

2020

IJIMAI

View full text Add to dashboard Cite

show abstract

“…Different kinds of features were explored: morphological features (such as ST-elevation value, QRS duration, T wave amplitude, Q wave amplitude etc.) [4,10,14], wavelet transform related features (coefficients) [5,11,19], empirical mode decomposition features [1] and so on. Compared with other features, morphological features are explainable but very sensitive to ECG delineation results.…”

Section: Introductionmentioning

confidence: 99%

Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection

Yang

Rocher

Moceri

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Automatic analysis of electrocardiograms with adequate explainability is a challenging task. Many deep learning based methods have been proposed for automatic classification of electrocardiograms. However, very few of them provide detailed explainable classification evidence. In our study, we explore explainable ECG classification through explicit decomposition of single-beat (median-beat) ECG signal. In particular, every single-beat ECG sample is decomposed into five subwaves and each subwave is parameterised by a Frequency Modulated Moebius. Those parameters have explicit meanings for ECG interpretation. In stead of solving the optimisation problem iteratively which is timeconsuming, we make use of an Cascaded CNN network to estimate the parameters for each single-beat ECG signal. Our preliminary results show that with appropriate position regularisation strategy, our neural network is able to estimate the subwave for P, Q, R, S, T events and maintain a good reconstruction accuracy (with R2 score 0.94 on test dataset of PTB-XL) in a unsupervised manner. Using the estimated parameters, we achieve very good classification and generalisation performance on myocardial infarction detection on four different datasets. The features of high importance are in accordance with clinical interpretations.

show abstract

“…[ 7 ] In 2016, Pereira and Daimiwal presented a method for analyzing wavelet transform-based features for the diagnosis of MI. [ 8 ] In this study, the 21-lead ECG signal was decomposed using a wavelet transform, and then multiple features were extracted from different subbands. It has been observed that the statistical features extracted from different ECG subbands were differed for the two groups of healthy and diseased and have been used for classification.…”

Section: Introductionmentioning

confidence: 99%

“…It has been observed that the statistical features extracted from different ECG subbands were differed for the two groups of healthy and diseased and have been used for classification. [ 8 ] In 2015, Bhaska et al . presented a method for analyzing and diagnosing MI using support vector machine (SVM) algorithms and artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of myocardial infarction with support vector machine classifier using grasshopper optimization algorithm

2021

View full text Add to dashboard Cite

Background: Providing a noninvasive, rapid, and cost-effective approach to diagnose of myocardial infarction (MI) is essential in the early stages of electrocardiogram (ECG) signaling. In this article, we proposed the new optimization method for support vector machine (SVM) classifier to MI classification. Methods: After preprocessing ECG signal and noise removal, three features such as Q-wave integral, T-wave integral, and QRS-complex integral have been extracted in this study. After that, different statistical tests have evaluated the matrix of these features. To more accurately detect and classify the MI disease, optimizing the SVM classification parameters using the grasshopper optimization algorithm (GOA) was first used in this study (that called SVM-GOA). Results: After applying the GOA on the SVM classifier for all three kernels, the final results of MI detection for sensitivity, specificity, and accuracy were 100% ± 0%, 100% ± 0%, and 100% ± 0%, respectively. The final results of different MI types' classification after applying the GOA on SVM for polynomial kernel were obtained 100% ± 0%, 97.37% ± 0%, and 94.2% ± 0.2% for sensitivity and specificity and accuracy, respectively. However, the results of both linear and RBF kernels that were used for the SVM classifier method have also shown a significant increase after using GOA. Conclusion: This article's results show the highly desirable effect of applying a GOA to optimize different kernel parameters used in the SVM classifier for accurate detection and classification of MI. The proposed algorithm's final results show that the proposed system has a relatively higher performance than other previous studies.

show abstract

Analysis of features for myocardial infarction and healthy patients based on wavelet

Cited by 9 publications

References 11 publications

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection

Detection and classification of myocardial infarction with support vector machine classifier using grasshopper optimization algorithm

Contact Info

Product

Resources

About