Non-linear analysis of heart rate variability for evaluating the acute effects of caffeinated beverages in young adults

Genç yetişkinlerde elektrofizyolojik ve hemodinamik parametreler üzerine kafeinli içeceklerin akut etkileri

Purpose: The consumption of caffeinated beverages has significantly increased among young people in recent years. The objective of the study is to investigate the acute effects of caffeinated beverages on electrocardiographic and hemodynamic parameters of young adults. Materials and Methods: Study was designed as a non-randomized, non-placebo controlled, three-arm parallel assignment. It was conducted on 56 individuals selected from a pool of students enrolled at Aydın Adnan Menderes University. Energy drink, coffee, cola and control groups were determined according to the results of the applicant acceptance questionnaire. Blood pressure and electrocardiogram were measured before and after consuming the drinks at 30 minutes and 60 minutes. Heart rate variability was detected from electrocardiogram signal and investigated by linear analysis. Results: All caffeinated drinks increased the blood pressure. Only, heart rate was increased by energy drink and cola. There was a small increment in root-mean square differences of successive R-R intervals and the number of times successive heartbeat intervals exceed 50ms (NN50) values in coffee group. High frequency (HF) values were increased some for coffee and cola groups. But, low frequency (LF) and LF/HF values were decreased. These alterations were statistically significant for coffee group. PR interval and QRS complex did not alter, however, QTc interval was lower in energy drink and cola groups. Conclusion: Caffeinated beverage consumption has the potential to induce adverse effects on cardiovascular system of young adults. Coffee appears to be more prominent than energy drink and cola.

Acute Effects of Energy Drink on Autonomic and Cardiovascular Parameters Recovery in Individuals with Different Cardiorespiratory Fitness: A Randomized, Crossover, Double-Blind and Placebo-Controlled Trial

Fundamento

Tem-se sugerido que o consumo de bebidas energéticas (BEs) possa afetar a atividade cardiovascular.

Objetivos

Investigar os efeitos agudos da ingestão de BE sobre a variabilidade da frequência cardíaca (VFC) recuperação cardiovascular após exercício aeróbico moderado em homens de diferentes capacidades cardiorrespiratórias.

Métodos

Este é um estudo randomizado, duplo cego, crossover, controlado por placebo. Vinte e oito jovens adultos foram divididos em dois grupos de acordo com o pico de consumo de oxigênio (pico de VO2): (1) pico de VO2 alto (AO) – pico de VO2 > 52,15 mL/Kg/min, e (2) pico de VO2 baixo (BO) - pico de VO2 <52,15 mL/Kg/min. Os indivíduos de ambos os grupos foram submetidos a dois protocolos de exercícios em ordem aleatória: exercício moderado aeróbico (60% de pico de VO2) após a ingestão de 250 mL de água (protocolo placebo) ou 250 mL de BE (protocolo BE). Durante os testes de exercício, foram registrados valores de parâmetros cardiorrespiratórios e de VFC.

Resultados

Foram observadas diferenças significativas para o índice de LF (unidades normalizadas) entre “repouso” e “Rec1” nos grupos de AO e BO durante o protocolo BE. Para a razão LF/HF, foram observadas diferenças significativas entre “repouso” e Rec1 nos grupos AO e BO nos protocolos BE.

Conclusão

A ingestão aguda de BE retardou a recuperação da frequência cardíaca após o exercício em indivíduos com capacidade cardiorrespiratória baixa e indivíduos com capacidade cardiorrespiratória alta.

Automated Detection of Caffeinated Coffee-Induced Short-Term Effects on ECG Signals Using EMD, DWT, and WPD

The effect of coffee (caffeinated) on electro-cardiac activity is not yet sufficiently researched. In the current study, the occurrence of coffee-induced short-term changes in electrocardiogram (ECG) signals was examined. Further, a machine learning model that can efficiently detect coffee-induced alterations in cardiac activity is proposed. The ECG signals were decomposed using three different joint time–frequency decomposition methods: empirical mode decomposition, discrete wavelet transforms, and wavelet packet decomposition with varying decomposition parameters. Various statistical and entropy-based features were computed from the decomposed coefficients. The statistical significance of these features was computed using Wilcoxon’s signed-rank (WSR) test for significance testing. The results of the WSR tests infer a significant change in many of these parameters after the consumption of coffee (caffeinated). Further, the analysis of the frequency bands of the decomposed coefficients reveals that most of the significant change was localized in the lower frequency band (<22.5 Hz). Herein, the performance of nine machine learning models is compared and a gradient-boosted tree classifier is proposed as the best model. The results suggest that the gradient-boosted tree (GBT) model that was developed using a db2 mother wavelet at level 2 decomposition shows the highest mean classification accuracy of 78%. The outcome of the current study will open up new possibilities in detecting the effects of drugs, various food products, and alcohol on cardiac functionality.