ArticleComputational intelligence and neuroscience2022
Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Article
- Arrhythmia classification for non-experts using infinite impulse response (IIR)-filter-based machine learning and deep learning models of the electrocardiogram.PeerJ. Computer science · 2024Article
- An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction.Physical and engineering sciences in medicine · 2023Article
- Retracted: Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks.Computational intelligence and neuroscience · 2023Article
- Treatment effects of low-frequency repetitive transcranial magnetic stimulation combined with motor relearning procedure on spasticity and limb motor function in stroke patients.Frontiers in neurology · 2023Article
- The Understanding of Medical Informatics among Dental and Medical Students.BioMed research international · 2022Article
- Mobility and Quality of Life in Chinese Elderly and Geriatric Patients and Biomedical Diagnosis.Computational intelligence and neuroscience · 2022Article
- Accuracy of Brain Computed Tomography Diagnosis by Emergency Medicine Physicians.Computational intelligence and neuroscience · 2022Article
- Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology.Computational intelligence and neuroscience · 2022Article
- Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retraction · 2023-08-02Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/08023704E1AF03E4B971ECCC6A2EF2
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The electrocardiogram, also known as an electrocardiogram (ECG), is considered to be one of the most significant sources of data regarding the structure and function of the heart. In order to obtain an electrocardiogram, the contractions and relaxations of the heart are first captured in the proper recording medium. Due to the fact that irregularities in the functioning of the heart are reflected in the ECG indications, it is possible to use these indications to diagnose cardiac issues. Arrhythmia is the medical term for the abnormalities that might occur in the regular functioning of the heart (rhythm disorder). Environmental and genetic variables can both play a role in the development of arrhythmias. Arrhythmias are reflected on the ECG sign, which depicts the same region regardless of where in the heart they occur; thus, they may be seen in ECG signals. This is how arrhythmias can be detected. Due to the time limits of this study, the ECG signals of individuals who were healthy, as well as those who suffered from arrhythmias were divided into 10-minute segments. The arithmetic mean approach is one of the fundamental statistical factors. It is used to construct the feature vectors of each received wave and interval, and these vectors offer information regarding arrhythmias in accordance with the agreed-upon temporal restrictions. In order to identify the heart arrhythmias, the obtained feature vectors are fed into a classifier that is based on a multilayer perceptron neural network. In conclusion, ROC analysis and contrast matrix are utilised in order to evaluate the overall correct classification result produced by the ECG-based classifier. Because of this, it has been demonstrated that the method that was recommended has high classification accuracy when attempting to diagnose arrhythmia based on ECG indications. This research makes use of a variety of diagnostic terminologies, including ECG signal, multilayer perceptron neural network, signal processing, disease diagnosis, and arrhythmia diagnosis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.