RESUMO
Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.
RESUMO
The intention of the experiment is to investigate whether different sounds have influence on heart signal features in the situation the observer is judging the different sounds as positive or negative. As the heart is under (para)sympathetic control of the nervous system this experiment could give information about the processing of sound stimuli beyond the conscious processing of the subject. As the nature of the influence on the heart signal is not known these signals are to be analysed with AI/machine learning techniques. Heart rate variability (HRV) is a variable derived from the R-R interval peaks of electrocardiogram which exposes the interplay between the sympathetic and parasympathetic nervous system. In addition to its uses as a diagnostic tool and an active part in the clinic and research domain, the HRV has been used to study the effects of sound and music on the heart response; among others, it was observed that heart rate is higher in response to exciting music compared with tranquilizing music while heart rate variability and its low-frequency and high-frequency power are reduced. Nevertheless, it is still unclear which musical element is related to the observed changes. Thus, this study assesses the effects of harmonic intervals and noise stimuli on the heart response by using machine learning. The results show that noises and harmonic intervals change heart activity in a distinct way; e.g., the ratio between the axis of the ellipse fitted in the Poincaré plot increased between harmonic intervals and noise exposition. Moreover, the frequency content of the stimuli produces different heart responses, both with noise and harmonic intervals. In the case of harmonic intervals, it is also interesting to note how the effect of consonance quality could be found in the heart response.
RESUMO
The electrocardiogram is traditionally used to diagnose a large number of heart pathologies. Research to improve the readability and classification of cardiac signals includes studies geared toward sonification of the electrocardiographic signal and others involving features related to music processing, such as Mel-frequency cepstral coefficients. In terms of music processing features, this study seeks to use music information retrieval (MIR) features as electrocardiographic signal descriptors. The study compares the discriminatory capability of the introduced features in relation to standard groups such as heart rate variability, wavelet transform, descriptive statistics, Mel coefficients and fractal analysis, evaluated using classification algorithms; the signals analyzed were extracted from public databases. The group of features extracted from wavelet transform and the MIR group showed a high level of discrimination; the best representation of the ECG signals in the study was achieved in most cases by the MIR features. Moreover, a correlation coefficient higher than 0.8 was found between a number of MIR and other feature groups, indicating a likely relationship between the electrocardiographic signals and MIR features. These results suggest the feasibility of representing the analyzed signals by music information retrieval descriptors, giving the potential to consider these electrocardiographic signals as analogues to musical signals.
RESUMO
Background: For some time now, the effects of sound, noise, and music on the human body have been studied. However, despite research done through time, it is still not completely clear what influence, interaction, and effects sounds have on human body. That is why it is necessary to conduct new research on this topic. Thus, in this paper, a systematic review is undertaken in order to integrate research related to several types of sound, both pleasant and unpleasant, specifically noise and music. In addition, it includes as much research as possible to give stakeholders a more general vision about relevant elements regarding methodologies, study subjects, stimulus, analysis, and experimental designs in general. This study has been conducted in order to make a genuine contribution to this area and to perhaps to raise the quality of future research about sound and its effects over ECG signals. Methods: This review was carried out by independent researchers, through three search equations, in four different databases, including: engineering, medicine, and psychology. Inclusion and exclusion criteria were applied and studies published between 1999 and 2017 were considered. The selected documents were read and analyzed independently by each group of researchers and subsequently conclusions were established between all of them. Results: Despite the differences between the outcomes of selected studies, some common factors were found among them. Thus, in noise studies where both BP and HR increased or tended to increase, it was noted that HRV (HF and LF/HF) changes with both sound and noise stimuli, whereas GSR changes with sound and musical stimuli. Furthermore, LF also showed changes with exposure to noise. Conclusion: In many cases, samples displayed a limitation in experimental design, and in diverse studies, there was a lack of a control group. There was a lot of variability in the presented stimuli providing a wide overview of the effects they could produce in humans. In the listening sessions, there were numerous examples of good practice in experimental design, such as the use of headphones and comfortable positions for study subjects, while the listening sessions lasted 20 min in most of the studies.