Effects of two teaching interventions on nursing students’ acquisition of competence in ECG interpretation

Purpose: Nurse ability to recognise patient arrhythmias could contribute to preventing in-hospital cardiac arrest. Research suggests that nurses and nursing students lack competence in electrocardiogram (ECG) interpretation. The aim of this study was to compare the effects of two training strategies on nursing students’ acquisition of competence in ECG interpretation. Materials and methods: A controlled randomised trial with 98 nursing students. Divided in groups of 12–16, participants were randomly allocated to one of the following 3-h teaching intervention groups: 1) traditional instructor-led (TILG), and 2) flipped classroom (FCG). Participants’ competence in ECG interpretation was measured in terms of knowledge (%), skills (%) and self-efficacy (%) using a specifically designed and previously validated toolkit at pre-test and post-test. Two-way MANOVA explored the interaction effect between ‘teaching group’ and ‘time of assessment’ and its impact on participants’ competence. Within-group differences at pre-test and post-test were explored by carrying out paired t-tests. Between-group differences at pre- and post-test were examined by performing independent t-test analysis. Results: There was a statistically significant interaction effect between ‘teaching group’ and ‘time of assessment’ on participants’ competence in ECG interpretation (F(3,190) = 86.541, p = 0.001; Wilks’ Λ = 0.423). At pre-test, differences in knowledge (TILG = 35.12 ± 12.07; FCG = 35.66 ± 10.66), skills (TILG = 14.05 ± 10.37; FCG = 14.82 ± 14.14), self-efficacy (TILG = 46.22 ± 23.78; FCG = 40.01 ± 21.77) and all other variables were non-significant (p > 0.05). At post-test, knowledge (TILG = 55.12 ± 14.16; FCG = 94.2 ± 7.31), skills (TILG = 36.90 ± 16.45; FCG = 86.43 ± 14.32) and self-efficacy (TILG = 70.78 ± 14.55; FCG = 79.98 ± 10.35) had significantly improved, regardless of the training received (p < 0.05). Nonetheless, participants in the FCG scored significantly higher than participants in the TILG in knowledge, skills and self-efficacy (p < 0.05). Conclusion: Flipping the classroom for teaching ECG interpretation to nursing students may be more effective than using a traditional instructor-led approach in terms of immediate acquisition of competence in terms of knowledge, skills and self-efficacy. Further research on the effects of both teaching strategies on the retention of the competence will be undertaken.

An automatic classifier of emotions built from entropy of noise

The electrocardiogram (ECG) signal has been widely used to study the physiological substrates of emotion. However, searching for better filtering techniques in order to obtain a signal with better quality and with the maximum relevant information remains an important issue for researchers in this field. Signal processing is largely performed for ECG analysis and interpretation, but this process can be susceptible to error in the delineation phase. In addition, it can lead to the loss of important information that is usually considered as noise and, consequently, discarded from the analysis. The goal of this study was to evaluate if the ECG noise allows for the classification of emotions, while using its entropy as an input in a decision tree classifier. We collected the ECG signal from 25 healthy participants while they were presented with videos eliciting negative (fear and disgust) and neutral emotions. The results indicated that the neutral condition showed a perfect identification (100%), whereas the classification of negative emotions indicated good identification performances (60% of sensitivity and 80% of specificity). These results suggest that the entropy of noise contains relevant information that can be useful to improve the analysis of the physiological correlates of emotion.