The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
RETRACTED ARTICLE: Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals
Open accessAbstract
The heart disease is one of the most serious health problems in today’s world. Over 50 million persons have cardiovascular diseases around the world. Our proposed work based on 744 segments of ECG signal is obtained from the MIT-BIH Arrhythmia database (strongly imbalanced data) for one lead (modified lead II), from 29 people. In this work, we have used long-duration (10 s) ECG signal segments (13 times less classifications/analysis). The spectral power density was estimated based on Welch’s method and discrete Fourier transform to strengthen the characteristic ECG signal features. Our main contribution is the design of a novel three-layer (48 + 4 + 1) deep genetic ensemble of classifiers (DGEC). Developed method is a hybrid which combines the advantages of: (1) ensemble learning, (2) deep learning, and (3) evolutionary computation. Novel system was developed by the fusion of three normalization types, four Hamming window widths, four classifiers types, stratified tenfold cross-validation, genetic feature (frequency components) selection, layered learning, genetic optimization of classifiers parameters, and new genetic layered training (expert votes selection) to connect classifiers. The developed DGEC system achieved a recognition sensitivity of 94.62% (40 errors/744 classifications), accuracy = 99.37%, specificity = 99.66% with classification time of single sample = 0.8736 (s) in detecting 17 arrhythmia ECG classes. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.
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“Stage III —the focus was on further developing ML methods by designing, optimizing and testing a more complex deep genetic (three layers, 53 classifiers) ensemble of classifiers combining the advantages of EL [21, 37, 38, 47, 62, 72], EC [14, 28] and DL [11, 16, 17, 27, 32, 39, 63].”
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Cited by
Classification of myocardial infarction with multi-lead ECG signals and deep CNN
Baloglu, U. B., Talo, M., Yildirim, O., Tan, R. S., & Acharya, U. R. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.02.016 · Cited by 473
Self-citationSuri, J. S., Bhagawati, M., Paul, S., Protogerou, A. D., Sfikakis, P. P., Kitas, G. D., Khanna, N. N., Ruzsa, Z., Sharma, A. M., Saxena, S., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030722 · Cited by 55
A comprehensive review of AI-Based detection of Arrhythmia using Electrocardiogram (ECG)
Ayyub, A., Politis, C., & Usman, M. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110594 · Cited by 18
Stoean, R., Stoean, C., Atencia, M., Rodríguez-Labrada, R., & Joya, G. (2020) · Mathematics · doi:10.3390/math8071078 · Cited by 16
Metadata
- Authors
- Pławiak, P., & Acharya, U. R.
- Venue
- Neural Computing and Applications
- Published
- 2019-01-05
- Volume
- 32
- Issue
- 15
- Pages
- 11137-11161
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W2907615080
- Cited by
- 248
- References
- 8
- FWCI
- 27.71
- Type
- journal article
- Language
- en
- Status
- Retracted
- Open access
- hybrid
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- Pławiak, P., & Acharya, U. R. (2020). RETRACTED ARTICLE: Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Computing and Applications. https://doi.org/10.1007/s00521-018-03980-2
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
