An Artificial Neural Network to Localize Atrioventricular Accessory Pathways in Patients Suffering from the Wolff‐Parkinson‐White Syndrome

Dassen, W.R.M.; Mulleneers, R.G.A.; Dulk, Karel den; Smeets, Joep; Cruz, Fernando E.S.; Penn, Olaf C.; Wellens, Hein J.J.

doi:10.1111/j.1540-8159.1990.tb06892.x

Cited by 23 publications

(14 citation statements)

References 6 publications

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“…7 There has been much excitement in the scientific literature in recent years regarding artificial neural networks (ANNs) 8,9 in medicine 10 and, specifically, in cardiology applications. 7,8,[11][12][13][14][15][16][17][18][19][20] ANNs are valuable tools used in complex pattern recognition and classification tasks. They learn complex interactions among inputs and identify relations in input data that may not be apparent to human analysis.…”

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confidence: 99%

Artificial Neural Network–Based Method of Screening Heart Murmurs in Children

et al. 2001

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Background-Early recognition of heart disease is an important goal in pediatrics. Efforts in developing an inexpensive screening device that can assist in the differentiation between innocent and pathological heart murmurs have met with limited success. Artificial neural networks (ANNs) are valuable tools used in complex pattern recognition and classification tasks. The aim of the present study was to train an ANN to distinguish between innocent and pathological murmurs effectively. Methods and Results-Using an electronic stethoscope, heart sounds were recorded from 69 patients (37 pathological and 32 innocent murmurs). Sound samples were processed using digital signal analysis and fed into a custom ANN. With optimal settings, sensitivities and specificities of 100% were obtained on the data collected with the ANN classification system developed. For future unknowns, our results suggest the generalization would improve with better representation of all classes in the training data. Conclusion-We demonstrated that ANNs show significant potential in their use as an accurate diagnostic tool for the classification of heart sound data into innocent and pathological classes. This technology offers great promise for the development of a device for high-volume screening of children for heart disease.

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Artificial Neural Network–Based Method of Screening Heart Murmurs in Children

et al. 2001

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“…In cardiology, the first applications of AI were the development of self-learning neural networks applied to electrocardiography (ECG). 11 , 12 One of the earliest works in 1990 trained a self-learning neural network using 60 ECGs to localize the atrioventricular accessory pathway in patients with Wolff-Parkinson-White Syndrome by using the polarity of the delta waves as an input. 12 In a testing cohort of 25 ECGs, the algorithm correctly localized the atrioventricular pathway in 23 patients.…”

Section: Introductionmentioning

confidence: 99%

“… 11 , 12 One of the earliest works in 1990 trained a self-learning neural network using 60 ECGs to localize the atrioventricular accessory pathway in patients with Wolff-Parkinson-White Syndrome by using the polarity of the delta waves as an input. 12 In a testing cohort of 25 ECGs, the algorithm correctly localized the atrioventricular pathway in 23 patients. 12 As AI techniques developed and computing power continued to improve, applications of AI in cardiology expanded to other fields, including cardiac imaging, electrophysiology, heart failure and interventional procedures.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

Haq¹,

Chhatwal²,

Sanaka³

et al. 2022

VHRM

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Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials.

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“…6 Few reports are available on the performance of artificial neural networks for the diagnosis of myocardial infarction 6 Few reports are available on the performance of artificial neural networks for the diagnosis of myocardial infarction…”

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confidence: 99%

“…ST-T ~e g m e n t s ,~ electrogram recognition in implantable cardioverter defibrillator^,^ and localization of atrioventricular accessory pathways in patients with Wolff-Parkinson-White syndrome. 6 Few reports are available on the performance of artificial neural networks for the diagnosis of myocardial infarction…”

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confidence: 99%

Localization of Myocardial Infarction Based on Learning Vector Quantization Networks Applied to ST Elevations of the 12‐Lead ECG

Reinhardt¹,

Vesanto²,

Montonen³

et al. 1997

Noninvasive Electrocardiol

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Background: During recent years artificial neural networks have been proposed as a diagnostic tool in different fields of cardiology. Most of the studies have utilized the multilayer perceptron with backpropagation learning rule for the design of the network. As a new approach, Learning Vector Quantization (LVQ) which belongs to the class of competitive learning networks, was developed particularly for classification problems. So far there are no data available on the application of LVQ for classification tasks in cardiology. The present study aims at investigating the performance of LVQ for localization of myocardial infarction (MI) based on ST elevations in the standard 12-lead ECC.Methods: Altogether, 769 male patients (age 53 ? 7 years) with an acute MI were included into the study. Three hundred fifty-three patients (46%) presented with anterior and 41 6 patients (54%) with inferior MI based on typical changes in the standard 12-lead ECG. Standardized ST elevations in all 12 leads were used as input structure for the network. The performance of the network was studied using two different learning and test sets. The influence of the number of reference vectors and training steps on the classification accuracy for infarct location was investigated. Results:The highest classification accuracy of 88.6% for infarct location was achieved using the learning set with 66% of all patients. This setup was based on five reference vectors and 200 training steps. The best accuracies for anterior MI were higher as compared to inferior infarctions in both the test and training set. Using more than 50 reference vectors resulted in a decrease of classification accuracy due to overtraining of the network. Conclusion: Appropriately initialized and trained artificial neural networks based on LVQ give a high accuracy for localization of MI using only ST elevations of the standard 12-lead ECG. A.N.E. 1997;2(4):331-337 artificial neural networks; Learning Vector Quantization; myocardial infarctionDuring recent years the automatic interpretation of electrocardiographic data has been shown to be useful for classification tasks in cardiology.' Artifi-cia1 neural networks have been applied in different fields of cardiology, such as detection of atrial fib r i l l a t i~n ,~'~ classification of electrocardiographic

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An Artificial Neural Network to Localize Atrioventricular Accessory Pathways in Patients Suffering from the Wolff‐Parkinson‐White Syndrome

Abstract: the neural network approach can be useful in situations where causal relations between the electrocardiogram and underlying mechanism are partly undefined.

Cited by 23 publications

References 6 publications

Artificial Neural Network–Based Method of Screening Heart Murmurs in Children

Artificial Neural Network–Based Method of Screening Heart Murmurs in Children

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

Localization of Myocardial Infarction Based on Learning Vector Quantization Networks Applied to ST Elevations of the 12‐Lead ECG

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