Morphological and Temporal ECG Features for Myocardial Infarction Detection Using Support Vector Machines

Arenas, Wilson J.; Sotelo, S; Zequera, Martha; Altuve, Miguel

doi:10.1007/978-3-030-30648-9_24

Cited by 4 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tree-based ML algorithmic models report high accuracy like 97.88% for Random Forest (RF), 98.23% for Extreme Gradient Boosting (XGBoost), and 98.03% for Light Gradient Boosting Machine (LightGBM) and outperform other ML algorithms, and their performance is comparable to the highend DL architectures [2]. Apart from tree-based algorithms, other ML algorithms like Support Vector Machine (SVM) give better accuracy like 96.67% with morphological & temporal features [17], 98.33% with 220 features [18] & 100% [19], along with grasshopper optimization algorithm, 99.9% for cubic models & 99.8% for a quadratic model [20], 95.1% for Adaptive boosting (AdaBoost) [17] algorithm, and 94.9% for Logistic Regression [21].…”

Section: Literature Surveymentioning

confidence: 99%

Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

S.,

Narayanan

2023

CMC

View full text Add to dashboard Cite

Machine Learning (ML) and Deep Learning (DL) technologies are revolutionizing the medical domain, especially with Electrocardiogram (ECG), by providing new tools and techniques for diagnosing, treating, and preventing diseases. However, DL architectures are computationally more demanding. In recent years, researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches, say for example, combining the convolutional layer blocks of Convolution Neural Networks (CNNs) into ML algorithms such as Extreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) resulting in CNN-XGBoost and CNN-KNN, respectively. However, these approaches are homogenous in the sense that they use a fixed Activation Function (AFs) in the sequence of convolution and pooling layers, thereby limiting the ability to capture unique features. Since various AFs are readily available and each could capture unique features, we propose a Convolutionbased Heterogeneous Activation Facility (CHAF) which uses multiple AFs in the convolution layer blocks, one for each block, with a motivation of extracting features in a better manner to improve the accuracy. The proposed CHAF approach is validated on PTB and shown to outperform the homogeneous approaches such as CNN-KNN and CNN-XGBoost. For PTB dataset, proposed CHAF-KNN has an accuracy of 99.55% and an F1 score of 99.68% in just 0.008 s, outperforming the state-of-the-art CNN-XGBoost which has an accuracy of 99.38% and an F1 score of 99.32% in 1.23 s. To validate the generality of the proposed CHAF, experiments were repeated on MIT-BIH dataset, and the proposed CHAF-KNN is shown to outperform CNN-KNN and CNN-XGBoost.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

S.,

Narayanan

2023

CMC

View full text Add to dashboard Cite

show abstract

“…The recent research on ECG classification computer-aid systems is based on new techniques of artificial intelligence. Various types of these techniques have been investigated and analyzed for this purpose and other purposes [ 22 , 23 ], such as decision trees [ 24 ], random forest (RF) [ 25 , 26 , 27 ], support vector machine (SVM) [ 28 , 29 ], k-nearest neighbor (KNN) [ 30 ], the hybrid FFPSONeural network classifier [ 31 ], in addition to other methods, such as [ 32 , 33 , 34 ]. In [ 35 ], the authors incorporated two categories, normal and MI, into their investigation.…”

Section: Literature Overviewmentioning

confidence: 99%

A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection

Sraitih

Jabrane²,

Hassani³

2022

JCM

View full text Add to dashboard Cite

An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we investigated an automatic ECG myocardial infarction detection system and presented a new approach to evaluate its robustness and durability performance in classifying the myocardial infarction (with no feature extraction) under different noise types. We employed three well-known supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), and tested the performance and robustness of these techniques in classifying normal (NOR) and myocardial infarction (MI) using real ECG records from the PTB database after normalization and segmentation of the data, with a suggested inter-patient paradigm separation as well as noise from the MIT-BIH noise stress test database (NSTDB). Finally, we measured four metrics: accuracy, precision, recall, and F1-score. The simulation revealed that all of the models performed well, with values of over 0.50 at lower SNR levels, in terms of all the metrics investigated against different types of noise, indicating that they are encouraging and acceptable under extreme noise situations are are thus considered sustainable and robust models for specific forms of noise. All of the methods tested could be used as ECG myocardial infarction detection tools in real-world practice under challenging circumstances.

show abstract

Linear and Nonlinear Features for Myocardial Infarction Detection Using Support Vector Machine on 12-Lead ECG Recordings

et al. 2020

View full text Add to dashboard Cite

Morphological and Temporal ECG Features for Myocardial Infarction Detection Using Support Vector Machines

Cited by 4 publications

References 15 publications

Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection

Linear and Nonlinear Features for Myocardial Infarction Detection Using Support Vector Machine on 12-Lead ECG Recordings

Contact Info

Product

Resources

About