Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach

Background Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-learning neural networks (NNs) that are specialized...

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Vydáno v:Biomedical engineering online Ročník 18; číslo 1; s. 15 - 17
Hlavní autoři: Sbrollini, Agnese, De Jongh, Marjolein C., Ter Haar, C. Cato, Treskes, Roderick W., Man, Sumche, Burattini, Laura, Swenne, Cees A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 12.02.2019
Springer Nature B.V
BMC
Témata:
Acute coronary syndromes
Algorithms
Automation
Biomaterials
Biomedical Engineering and Bioengineering
BioMedical Engineering and the Heart
Biomedical Engineering/Biotechnology
Biotechnology
Change detection
Complexity
Congestive heart failure
Constructive algorithm
Deep learning
Echocardiography
EKG
Electrocardiography
Engineering
Heart failure
Heart rate
Infarction
Integrals
Ischemia
Myocardial infarction
Neural networks
Optimization
Pathology
Patients
Physiology
Serial electrocardiography
Vectorcardiography
Ventricle
Deep learning
Constructive algorithm
Vectorcardiography
Serial electrocardiography
Neural networks
ISSN:1475-925X, 1475-925X
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Shrnutí:Background Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-learning neural networks (NNs) that are specialized in detecting newly emerging or aggravating existing cardiac pathology in serial ECGs. Methods We developed a novel deep-learning method for serial ECG analysis and tested its performance in detection of heart failure in post-infarction patients, and in the detection of ischemia in patients who underwent elective percutaneous coronary intervention. Core of the method is the repeated structuring and learning procedure that, when fed with 13 serial ECG difference features (intra-individual differences in: QRS duration; QT interval; QRS maximum; T-wave maximum; QRS integral; T-wave integral; QRS complexity; T-wave complexity; ventricular gradient; QRS-T spatial angle; heart rate; J-point amplitude; and T-wave symmetry), dynamically creates a NN of at most three hidden layers. An optimization process reduces the possibility of obtaining an inefficient NN due to adverse initialization. Results Application of our method to the two clinical ECG databases yielded 3-layer NN architectures, both showing high testing performances (areas under the receiver operating curves were 84% and 83%, respectively). Conclusions Our method was successful in two different clinical serial ECG applications. Further studies will investigate if other problem-specific NNs can successfully be constructed, and even if it will be possible to construct a universal NN to detect any pathologic ECG change.
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ISSN:1475-925X
1475-925X
DOI:10.1186/s12938-019-0630-9