Introduction to Non-Invasive Biomedical Signals for Healthcare

Mahmood, Danyal; Riaz, Hannan Naseem; Nisar, Humaira

doi:10.1007/978-3-031-23239-8_1

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…Additionally, Shubra Hospital had a higher mean score of knowledge (13.9 + 4.0, 22.6 + 3.4 &22.0 + 4. 19), after that Embaba Hospital (13.5 + 3.61, 22.0 + 4.1& 21.4 + 4.61) matched with Om-Elmasreen Hospital (12.9 + 2.93, 21.8 + 4.3& 21.2 + 4.8) across the program implementation phases, respectively.…”

Section: Table (3)mentioning

confidence: 93%

Efficiency of Competency Training Program on Intensive Care Nurses Performance Regarding the Electrocardiogram Interpretation

Ahmed,

Ali

2023

Egyptian Journal of Nursing and Health Sciences

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Background: Recently one of the most essential therapeutic skills that aids in the quick detection of cardiovascular disease that could be fatal is the interpretation of electrocardiograms. Nurses should have a competency training program with a high-quality content of knowledge and skills for providing competent electrocardiograms interpretation and procedure correctly that can result in wise clinical judgements with favourable effects. Aim: The study aims to evaluate the efficiency of competency training program on intensive care nurses' performance regarding the electrocardiogram interpretation. Design: This study's research methodology was quasi-experimental. Setting: The study was carried out in three public hospitals connected to the Ministry of Health's intensive care units. Subjects: A purposive sample of 60 nurses who were employed in the aforementioned location. Data collection tools: Nurses' knowledge structured questionnaire and electrocardiograms' practical competency checklist. Results: The study denoted that 85% of the studied nurses' knowledge had improved to a satisfactory level; however, this number fell to 80% on the follow-up test, as opposed to 13.3% on the pre-test , additionally, Shubra Hospital had a higher mean score of knowledge (13.9 ± 4.0, 22.6 ± 3.4 & 22.0 ± 4.19), after that Embaba Hospital (13.5 ± 3.61, 22.0 ± 4.1& 21.4 ± 4.61) up from Om-Elmasreen Hospital (12.9 ± 2.93, 21.8 ± 4.3& 21.2 ± 4.8) through the program implementation phases respectively. Conclusion:The competency training program had a positive large effect size on total knowledge and practices regarding electrocardiogram interpretation and procedure across the program phases among the studied nurses at (η 2 = 0.499, & 0.284) respectively, with a highly statistically significant positive correlation between cumulative total knowledge and practice at (r= 0.924 & P= 0.000). Recommendations: Create a generic self-instructional program on nursing interventions for electrocardiograms procedure and interpretation with various clinical cardiovascular disorders.

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Section: Table (3)mentioning

confidence: 93%

Efficiency of Competency Training Program on Intensive Care Nurses Performance Regarding the Electrocardiogram Interpretation

Ahmed,

Ali

2023

Egyptian Journal of Nursing and Health Sciences

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“…EEG signal waves are recorded by placing non-invasive electrodes on the scalp. These signals display information on mental defects and neurological conditions [6]. The brain's electrical signals can be analyzed through the five frequency bands produced by the EEG [7]: delta, alpha, theta, gamma and beta.…”

Section: Introductionmentioning

confidence: 99%

Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means

et al. 2023

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Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%.

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A Bibliometric Analysis of Neurofeedback Studies in Cognitive Impairment: Current Trends and Future Directions in Malaysia

Ali,

Nisar

2023

2023 IEEE 2nd National Biomedical Engineering Conference (NBEC)

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Introduction to Non-Invasive Biomedical Signals for Healthcare

Cited by 3 publications

References 52 publications

Efficiency of Competency Training Program on Intensive Care Nurses Performance Regarding the Electrocardiogram Interpretation

Efficiency of Competency Training Program on Intensive Care Nurses Performance Regarding the Electrocardiogram Interpretation

Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means

A Bibliometric Analysis of Neurofeedback Studies in Cognitive Impairment: Current Trends and Future Directions in Malaysia

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