The electrocardiogram is a test that records the electrical activity of the heart, and has recently been shown that it can also detect other non-cardiovascular conditions, such as diabetes, measles, Alzheimer’s, arterial hypertension, fatty liver, hiperpotasemia, hypothyroidism, malaria, etc. For this reason, this paper proposes a computational technique to analyze and detect patterns based on the position and length of the segments between the peaks that make up the electrocardiogram signals, using deep learning techniques created by Artificial Intelligence. The program started by evaluating a database of heart arrhythmia signals, which included more than 120 electrocardiograms grouped between signals from normal and arrhythmia patients, employing convolutional neural network (CNN). The program had a prediction accuracy rate of 94.3% Citation: Raul Isea. Artificial Intelligence Can Diagnose any Disease from the Data of an Electrocardiogram. J Biomed Sci Biotech Res. 2024. 2(1): 1...

Citations   Isea, R. (2024). Can Non-Heart Diseases like Diabetes and Sleep Apnea be Detected by an ECG. Virol Res Rev, 1(1), 01-06 PDF:  https://www.opastpublishers.com/open-access-articles/can-nonheart-diseases-like-diabetes-and-sleep-apnea-be-detected-by-an-ecg.pdf Abstract An electrocardiogram (ECG) is frequently used to identify heart problems, but it can also identify apnea, diabetes, and other conditions that are not heart-related. The study uses deep learning methods from artificial intelligence, particularly the Convolutional Neural Network (CNN) approach, to look for patterns in ECG data. To predict illness, two different methodologies are used. The first approach makes use of an ECG scale, while the second employs a gradient-boosting machine (GBM)-based learning technique. The MIT-BIH Arrhythmia database, the Normal Sinus Rhythm database, the BIDMC Congestive Heart Failure database, the Sleep Apnea database, the type 2 diabetes mellitus dataset, and the dataset for ...

  Citation : Raúl Isea (2021) Analytical Solutions of the Transmissibility of the SARS-CoV-2 in Three Interactive Populations. International Journal of Coronaviruses - 2(4):1-8. https://doi.org/10.14302/issn.2692-1537.ijcv-21-3732 PDF:  https://openaccesspub.org/article/1571/ijcv-21-3732.pdf Abstract This paper resolves analytically a mathematical model that reproduces the transmission of Covid-19 in three interactive populations, i.e. from the initial source of contagion associated with the bat population, subsequently transmitted to unknown host (usually associate with pangolins). The host were sent and distributed to Seafood Market in Wuhan (defined reservoir), and finally infected to the human population. The model is based on a system of ten differential equations reproducing all the possible infection scenarios among all of them, that is: (1) there is no infection in any of the three populations, (2) only the population of bats is infected, (3) only the pangolins, (4) on...

  Citation : Isea, R. (2021). ¿Hubo fraude electoral en las elecciones presidenciales de los Estados Unidos en 2020? Was there voter fraud in the 2020 United States presidential elections?. Conocimiento Libre Y Licenciamiento (CLIC), (24).  PDF:  https://convite.cenditel.gob.ve/revistaclic/index.php/revistaclic/article/view/1087 Resumen El trabajo realiza un estudio forense de los votos registrados en la elección presidencial de los Estados Unidos realizada el 3 de noviembre de 2020, para determinar si hubo fraude tras las continuas declaraciones del ex-presidente Donald Trump. Para ello, se analizaron el total de los votos obtenidos por estado en dos momentos diferentes, es decir, el acumulado de votos hasta el 18 de noviembre, y el total obtenido en la contienda. En ambos casos, no se detectaron sospechas de manipulación de los datos. En paralelo, se analizaron los votos acumulados hasta el 18 de noviembre en varios condados a medida que se iban totalizando por estado, ...

  Citation : Raúl Isea (2021) A Missing Data on Covid-19 Forecasts. International Journal of Coronaviruses - 3(2):27-31. https://doi.org/10.14302/issn.2692-1537.ijcv-21-3918 PDF:  https://openaccesspub.org/article/1685/ijcv-21-3918.pdf Abstract Mathematical and computational studies of Covid-19 have underestimated the influence that other countries have on their daily records. To visualize this, a Granger causality analysis was implemented in Python to determine if the cases registered in Brazil, Chile, Colombia, Ecuador, Panama, Paraguay, Peru and the USA have any effect on Venezuela, and between all of them. Finally, this paper highlights the need to incorporate causality analysis employing only the cases of Covid-19 to improve mid and long term forecasts.

  Citation : Raúl Isea, Jesús Isea (2021) Was there voter fraud in the 2021 Peru Presidential Elections?. Journal of Model Based Research - 1(4):1-5. https://doi.org/10.14302/issn.2643-2811.jmbr-21-3954 PDF:  https://openaccesspub.org/article/1713/jmbr-21-3954.pdf Abstract This paper performs a forensic study of the Peru’s presidential election on June 6th, 2021 between Pedro Castillo and Keiko Fujimori, where ex-candidate Keiko Fujimori claimed there had been irregularities. We calculate three p-values that help us determine if there was fraud. The consensus of the results indicates that there was no manipulation of the results.

  Citation : Raúl Isea (2022) What Could Represent the Mantissa of the Registered Covid-19 Cases?. International Journal of Coronaviruses - 4(2):1-6. https://doi.org/10.14302/issn.2692-1537.ijcv-22-4108 PDF:  https://openaccesspub.org/article/1783/ijcv-22-4108.pdf Abstract In this work we present how it is possible to visualize the different coronavirus outbreaks from the calculation of the mantissa from the daily record of cases by Covid-19.This curve can also indicate the speed of propagation of the disease from the calculated infection rate. These conclusions are derived from the respective calculations in four different countries: Australia, Brazil, Italy, and Venezuela.