ArticlePersonal and ubiquitous computing2023
Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients.
Article in Personal and ubiquitous computing, 2023. 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 It has been retracted, and should not be counted.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 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 117 citations in OpenAlex.
- Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
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- Portable Face-Shielding Device Based on sEMG Considering the COVID-19 Scenario.Journal of healthcare engineering · 2023Article
- Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms.Journal of biomedical physics & engineering · 2022Article
- Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19.Computers & electrical engineering : an international journal · 2022Article
- Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19.Computers & electrical engineering : an international journal · 2022Article
- Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction.PloS one · 2022Article
- Fuzzy Logic-Based Systems for the Diagnosis of Chronic Kidney Disease.BioMed research international · 2022Article
- Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.Frontiers in public health · 2022Article
- Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model.BioMed research international · 2022Article
Corrections and comments
- Retraction · 2025-11-08Concerns/Issues about Referencing/Attributions · Concerns/Issues about Results and/or Conclusions · Compromised Peer Review · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions · · Prabu Subramani aka Subramani Prabu; Srinivas K. aka K Srinivas; Kavitha Rani B aka Rani B Kavitha; Sujatha R aka R Sujatha; Parameshachari B.D aka P D Parameshachari;
- Retracted
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning-based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg's method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.
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Registered trials
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.