Modelling Naturalistic Work Stress Using Spectral HRV Representations and Deep Learning

Hagad, Juan Lorenzo; Fukui, Kenichi; Numao, Masayuki

doi:10.1007/978-3-030-39878-1_24

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…Frequently, continuous wavelet transform (CWT) is used as a time-frequency representation of ECG signals ( 151 ). However, this tool is not often used in analysing HRV ( 152 ); instead, Lomb-Scargle periodograms have occasionally been used ( 153 ). As such, this technique is proposed as an element to link HRV analysis to new techniques such as those related to deep learning.…”

Section: Discussionmentioning

confidence: 99%

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A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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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.

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Section: Discussionmentioning

confidence: 99%

“…this tool is not often used in analysing HRV ( 152); instead, Lomb-Scargle periodograms have occasionally been used (153). As such, this technique is proposed as an element to link HRV analysis to new techniques such as those related to deep learning.…”

Section: Frequency Domainmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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“…Few applications of deep learning techniques on HRV have been found to date. Elsewhere, spectral representation of HRV has been used to predict mental stress [26] and intervals between R-peaks (RRintervals -RRI) to detect congestive heart failure using multiinput deep learning networks [27], LSTM-based deep networks [28], and convolutional neural networks [29].…”

Section: Introductionmentioning

confidence: 99%

Heart Response to Harmonic Music Interval Stimuli Via Deep Learning Structures

Idrobo-Ávila¹,

Correa²,

Muñoz-Bolaños³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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The effect of music on the heart is reflected in variables such as heart rate and electrocardiographic (ECG) signals. ECG is a record of heart electrical activity and is a useful tool in diagnosing various cardiopathies. Artificial intelligence techniques have recently been implemented to analyze ECG and RR-interval data and are used thus in the present study to examine the influence on the heart of harmonic musical intervals and colored noise. Harmonic intervals were chosen because of their emotional response, while noise has been linked to positive responses such as improved sleep quality. A deep learning system was implemented, employing the ResNet-18 and GoogLeNet pre-trained networks to discriminate 31 different classes of ECG and RR-interval responses to the sound stimuli. Following an exploratory approach, deep learning was selected as an alternative to traditional analysis with the expectation that it could be incorporated into future music perception research. Classification revealed the ability of the implemented system to demonstrate heart response to the stimuli. ECG signals performed best, with 97% accuracy and Matthew's coefficient of 0.97, while RR-interval achieved a 93% accuracy and Matthews coefficient of 0.93, suggesting that the considered stimuli of harmonic musical intervals and noise produced different responses in the heart. Moreover, the Matthews coefficient values above 0.7 and close to 1 imply a correlation between the two types of stimuli and the heart response, as measured by ECG and RR-interval signals.

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Modelling Naturalistic Work Stress Using Spectral HRV Representations and Deep Learning

Cited by 2 publications

References 16 publications

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Heart Response to Harmonic Music Interval Stimuli Via Deep Learning Structures

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