Myocardial infarction detection and heart patient identity verification

Nidhyananthan, S. Selva; Saranya, S.; Kumari, R. Shantha Selva

doi:10.1109/wispnet.2016.7566308

Cited by 8 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early computer-aided programs often use manually extracted morphological features to compare the threshold [29,30], or the morphological features extracted manually are classified by the machine learning method [10,[31][32][33][34][35]. The diagnosis process can usually be divided into four steps [1], followed by pretreatment, waveform detection, feature extraction, and classification, of which the most critical step is feature extraction.…”

Section: Traditional Methodsmentioning

confidence: 99%

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Zhang

Liu

2021

Micromachines

View full text Add to dashboard Cite

Heart diseases such as myocardial ischemia (MI) are the main causes of human death. The prediction of MI and arrhythmia is an effective method for the early detection, diagnosis, and treatment of heart disease. For the rapid detection of arrhythmia and myocardial ischemia, the electrocardiogram (ECG) is widely used in clinical diagnosis, and its detection equipment and algorithm are constantly optimized. This paper introduces the current progress of portable ECG monitoring equipment, including the use of polymer material sensors and the use of deep learning algorithms. First, it introduces the latest portable ECG monitoring equipment and the polymer material sensor it uses and then focuses on reviewing the progress of detection algorithms. We mainly introduce the basic structure of existing deep learning methods and enumerate the internationally recognized ECG datasets. This paper outlines the deep learning algorithms used for ECG diagnosis, compares the prediction results of different classifiers, and summarizes two existing problems of ECG detection technology: imbalance of categories and high computational overhead. Finally, we put forward the development direction of using generative adversarial networks (GAN) to improve the quality of the ECG database and lightweight ECG diagnosis algorithm to adapt to portable ECG monitoring equipment.

show abstract

Section: Traditional Methodsmentioning

confidence: 99%

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Zhang

Liu

2021

Micromachines

View full text Add to dashboard Cite

show abstract

“…In [15], BW was eliminated using the low frequencies derivative-based filter. On the other hand [16,17] employed moving average filtering to eliminate the BW. Further studies proposed in [30] employed median filter to estimate BW.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Using K value as 5, KNN classifier was used in [18] to classify the signal using description found in [46]. Support Vector Machines (SVMs) have been widely used for classification of ischemia and MI with linear [17,18,24,33], Radial Basis Function (RBF) [18,21,25,35], polynomial [11] and exponential chi-squared kernel functions [11]. Review in [47] used artificial neural networks, fuzzy logic, rough set theory, decision trees, genetic and hybrid algorithms for classification of various heart diseases.…”

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

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

“…In addition, the RR interval utilized to authenticate the ECG signal. [ 15 ] In addition, Sharma et al . represented a novel technique on multiscale energy and Eigen space approach to detect and localize MI from multi-lead ECG.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Morphological Features of Electrocardiogram Signals for Diagnosing of Myocardial Infarction Using Classification-Based Feature Selection

Mahmoudinejad

Safdarian

2021

Journal of Medical Signals &Amp; Sensors

View full text Add to dashboard Cite

Background: Cardiovascular disease (CVD) is the first cause of world death, and myocardial infarction (MI) is one of the five primary disorders of CVDs which the patient electrocardiogram (ECG) analysis plays a dominant role in MI diagnosis. This research aims to evaluate some extracted features of ECG data to diagnose MI. Methods: In this paper, we used the Physikalisch-Technische Bundesanstalt database and extracted some morphological features, such as total integral of ECG, integral of the T-wave section, integral of the QRS complex, and J-point elevation from a cycle of normal and abnormal ECG waveforms. Since the morphology of healthy and abnormal ECG signals is different, we applied integral to different ECG cycles and intervals. We executed 100 of iterations on a 10-fold and 5-fold cross-validation method and calculated the average of statistical parameters to show the performance and stability of four classifiers, namely logistic regression (LR), simple decision tree, weighted K-nearest neighbor, and linear support vector machine. Furthermore, different combinations of proposed features were employed as a feature selection procedure based on classifier's performance using the aforementioned trained classifiers. Results: The results of our proposed method to diagnose MI utilizing all the proposed features with an LR classifier include 90.37%, 94.87%, and 86.44% for accuracy, sensitivity, specificity, respectively. Also, we calculated the standard deviation value for the accuracy of 0.006. Conclusion: Our proposed classification-based method successfully classified and diagnosed MI using different combinations of presented features. Consequently, all proposed features are valuable in MI diagnosis and are praiseworthy for future works.

show abstract

Myocardial infarction detection and heart patient identity verification

Cited by 8 publications

References 6 publications

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

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

Evaluating Morphological Features of Electrocardiogram Signals for Diagnosing of Myocardial Infarction Using Classification-Based Feature Selection

Contact Info

Product

Resources

About