OBJECTIVE: Recently, older people's cardiovascular systems have been affected by aging-related changes. An electrocardiogram (ECG) provides information about cardiac health. Analyzing ECG signals can help doctors and researchers diagnose many deaths. Besides direct ECG analysis, some measurements can be extracted from the ECG signals, and one of the most important measurements is heart rate variability (HRV). Research and clinical domains can benefi t from HRV measurement and analysis as a potential noninvasive tool for assessing autonomic nervous system activity. The HRV describes the variation between an ECG signal's RR intervals over time and the change in that interval over time. An individual's heart rate (HR) is a non-stationary signal, and its variation can indicate a medical condition or impending cardiac disease. Many factors, such as stress, gender, disease, and age, infl uence HRV. METHODS: The data for this study is taken from a standard database, the Fantasia Database, which contains 40 subjects, including two groups of 20 young subjects (21-34 years old) and 20 older subjects (68-85 years old). We used two non-linear methods, Poincare and Recurrence Quantifi cation Analysis (RQA), to determine how different age groups affect HRV using Matlab and Kubios software. RESULTS: By analyzing some features extracted from this non-linear method based on a mathematical model and making a comparison, the results indicate that the SD1, SD2, SD1/SD2, and area of an ellipse (S) in Poincare will be lower in old people than in young people, but %REC, %DET, Lmean and Lmax will recur more often in older people than in younger ones. Poincare Plot and RQA show opposite correlations with aging. In addition, Poincaré's plot showed that young people have a greater range of changes than the elderly. CONCLUSION: According to the result of this study, heart rate changes can be reduced by aging, and ignoring this issue could lead to cardiovascular disease in the future (Tab.
BACKGROUND: Mental arithmetic analysis based on electroencephalogram (EEG) signals can help to understand some disorders such as attention defi cit hyperactivity disorder, arithmetic disorder, or autism spectrum disorder in which learning is diffi cult. Most mental computation detection and classifi cation systems rely on the characteristics of a single channel, however, the understanding of the connections between EEG channels, which certainly contains valuable information, is still evolving. The methods presented in this paper are the result of a research project that introduces an alternative method for better and faster receipt of information from the EEG signals of individuals, which are generally complex and nonlinear. METHODS: The EEGs of 66 healthy individuals were recorded in two rest modes and mental task a designed, with a sampling frequency of 500 Hz. To classify these two modes, we extracted features from our recordings to differentiate the EEG signals of these two groups in a single channel as well as combine possible channels. The new method that was proposed was the extraction of several geometric features from Poincaré design analysis, which used the necessary comparison t-test to determine brain differences, with a signifi cance level of less than 0.05 in the state of mental calculations and facial rest. Also, an artifi cial neural network (ANN) has been used for automatic learning and diagnosis in the two mentioned modes. RESULTS: The results of this paper show that by using a combination of geometric properties (sides, angles, shortest distance, slope, and coeffi cients of the third-degree equation) using selected channels (FP1, F7, C4, O1) can achieve 100 % accuracy. The sensitivity reached 100 %. As well as 100 % feature. CONCLUSIONS: With the help of mental calculation, it is possible to diagnose, treat, rehabilitate and rehabilitation people who have lost the function of a part of their brain due to a disease in this fi eld (Tab. 6, Fig. 15, Ref. 45).
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