Technological and medical advances in recent decades have promoted a lengthening of life expectancy, leading to an increase in age-associated cognitive diseases. As a result, there is a growing interest in efficient and cost-effective technological alternatives for the early detection of cognitive problems. Recent studies show growing evidence of a relationship between electrocardiogram (ECG) parameters and cognitive decline. For this reason, this study aims to assess the validity of ECG markers to detect mild cognitive impairment in elderly subjects. The 'Lemon' database, that contains the cognitive and ECG results of 227 healthy adults, has been analysed. Initially, the electrocardiographic signals have been filtered and adapted to extract multiple relevant ECG markers. To assess the reliability of these markers, several tests were conducted. A statistical analysis between cognitive categories, a machine learning classification problem, and multiple regressions with the additional tests included in the database. The analysis focused on elderly patients, who are classified in three cognitive groups according to their impairment (normal, moderate, and severe). Given the statistical significance between cognitive categories and the favourable results obtained in the regression analysis, two markers should be emphasised: the traditional temporal ratios of heart rate variability (HRV), such as the average beat, a measure of the average between QRS peaks, and the fragmentation ratios (HRF), an estimate of cardiac neuroautonomic dysfunction measured by variations in the QRS interval. Among the different classifiers, the artificial neural network (ANN) stands out, providing the best and most stable classification ratios (55.00 % accuracy, 47.60 % sensitivity, 50.00 % precision, in a three class, five-cross-fold problem). The reported tendency to correctly differentiate normal subjects must be emphasized. This is observed both in the statistical tests, where no significance is found between the moderate and severe groups, and in the classification problems, since there is a bias towards error among subjects with impaired condition. In conclusion, a slight relationship between cardiac markers and cognitive decline has been reported. Further research as well as the replication of the conducted study with different databases is recommended. The idea of combining ECG markers with other literary validated technologies, such as electrocardiography (EEG), suggests an interesting approach that may lead to improved detection of cognitive disorders in early stages
