Physical Fatigue Prediction Based on Heart Rate Variability (HRV) Features in Time and Frequency Domains Using Artificial Neural Networks Model During Exercise

Artificial neural networks (ANN) are a computational method that has been widely used to solve complex problems and carry out predictions on nonlinear systems. Multilayer perceptron artificial neural networks were used to predict the physiological response that would be obtained by adding a specific concentration of digoxin to Tivela stultorum hearts, this organism is a model for testing cardiac drugs that pretends to be used in humans. The MLPANN inputs were weight, volume, length, and width of the heart, digoxin concentration and volume used for diluting digoxin, and maximum contraction, minimum contraction, filling time, and heart rate before adding digoxin, and the outputs were the maximum contraction, minimum contraction, filling time, and heart rate that would be obtained after adding digoxin to the heart. ANNs were trained, validated, and tested with the results obtained from the in vivo experiments. To choose the optimal network, the smallest square mean error value was used. Perceptrons obtained a high performance and correlation between predicted and calculated values, except in the case of the filling time output. Accurate predictions of the T. stultorum clams cardioactivity were obtained when a specific concentration of digoxin was added using ANNs with one hidden layer; this could be useful as a tool to facilitate laboratory experiments to test digoxin effects.

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Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison to Electrocardiography: A Comprehensive Analysis in Time and Frequency Domains (Preprint)

Journal of Medical Internet Research ◽

10.2196/27487 ◽

2021 ◽

Author(s):

Rui Cao ◽

Iman Azimi ◽

Fatemeh Sarhaddi ◽

Hannakaisa Niela-Vilen ◽

Anna Axelin ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Accuracy Assessment ◽

Comprehensive Analysis ◽

Frequency Domains ◽

Time And Frequency Domains

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Effects of Short-Term Psychological Stress on the Time and Frequency Domains of Heart-Rate Variability

Perceptual and Motor Skills ◽

10.2466/pms.2000.91.2.515 ◽

2000 ◽

Vol 91 (2) ◽

pp. 515-524 ◽

Cited By ~ 136

Author(s):

J. P. A. Delaney ◽

D. A. Brodie

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Psychological Stress ◽

Short Term ◽

Frequency Domains ◽

Time And Frequency Domains

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Classification of fetal abnormalities based on CTG signal

Baghdad Science Journal ◽

10.21123/bsj.11.2.681-689 ◽

2014 ◽

Vol 11 (2) ◽

pp. 681-689

Author(s):

Baghdad Science Journal

Keyword(s):

Neural Networks ◽

Heart Rate ◽

Fuzzy Logic ◽

Artificial Neural Networks ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Discrete Wavelet ◽

Artificial Neural

The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was transformed using transform domains Discrete Wavelet Transform(DWT) in order to obtain the system features .At the last stage the approximation coefficients result from the Discrete Wavelet Transform were fed to the Artificial Neural Networks and to the Fuzzy Logic, then compared between two results to obtain the best for classifying fetal heart rate.

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