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