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Lobo Marques, J. A. (July 21). Finding the “BUY BUTTON”. The creation of the Laboratory of Neuroeconomics at USJ [Conference]. 12th International Conference on E-business, Management and Economics (ICEME 2021), Beijing China.

Lobo Marques, J. A. (2022). Neuroeconomics Applications Based on Multiple Monitoring Tools. [Conference]. 13th International Conference on E-business, Management and Economics (ICEME 2022), Beijing China.

Lobo Marques, J. A. (2022, August 19). IoT-based smart health system for ambulatory maternal and fetal monitoring [Symposium]. Symposium on Intelligent Manufacturing and Artificial Intelligence Technologies (ISIMAIT 2022), Zhejiang, China.

Lobo Marques, J. A. (2022, October 25). Developing Neuromarketing Innovative Research [Conference]. Neuromarathon 2022.

Lobo Marques, J. A. (2023, May 26). Intelligent Data Fusion System for Assessing and Classifying the Long-Term Effects of Exposure to COVID-19 in Pregnancy (Long Covid): Associated Neurophysiological and Epigenetic Mechanisms and Consequences for Infant Development [Conference]. 2023 7th International Conference on Data Mining, Communications and Information Technology (DMCIT & CSMO 2023), Chongqin, China.

Marques, J. A. L. (2023, December). Connecting Neuromarketing and AI - a path to new insights and analysis [CONFERENCE INVITED SPEECH]. Neuromarathon, Italian Association of Neuromarketing.

Nguyen Van, S., Lobo Marques, J. A., Biala, T. A., & Li, Y. (2021). Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning Techniques. Computer Methods and Programs in Biomedicine, 200, 105842. https://doi.org/10.1016/j.cmpb.2020.105842

Background and objective Intrauterine Growth Restriction (IUGR) is a condition in which a fetus does not grow to the expected weight during pregnancy. There are several well documented causes in the literature for this issue, such as maternal disorder, and genetic influences. Nevertheless, besides the risk during pregnancy and labour periods, in a long term perspective, the impact of IUGR condition during the child development is an area of research itself. The main objective of this work is to propose a machine learning solution to identify the most significant features of importance based on physiological, clinical or socioeconomic factors correlated with previous IUGR condition after 10 years of birth. Methods In this work, 41 IUGR (18 male) and 34 Non-IUGR (22 male) children were followed up 9 years after the birth, in average (9.1786 ± 0.6784 years old). A group of machine learning algorithms is proposed to classify children previously identified as born under IUGR condition based on 24-hours monitoring of ECG (Holter) and blood pressure (ABPM), and other clinical and socioeconomic attributes. In additional, an algorithm of relevance determination based on the classifier is also proposed, to determine the level of importance of the considered features. Results The proposed classification solution achieved accuracy up to 94.73%, and better performance than seven state-of-the-art machine learning algorithms. Also, relevant latent factors related to HRV and BP monitoring are proposed, such as: day-time heart rate (day-time HR), day-night systolic blood pressure (day-night SBP), 24-hour standard deviation (SD) of SBP, dropped, morning cortisol creatinine, 24-hour mean of SDs of all NN intervals for each 5 minutes segment (24-hour SDNNi), among others. Conclusion With outstanding accuracy of our proposed solutions, the classification system and the indication of relevant attributes may support medical teams on the clinical monitoring of IUGR children during their childhood development.

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Motta, P., Silva, B., Furtado, F., & Lobo Marques, J. A. (2022). Automatic Classification System for Subjects Exposed to Short-Term Stress Based on Facial Expression Analysis and ElectroDermal Activity [Conference]. XXVIII Brazilian Congress of Biomedical Engineering CBEB 2022, Florianópolis.

Marques, J. A. L., Cortez, P. C., Madeiro, J. P. D. V., Fong, S. J., Schlindwein, F. S., & Albuquerque, V. H. C. D. (2019). Automatic Cardiotocography Diagnostic System Based on Hilbert Transform and Adaptive Threshold Technique. IEEE Access, 7, 73085–73094. https://doi.org/10.1109/ACCESS.2018.2877933

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.

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Khatoon, F., Kumar, M., Khalid, A. A., Alshammari, A. D., Khan, F., Alshammari, R. D., Balouch, Z., Verma, D., Mishra, P., Abotaleb, M., Makarovskikh, T., El-kenawy, E. M., Dutta, P. K., & Marques, J. A. (2022). Quality of life during the pandemic: a cross sectional study about attitude, individual perspective and behavior change affecting general population in daily life. 379–383. https://doi.org/10.1049/icp.2023.0596

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.

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