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Between-subject correlation of heart rate variability predicts movie preferences.

ABSTRACT: We introduce a novel and simple method for assessing audiences' emotional responses to audiovisuals (e.g. films). Viewers (N = 21) watched movies and TV commercials from different genres while wearing photoplethysmography (PPG) optic sensors on their wrists. Heart rate variability (HRV) synchrony was observed among the audience. Based on this between-subject synchrony measure, we identified emotionally arousing segments from the materials. New participants (N = 24; N = 16) were then invited to watch these identified segments along with some randomly selected segments as control; they reported that the former was more engaging (effect size w = .67; w = .5). This finding was confirmed in an online study with a larger cohort (N = 300). While some specific effects varied depending on movie genre or gender, HRV-based editing generally performed better than the control. These findings suggest that HRV synchrony can be used as a new tool for audience psychology, and potentially also for automatically creating short trailers out of movies in a principled manner while taking into account the human perspective.

SUBMITTER: So TY

PROVIDER: S-EPMC7904173 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Between-subject correlation of heart rate variability predicts movie preferences.

So Tsz Yan TY Li Man Yi Erica MYE Lau Hakwan H

PloS one 20210224 2

We introduce a novel and simple method for assessing audiences' emotional responses to audiovisuals (e.g. films). Viewers (N = 21) watched movies and TV commercials from different genres while wearing photoplethysmography (PPG) optic sensors on their wrists. Heart rate variability (HRV) synchrony was observed among the audience. Based on this between-subject synchrony measure, we identified emotionally arousing segments from the materials. New participants (N = 24; N = 16) were then invited to w ...[more]

PMID: 33626088

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Project description:ObjectiveTo explore the correlation between Heart Rate Variability Index (HRV) and poor prognosis in patients with acute decompensated heart failure (ADHF).MethodsA retrospective compilation of clinical data encompassed 128 cases of patients afflicted with acute decompensated heart failure (ADHF) who were admitted to and discharged from our hospital between April 2019 and July 2022. Subsequent to assessing their follow-up progress during the tracking period, the subjects were categorized into two cohorts: the poor prognosis group (n = 31) and the good prognosis group (n = 97). Comparative analysis of clinical data and Heart Rate Variability (HRV) parameters was executed between these two groups. Moreover, a multiple linear regression analysis was employed to identify the contributing factors associated with adverse prognoses in ADHF patients. Furthermore, the receiver operating characteristic (ROC) curve was employed to evaluate the prognostic predictive capability of HRV parameters among ADHF patients.ResultsThe levels of SDNN (t = 3.924, P < 0.001), SDANN (t = 4.520, P < 0.001) and LF (t = 2.676, P = 0.018) in the poor prognosis group were significantly higher than those in the good prognosis group, and the differences were statistically significant (P < 0.05). The levels of PNN50 (t = 2.132, P = 0.035), HF (t = 11.781, P < 0.001) and LF/HF (t = 11.056, P < 0.001) in the poor prognosis group were significantly lower than those in the good prognosis group (P < 0.05). The results of multiple linear regression analysis indicated that SDNN, SDANN, LF, PNN50, and HF were factors influencing poor prognosis in ADHF patients (P < 0.05). The results of the ROC curve analysis indicate that the area under the curve (AUC) for predicting poor prognosis in ADHF patients using HRV parameters were as follows: SDNN (AUC = 0.818, 95% CI [0.722-0.914]), SDANN (AUC = 0.684, 95% CI [0.551-0.816]), PNN50 (AUC = 0.754, 95% CI [0.611-0.841]), LF/HF (AUC = 0.787, 95% CI [0.679-0.896]), and combined diagnosis (AUC = 0.901, 95% CI [0.832-0.970]). Among these, the combined diagnosis exhibited the highest AUC, sensitivity, and specificity for predicting poor prognosis in ADHF patients (P < 0.001).ConclusionThe HRV parameters of SDNN, SDANN, PNN50 and LF/HF are closely related to the prognosis of ADHF patients. The combined detection of the above HRV parameters can improve the efficacy of predicting the poor prognosis of ADHF patients. This suggests that clinical staff can identify ADHF patients at risk of poor prognosis by long-term monitoring of HRV in the future.

Project description:Heart rate variability (HRV) can reflect the changes in the autonomic nervous system (ANS) that are affected by apnea or hypopnea events among patients with obstructive sleep apnea hypopnea syndrome (OSAHS). To evaluate the possibility of using HRV to screen for OSAHS, we investigated the relationship between HRV and polysomnography (PSG) diagnostic indices using electrocardiography (ECG) and PSG data from 25 patients with OSAHS and 27 healthy participants. We evaluated the relationship between various PSG diagnostic indices (including the apnea hypopnea index [AHI], micro-arousal index [MI], oxygen desaturation index [ODI]) and heart rate variability (HRV) parameters using Spearman's correlation analysis. Moreover, we used multiple linear regression analyses to construct linear models for the AHI, MI, and ODI. In our analysis, the AHI was significantly associated with relative powers of very low frequency (VLF [%]) (r = 0.641, P = 0.001), relative powers of high frequency (HF [%]) (r = -0.586, P = 0.002), ratio between low frequency and high frequency powers (LF/HF) (r = 0.545, P = 0.049), normalized powers of low frequency (LF [n.u.]) (r = 0.506, P = 0.004), and normalized powers of high frequency (HF [n.u.]) (r = -0.506, P = 0.010) among patients with OSAHS. The MI was significantly related to standard deviation of RR intervals (SDNN) (r = 0.550, P = 0.031), VLF [%] (r = 0.626, P = 0.001), HF [%] (r = -0.632, P = 0.001), LF/HF (r = 0.591, P = 0.011), LF [n.u.] (r = 0.553, P = 0.004), HF [n.u.] (r = -0.553, P = 0.004), and absolute powers of very low frequency (VLF [abs]) (r = 0.525, P = 0.007) among patients with OSAHS. The ODI was significantly correlated with VLF [%] (r = 0.617, P = 0.001), HF [%] (r = -0.574, P = 0.003), LF [n.u.] (r = 0.510, P = 0.012), and HF [n.u.] (r = -0.510, P = 0.012) among patients with OSAHS. The linear models for the PSG diagnostic indices were AHI = -38.357+1.318VLF [%], MI = -13.389+11.297LF/HF+0.266SDNN, and ODI = -55.588+1.715VLF [%]. However, the PSG diagnostic indices were not related to the HRV parameters among healthy participants. Our analysis suggests that HRV parameters are powerful tools to screen for OSAHS patients in place of PSG monitoring.

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Between-subject correlation of heart rate variability predicts movie preferences.

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Between-subject correlation of heart rate variability predicts movie preferences.

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