introduction automatic human emotion recognition is one of the most interesting topics in the field of affective computing. however, development of a reliable approach with a reasonable recognition rate is a challenging task. the main objective of the present study was to propose a robust method for discrimination of emotional responses thorough examination of heart rate variability (hrv). in t...

the aim of this study is to show that the measures derived from electrocardiogram (ecg) signals many a time perform better than the same measures obtained from heart rate (hr) signals. a comparison was made to investigate how far the nonlinear symbolic dynamics approach helps to characterize the nonlinear properties of ecg signals and hr signals, and thereby discriminate between normal and cong...

background: fetal heart rate (fhr) extracted from abdominal electrocardiogram (ecg) is a powerful non-invasive method in appropriately assessing the fetus well-being during pregnancy. despite significant advances in the field of electrocardiography, the analysis of fetal ecg (fecg) signal is considered a challenging issue which is mainly due to low signal to noise ratio (snr) of fecg. objective...

this paper aims to propose an effective paroxysmal atrial fibrillation (paf) predictor which is based on the analysis of the heart rate variability (hrv) signal. predicting the onset of paf, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize the risks for the patients. this method consists of four st...

Introduction Automatic human emotion recognition is one of the most interesting topics in the field of affective computing. However, development of a reliable approach with a reasonable recognition rate is a challenging task. The main objective of the present study was to propose a robust method for discrimination of emotional responses thorough examination of heart rate variability (HRV). In t...