@article{ribeiro_machine_2024, title = {Machine learning-based cardiac activity non-linear analysis for discriminating {COVID}-19 patients with different degrees of severity}, volume = {87}, issn = {1746-8094}, url = {https://www.sciencedirect.com/science/article/pii/S1746809423009916}, doi = {10.1016/j.bspc.2023.105558}, abstract = {Objective: This study highlights the potential of an Electrocardiogram (ECG) as a powerful tool for early diagnosis of COVID-19 in critically ill patients with limited access to CT–Scan rooms. Methods: In this investigation, 3 categories of patient status were considered: Low, Moderate, and Severe. For each patient, 2 different body positions have been used to collect 2 ECG signals. Then, from each collected signal, 10 non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) were extracted every 1s ECG time-series length to serve as entries for 19 Machine learning classifiers within a leave-one-out cross-validation procedure. Four different classification scenarios were tested: Low vs. Moderate, Low vs. Severe, Moderate vs. Severe and one Multi-class comparison (All vs. All). Results: The classification report results were: (1) Low vs. Moderate - 100\% of Accuracy and 100\% of F1–Score; (2) Low vs. Severe - Accuracy of 91.67\% and an F1–Score of 94.92\%; (3) Moderate vs. Severe - Accuracy of 94.12\% and an F1–Score of 96.43\%; and (4) All vs All - 78.57\% of Accuracy and 84.75\% of F1–Score. Conclusion: The results indicate that the applied methodology could be considered a good tool for distinguishing COVID-19’s different severity stages using ECG signals. Significance: The findings highlight the potential of ECG as a fast and effective tool for COVID-19 examination. In comparison to previous studies using the same database, this study shows a 7.57\% improvement in diagnostic accuracy for the All vs All comparison.}, urldate = {2024-01-14}, journal = {Biomedical Signal Processing and Control}, author = {Ribeiro, Pedro and Marques, João Alexandre Lobo and Pordeus, Daniel and Zacarias, Laíla and Leite, Camila Ferreira and Sobreira-Neto, Manoel Alves and Peixoto, Arnaldo Aires and de Oliveira, Adriel and Madeiro, João Paulo do Vale and Rodrigues, Pedro Miguel}, month = jan, year = {2024}, keywords = {Accuracy, COVID-19, ECG signals, Machine learning classifiers, Non-linear analysis, –}, pages = {105558}, } @article{rodrigues_enhancing_2023, title = {Enhancing {Health} and {Public} {Health} through {Machine} {Learning}: {Decision} {Support} for {Smarter} {Choices}}, volume = {10}, copyright = {http://creativecommons.org/licenses/by/3.0/}, issn = {2306-5354}, shorttitle = {Enhancing {Health} and {Public} {Health} through {Machine} {Learning}}, url = {https://www.mdpi.com/2306-5354/10/7/792}, doi = {10.3390/bioengineering10070792}, abstract = {In recent years, the integration of Machine Learning (ML) techniques in the field of healthcare and public health has emerged as a powerful tool for improving decision-making processes [...]}, language = {en}, number = {7}, urldate = {2023-08-01}, journal = {Bioengineering}, author = {Rodrigues, Pedro Miguel and Madeiro, João Paulo and Marques, João Alexandre Lobo}, month = jul, year = {2023}, note = {Number: 7 Publisher: Multidisciplinary Digital Publishing Institute}, keywords = {n/a}, pages = {792}, } @article{ribeiro_covid-19_2023, title = {{COVID}-19 {Detection} by {Means} of {ECG}, {Voice}, and {X}-ray {Computerized} {Systems}: {A} {Review}}, volume = {10}, copyright = {http://creativecommons.org/licenses/by/3.0/}, issn = {2306-5354}, shorttitle = {{COVID}-19 {Detection} by {Means} of {ECG}, {Voice}, and {X}-ray {Computerized} {Systems}}, url = {https://www.mdpi.com/2306-5354/10/2/198}, doi = {10.3390/bioengineering10020198}, abstract = {Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70\% and 100\%, and F1-Scores from 89.52\% to 100\%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals’ evolution of the disease.}, language = {en}, number = {2}, urldate = {2023-03-21}, journal = {Bioengineering}, author = {Ribeiro, Pedro and Marques, João Alexandre Lobo and Rodrigues, Pedro Miguel}, month = feb, year = {2023}, note = {Number: 2 Publisher: Multidisciplinary Digital Publishing Institute}, keywords = {COVID-19, artificial intelligence, computerized diagnostic systems, image processing, signal processing}, pages = {198}, } @article{cavalcante_sudden_2023, title = {Sudden cardiac death multiparametric classification system for {Chagas} heart disease's patients based on clinical data and 24-hours {ECG} monitoring}, volume = {20}, copyright = {2023 The Author(s)}, issn = {1551-0018}, url = {http://www.aimspress.com/rticle/doi/10.3934/mbe.2023402}, doi = {10.3934/mbe.2023402}, abstract = {{\textless}abstract{\textgreater}{\textless}p{\textgreater}About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that \$ \> million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65\% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63\% recall (sensitivity) and 80.55\% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.{\textless}/p{\textgreater}{\textless}/abstract{\textgreater}}, language = {en}, number = {5}, urldate = {2023-03-21}, journal = {Mathematical Biosciences and Engineering}, author = {Cavalcante, Carlos H. L. and Primo, Pedro E. O. and Sales, Carlos A. F. and Caldas, Weslley L. and Silva, João H. M. and Souza, Amauri H. and Marinho, Emmanuel S. and Pedrosa, Roberto C. and Marques, João A. L. and Santos, Hélcio S. and Madeiro, João P. V. and Cavalcante, Carlos H. L. and Primo, Pedro E. O. and Sales, Carlos A. F. and Caldas, Weslley L. and Silva, João H. M. and Souza, Amauri H. and Marinho, Emmanuel S. and Pedrosa, Roberto C. and Marques, João A. L. and Santos, Hélcio S. and Madeiro, João P. V.}, year = {2023}, note = {Cc\_license\_type: cc\_by Number: mbe-20-05-402 Primary\_atype: Mathematical Biosciences and Engineering Subject\_term: Research article Subject\_term\_id: Research article}, pages = {9159--9178}, } @article{yan_review_2023, title = {A review on multimodal machine learning in medical diagnostics}, volume = {20}, copyright = {2023 The Author(s)}, issn = {1551-0018}, url = {http://www.aimspress.com/rticle/doi/10.3934/mbe.2023382}, doi = {10.3934/mbe.2023382}, abstract = {Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.}, language = {en}, number = {5}, urldate = {2023-03-21}, journal = {Mathematical Biosciences and Engineering}, author = {Yan, Keyue and Li, Tengyue and Marques, João Alexandre Lobo and Gao, Juntao and Fong, Simon James and Yan, Keyue and Li, Tengyue and Marques, João Alexandre Lobo and Gao, Juntao and Fong, Simon James}, year = {2023}, note = {Cc\_license\_type: cc\_by Number: mbe-20-05-382 Primary\_atype: Mathematical Biosciences and Engineering Subject\_term: Review Subject\_term\_id: Review}, pages = {8708--8726}, } @article{khatoon_quality_2022, title = {Quality of life during the pandemic: a cross sectional study about attitude, individual perspective and behavior change affecting general population in daily life}, shorttitle = {Quality of life during the pandemic}, url = {https://digital-library.theiet.org/content/conferences/10.1049/icp.2023.0596}, doi = {10.1049/icp.2023.0596}, abstract = {Quality of life in general population before and during pandemic is topic need to be address by researcher in terms of mobility, self-care, usual activities, pain/discomfort and anxiety/depression. The study was carried out among Saudi population. Data were collected from general population using questionnaire during the period from 22 August 2021 to 10th January 2022. As a result, total 214 participants have included in this study. Among them prevalent age group include 40 years (n= 63, 29.4\%) shadowed by the age group 25-35 (n= 61, 28.5\%) while above 60 years group were least frequent (n= 1, 0.5\%). On questioning the applicants whether they were satisfied with their health and how would they rate their quality of life, their answers were as follows: yes, or satisfied (n= 86, 40.2\%), very Satisfied (n= 102, 47.7\%) Dissatisfied (n= 11, 5.1\%) and neither satisfied nor dissatisfied (n= 15, 7\%). Due to pandemic, they were rate quality of life very good (n= 94, 43.9\%), good (n= 63, 29.4 \%) poor (n= 5, 2.3 \%) and neither good and nor poor (n= 52, 24.3 \%). During pandemic 96 participants feel no change in their weight but 110 participants respond that there is increase in coffee intake during the pandemic. Similarly increased in smoking habits and decrease rate in social activities (n=119,41.4\%). The psychosomatic well-being of people has been interrupted by disturbing their social activities during pandemic.}, language = {en}, urldate = {2023-10-10}, author = {Khatoon, F. and Kumar, M. and Khalid, A. A. and Alshammari, A. D. and Khan, F. and Alshammari, R. D. and Balouch, Z. and Verma, D. and Mishra, P. and Abotaleb, M. and Makarovskikh, T. and El-kenawy, E. M. and Dutta, P. K. and Marques, J. A.}, month = jan, year = {2022}, note = {Publisher: IET Digital Library}, pages = {379--383}, } @article{marques_automatic_2019, title = {Automatic {Cardiotocography} {Diagnostic} {System} {Based} on {Hilbert} {Transform} and {Adaptive} {Threshold} {Technique}}, volume = {7}, issn = {2169-3536}, url = {https://ieeexplore.ieee.org/abstract/document/8682138}, doi = {10.1109/ACCESS.2018.2877933}, abstract = {The visual analysis of cardiotocographic examinations is a very subjective process. The accurate detection and segmentation of the fetal heart rate (FHR) features and their correlation with the uterine contractions in time allow a better diagnostic and the possibility of anticipation of many problems related to fetal distress. This paper presents a computerized diagnostic aid system based on digital signal processing techniques to detect and segment changes in the FHR and the uterine tone signals automatically. After a pre-processing phase, the FHR baseline detection is calculated. An auxiliary signal called detection line is proposed to support the detection and segmentation processes. Then, the Hilbert transform is used with an adaptive threshold for identifying fiducial points on the fetal and maternal signals. For an antepartum (before labor) database, the positive predictivity value (PPV) is 96.80\% for the FHR decelerations, and 96.18\% for the FHR accelerations. For an intrapartum (during labor) database, the PPV found was 91.31\% for the uterine contractions, 94.01\% for the FHR decelerations, and 100\% for the FHR accelerations. For the whole set of exams, PPV and SE were both 100\% for the identification of FHR DIP II and prolonged decelerations.}, journal = {IEEE Access}, author = {Marques, J. A. Lobo and Cortez, P. C. and Madeiro, J. P. D. V. and Fong, S. J. and Schlindwein, F. S. and Albuquerque, V. H. C. D.}, year = {2019}, note = {13 citations (Crossref) [2022-09-21] Conference Name: IEEE Access}, keywords = {Acceleration, Biomedical monitoring, Cardiography, Cardiotocography (CTG), Databases, FHR DIP II, FHR accelerations, FHR baseline detection, FHR decelerations, Fetal heart rate, Hilbert transform, Hilbert transforms, Monitoring, PPV, Transforms, accurate fetal heart rate feature detection, accurate fetal heart rate feature segmentation, adaptive threshold technique, antepartum database, automatic cardiotocography diagnostic system, auxiliary signal, cardiotocographic examinations, computerized diagnostic aid system, digital signal processing techniques, fetal distress, fetal heart rate (FHR), fetal signals, maternal signals, medical signal detection, medical signal processing, obstetrics, patient monitoring, positive predictivity value, preprocessing phase, segmentation processes, uterine contractions, uterine contractions (UC), uterine tone signals, visual analysis}, pages = {73085--73094}, } @article{paulo_do_vale_madeiro_heart_2015, title = {A {Heart} {Rate} {Variability}-based {Smart} {Approach} to {Analyze} {Frailty} in {Older} {Adults}}, issn = {22344624}, url = {http://smartcr.org/view/download.php?filename=smartcr_vol5no4p002.pdf}, doi = {10.6029/smartcr.2015.04.002}, abstract = {This paper presents an algorithm that applies metrics derived from automatic QRS detection and segmentation in electrocardiogram signals for analyzing Heart Rate Variability to study the evolution of metrics in the frequency domain of a clinical procedure. The analysis was performed on three sets of elderly people, who are categorized according to frailty phenotype. The first set was comprised of frail elderly, the second pre-frail elderly, and the third robust elderly. Investigators from many disciplines have been encouraged to contribute to the understanding of molecular and physiological changes in multiple systems that may increase the vulnerability of frail elderly. In this work, the frailty phenotype can be characterized by unintentional weight loss, as self-reported, fatigue assessed by self-report, grip strength (measured directly), physical activity level assessed by self-report and gait speed (measured). The results obtained demonstrate the existence of significant differences between Heart Rate Variability metrics for the three groups, especially considering a higher preponderance for sympathetic nervous system for the group of robust patients in response to postural maneuver.}, urldate = {2023-03-22}, journal = {The Smart Computing Review}, author = {Paulo do Vale Madeiro, João and César Cortez, Arnaldo Aires Peixoto Júnior, Paulo and Alexandre Lôbo Marques, João and Alisson Pessoa Guimarães, Antônio and Hebert da Silva Felix, John}, month = aug, year = {2015}, }