# A Brief Review on Gender Identification with Electrocardiography Data
## 3 Results  
As presented in Figure 1, we identified 25,388 studies from the selected sources with automation tools. After the search, the automation tool excluded the duplicate records, amounting to 10,335 articles. Next, it filtered the scientific papers by their metadata, i.e., title, abstract, and keywords. It resulted in 14,907 studies being excluded from the analysis, because they did not directly relate to evaluating gender identification with electrocardiography signals. Next, the full text of the remaining 20 articles was assessed considering the inclusion criteria and, consequently, 9 scientific papers were excluded. Finally, the remaining 13 papers were examined and included in the qualitative and quantitative syntheses. The studies were analyzed and selected, extracting the relevant information and metadata. The research performed in this study found research articles published between 2010 and 2022. As reported in Table 1, one study was published in 2022, one study was published in 2021, one study was published in 2020, two studies were published in 2018, one study was published in 2016, one study was published in 2015, two studies were published in 2014, one study was published in 2012, two studies were published in 2011, and one study was published in 2010. Identifying by type of sensors used, eleven studies used electrocardiography sensors, four used echocardiography sensors, two used angiography sensors, and one used myocardial scintigraphy sensors. According to the diseases among the studied population, three studies considered patients with coronary artery disease, four studies considered patients with myocardial infarction, two studies considered patients with arrhythmia, one study considered patients with Duchenne muscular dystrophy, one study considered patients with the acute coronary syndrome, one study considered patients with congenital long-QT syndrome, one study considered patients with left atrial enlargement, one study considered patients with Bundle branch block, and one study considered patients with unstable angina. Geographically, according to the origin of the studies, three  
Figure 1: Flow diagram of the selection of the papers. were performed in the United States of America, two were performed in Egypt, two were performed in China, and the remaining were conducted in the United Kingdom, Lithuania, Germany, Poland, France, and Sweden. Finally, regarding the implemented methods in the different studies, three studies used machine learning methods, but the remaining only performed statistical analysis. In [23], the authors used coronary angiography and echocardiography to predict ischemic mitral regurgitation (MR) in acute ST-elevation myocardial infarction patients (STEMI). The study considered patients with STEMI, whose examination was conducted 12 h after the onset of symptoms and were treated with primary percutaneous coronary intervention (PPCI) at the Lithuanian University of Health Sciences Hospital. Echocardiography was performed after PPCI. Based on the MR grade, patients were divided into groups without significant MR (NMR grade 0-1 MR, \(n\) = 102) and ischemic MR (IMR, grade \(\geq\) 2 MR, \(n\) = 71). Well-developed adverse symptoms were identified as a class \(\geq\) 2 according to the Rentrop classification. The Student's \(t\)-test was used to compare continuous variables with independent samples. Independent predictors of ischemia MR were found based on multivariate logistic regression analysis. [PERSON] et al. [20] used The Statistical Analysis Software to determine the risk factors for the development of cardiac arrhythmia (CA) in patients undergoing hematopoietic stem cell transplantation (HSCT). The experiments were performed with 138 consecutive patients undergoing HSCT at their institution between 1 January 2015 and 31 December 2017. Thirty-one patients (23%) had CA while undergoing HSCT. Atrial fibrillation was the most prevalent form (\(n\) = 13; 42%).