Reaction-diffusion informed approach to determine myocardial ischemia using stochastic in-silico ECGs and CNNs

Loeffler, Shane; Starobin, Joseph M.

doi:10.1016/j.compbiomed.2021.104635

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…In this section, diverse proposals from different diseases are grouped into the same table ( Table 18 ), for a total of seven proposals. For example, Dai, Hwang, and Tseng [ 202 ] focused on classifying cardiomyopathy; Smole et al [ 204 ] considered hypertrophic cardiomyopathy; Elias et al [ 200 ] considered valvular heart disease; Duffy et al [ 201 ] considered left ventricular hypertrophy; Loeffler and Starobin [ 203 ] considered ischemic heart disease; Li et al [ 199 ] considered chronic heart disease; and Pathan et al [ 205 ] considered stroke.…”

Section: Examples Of Cvd Detection Utilizing Machine Learningmentioning

confidence: 99%

“… 3 [ 46 ] Artif Intell Med. 1 [ 38 , 68 , 129 , 131 , 150 , 157 , 180 , 203 , 204 ] Comput Biol Med. 9 [ 41 , 179 ] Comput Electr Eng 2 [ 24 , 33 , 42 , 141 , 151 , 195 , 202 ] Comput Methods Programs Biomed 7 [ 98 ] Evol Intel 1 [ 132 ] Evol Syst.…”

Section: Table A1mentioning

confidence: 99%

“…Table 12 shows a summary of the disease classification, which led to the determination of what type of machine learning techniques have been used to detect, predict, or monitor CVD. 10 (11.90%) [37,45,46, 25 (28.57%) [189][190][191][192][193][194] 6 (7.14%) [195][196][197][198] 4 (4.76%) [199][200][201][202][203][204][205] 7 (8.33%)…”

Section: Examples Of Cvd Detection Utilizing Machine Learningmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

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According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.

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Section: Examples Of Cvd Detection Utilizing Machine Learningmentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

Section: Examples Of Cvd Detection Utilizing Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

View full text Add to dashboard Cite

show abstract

“…In a recent explosion in popularity, NNs are being widely used in cardiac EP, from predicting AF or congestive heart failure from heart rate variability biomarkers, to predicting the presence of acute myocardial infarction from an ECG. [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] NN models have been preferred in these studies because they can practically operate on any data type, and they inherently learn feature importance and the relationships between features. These studies have demonstrated the advantages of NN in handling raw ECG data, raw LGE-MRI signals, electroanatomic mapping (EAM) data, and overall, in learning from high dimensional complex data.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Primer on Machine Learning in Electrophysiology

Loeffler

Trayanova

2023

Arrhythm Electrophysiol Rev

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Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies.

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“…Some scholars proposed a lot of different machine learning-based optimization approaches to implement some optimization problems [ 25 – 30 ]. KAdam and Mahajan [ 31 ] tried to optimize the cutting temperature prediction model using GA to optimize the objective function, and the outcomes acquired through experimental test are likewise similar to the outcomes of GA. El et al [ 32 ] compared the optimization models between PSO and GA.…”

Section: Introductionmentioning

confidence: 99%

Machine Leaning-Based Optimization Algorithm for Myocardial Injury under High-Intensity Exercise in Track and Field Athletes

Li¹

2022

Computational Intelligence and Neuroscience

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In order to train at high-intensity, athletics can again cause varying degrees of myocardial damage. Evaluating the balance between exercise myocardial injury and exercise intensity should actively prevent myocardial injury caused by high-intensity athletic training. In this paper, an intelligent optimization algorithm is used to investigate the degree of myocardial injury. The basic idea is to define the measured data and the output of the numerical model as an objective function of the structural parameters, to obtain the structural parameters by finding ways to continuously optimize the objective function to be close to the observed values, and to identify the injury based on the changes in these parameters before and after myocardial injury. The objective function can be defined in various ways, and the myocardial injury optimization algorithm can be chosen. In order to obtain the best computational results, numerical simulations of damage identification are performed using the objective function and three machine learning-based optimization algorithms. The computational results show that the combination of the objective function and the machine learning algorithms provides good accuracy and computational speed in identifying myocardial injury.

show abstract

Reaction-diffusion informed approach to determine myocardial ischemia using stochastic in-silico ECGs and CNNs

Cited by 5 publications

References 31 publications

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Primer on Machine Learning in Electrophysiology

Machine Leaning-Based Optimization Algorithm for Myocardial Injury under High-Intensity Exercise in Track and Field Athletes

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