A physiological valence/arousal model from musical rhythm to heart rhythm

Wang, Huimin; Lee, Yaw-Chern; Yen, Brad S.; Wang, Chun-Yen; Huang, Sheng‐Chieh; Tang, Kea-Tiong

doi:10.1109/iscas.2011.5937740

Cited by 3 publications

(2 citation statements)

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“…One of the physiological actions, heart rate variability (HRV), which is controlled by the autonomic nervous system (ANS), is tightly connected with emotions [4]. Previously, we had analyzed the relationship between musical rhythms and HRV [5] and built two heuristic models [6,7]. In this paper, a systematic algorithm is proposed to construct new models.…”

Section: Introductionmentioning

confidence: 99%

Musical Rhythms Affect Heart Rate Variability: Algorithm and Models

Wang

Huang

2014

Advances in Electrical Engineering

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There were a lot of psychological music experiments and models but there were few psychological rhythm experiments and models. There were a lot of physiological music experiments but there were few physiological music models. There were few physiological rhythm experiments but there was no physiological rhythm model. We proposed a physiological rhythm model to fill this gap. Twenty-two participants, 4 drum loops as stimuli, and electrocardiogram (ECG) were employed in this work. We designed an algorithm to map tempo, complexity, and energy into two heart rate variability (HRV) measures, the standard deviation of normal-to-normal heartbeats (SDNN) and the ratio of low- and high-frequency powers (LF/HF); these two measures form the physiological valence/arousal plane. There were four major findings. Initially, simple and loud rhythms enhanced arousal. Secondly, the removal of fast and loud rhythms decreased arousal. Thirdly, fast rhythms increased valence. Finally, the removal of fast and quiet rhythms increased valence. Our work extended the psychological model to the physiological model and deepened the musical model into the rhythmic model. Moreover, this model could be the rules of automatic music generating systems.

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Section: Introductionmentioning

confidence: 99%

Musical Rhythms Affect Heart Rate Variability: Algorithm and Models

Wang

Huang

2014

Advances in Electrical Engineering

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“…The time domain analysis reports the activity of circulatory system and the frequency domain analysis reflects the sympathovagal balance of autonomic nervous system (ANS) [9][10][11][12][13]. In our previous works, this standard routine had been modified to MATLAB codes [14,15] and the QRS detection algorithm had also been implemented in field programmable gate array (FPGA) [16].…”

Section: Introductionmentioning

confidence: 99%

A ±6 ms‐Accuracy, 0.68 mm², and 2.21 μW QRS Detection ASIC

2012

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Healthcare issues arose from population aging. Meanwhile, electrocardiogram (ECG) is a powerful measurement tool. The first step of ECG is to detect QRS complexes. A state-of-the-art QRS detection algorithm was modified and implemented to an application-specific integrated circuit (ASIC). By the dedicated architecture design, the novel ASIC is proposed with 0.68 mm2 core area and 2.21 μW power consumption. It is the smallest QRS detection ASIC based on 0.18 μm technology. In addition, the sensitivity is 95.65% and the positive prediction of the ASIC is 99.36% based on the MIT/BIH arrhythmia database certification.

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