Abstract
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.
Highlights
While the electrocardiogram (ECG) is an electrical recording of heart activity, heart rate variability (HRV) is a measurement derived from the ECG signal that provides information about the state or condition of the autonomic nervous system (ANS) [1]
The most used geometric indices include Triangular index (TRI), a metric extracted from a histogram of normal RR intervals with all the RR values and their frequency of occurrence [5]; triangular interpolation of normal RR interval histogram (TINN), a distribution of all RR intervals calculated from the base of a triangle shape formed from the histogram peaks [5]; and the Poincaré or Lorenz plot, a graphical representation of HRV [145] obtained by plotting each RR interval as a function of the previous RR interval
A methodological framework to design new experiments to study the effects of musical structures and noise on ECG and HRV signals was presented
Summary
While the electrocardiogram (ECG) is an electrical recording of heart activity, heart rate variability (HRV) is a measurement derived from the ECG signal that provides information about the state or condition of the autonomic nervous system (ANS) [1]. The inclusion of a period of silence between successive stimuli is suggested, to reduce a carry-over effect from the previous stimulus The duration of this period of silence should be set, considering the general experimental design: the total duration of the experiment should be as short as possible while enabling the stated objective to be achieved. It is advised that study subjects keep their eyes closed as they listen to stimuli during the experiment; the use of a mask to cover the eyes would assist in avoiding the influence of visual stimuli on the measurements This would favour the capture of electroencephalographic signals as eyelid movements are reduced, minimising a source of noise. Other benefits of AI are related to efficiency, accuracy, and precision in analysis [99]; competence to identify, classify and extract features from complex, high-dimensional and noisy data [100]; the capability of generalisation; robustness; and the possibility to integrate expert knowledge [101]
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