scholarly journals Identification of Video Game Addiction Using Heart-Rate Variability Parameters

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4683
Author(s):  
Jung-Yong Kim ◽  
Hea-Sol Kim ◽  
Dong-Joon Kim ◽  
Sung-Kyun Im ◽  
Mi-Sook Kim
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Video Game ◽  
Diagnostic Method ◽  
Factor Analyses ◽  
Effective Parameters ◽  
Game Addiction ◽  
League Of Legends ◽  
Logistic Regression Equation

The purpose of this study is to determine heart rate variability (HRV) parameters that can quantitatively characterize game addiction by using electrocardiograms (ECGs). 23 subjects were classified into two groups prior to the experiment, 11 game-addicted subjects, and 12 non-addicted subjects, using questionnaires (CIUS and IAT). Various HRV parameters were tested to identify the addicted subject. The subjects played the League of Legends game for 30–40 min. The experimenter measured ECG during the game at various window sizes and specific events. Moreover, correlation and factor analyses were used to find the most effective parameters. A logistic regression equation was formed to calculate the accuracy in diagnosing addicted and non-addicted subjects. The most accurate set of parameters was found to be pNNI20, RMSSD, and LF in the 30 s after the “being killed” event. The logistic regression analysis provided an accuracy of 69.3% to 70.3%. AUC values in this study ranged from 0.654 to 0.677. This study can be noted as an exploratory step in the quantification of game addiction based on the stress response that could be used as an objective diagnostic method in the future.

scholarly journals Association of Resting Heart Rate and Heart Rate Variability With Proximal Suicidal Risk in Patients With Diverse Psychiatric Diagnoses

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongbin Lee ◽  
Ji Hyun Baek ◽  
Yun Ji Cho ◽  
Kyung Sue Hong
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Linear Regression ◽  
Risk Prediction ◽  
Suicide Risk ◽  
Psychiatric Diagnoses ◽  
Resting Heart Rate ◽  
Regression Analyses ◽  
Suicidal Risk

Objectively measurable biomarkers have not been applied for suicide risk prediction. Resting heart rate (HR) and heart rate variability (HRV) showed potential as trans-diagnostic markers associated with suicide. This study aimed to investigate the associations of resting HR and HRV on proximal suicide risk in patients with diverse psychiatric diagnoses. This chart review study used the medical records of psychiatric patients who visited the outpatient clinic at an academic tertiary hospital. A total of 1,461 patients with diverse psychiatric diagnoses was included in the analysis. Proximal suicide risk was measured using the Mini-International Neuropsychiatric Interview (MINI) suicidal score. Linear regression analyses with the MINI suicidal score as a dependent variable and binary logistic regression analyses with moderate-to-high suicide risk (MINI suicidal risk score ≥6) as a dependent variable were conducted to explore the effects of resting HR and HRV parameters on acute suicide risk after adjusting for age, sex, presence of major depressive disorder (MDD) and bipolar disorder (BD), severity of depression and anxiety severity. We found that 55 (34.6%) patients in the MDD group, 40 (41.7%) in the BD group and 36 (3.9%) in the others group reported moderate-to-high suicide risk. Linear regression analysis revealed that both resting HR and root-mean-square of successive difference (RMSSD) had significant associations with the MINI suicidal score (P = 0.037 with HR, P = 0.003 with RMSSD). In logistic regression, only RMSSD showed a significant association with moderate-to-high suicide risk (P = 0.098 with HR, P = 0.019 with RMSSD), which remained significant in subgroup analysis with patients who reported any suicide-related symptom (MINI suicidal score >0; n = 472; P = 0.017 with HR, P = 0.012 with RMSSD). Our study findings suggest the potential for resting HR and RMSSD as biomarkers for proximal suicide risk prediction. Further research with longitudinal evaluation is needed to confirm our study findings.

scholarly journals Correlation between Palpitations below the Heart in Traditional Chinese Medicine and Autonomic Nerve Function Based on Heart Rate Variability: A Case-Control Study

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chengcheng Song ◽  
Kelong Chen ◽  
Ziqian Wu ◽  
Wei Liu ◽  
Ling Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Risk Factor ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Autonomic Nerve ◽  
Nerve Function ◽  
Spearman Correlation ◽  
Autonomic Nerve Function

Objective. To explore the autonomic nerve rhythm and the correlation between palpitations below the heart (PBTH) and autonomic nerve function in patients with PBTH based on heart rate variability (HRV). Methods. The outpatients or ward patients of Wenzhou Hospital of Traditional Chinese Medicine were collected and divided into two groups: the PBTH group and the normal group. The HRV of each group was detected. Single-factor statistical methods, Spearman correlation analysis, and logistic regression were used to describe and analyze the rhythm and characteristics of autonomic nerves in patients with PBTH and the correlation between PBTH and autonomic nerve function. Results. (1) In the comparison of HRV in different time periods in the same group, the SDNN, RMSSD, pNN50, TP, and HF in the PBTH group at night were significantly higher than those in the daytime ( P < 0.01 ), while the LF/HF ratio was significantly lower than that in the daytime ( P < 0.01 ). (2) In the comparison of HRV between the two groups in the same time period, the RMSSD and pNN50 of the PBTH group during the daytime period were significantly higher than those of the normal control group ( P < 0.05 ), and the LF/HF was significantly lower than that of the normal group ( P < 0.05 ). (3) In the Spearman correlation analysis, PBTH was significantly correlated with RMSSD, pNN50, and LF/HF ratio in the daytime period, with correlation coefficients of 0.424, 0.462, and −0.524, respectively ( P < 0.05 ). (4) Logistic regression analysis showed that the decrease of LF/HF ratio during the daytime period was an independent risk factor for PBTH in TCM (OR = 0.474, 95% CI: 0.230–0.977, P < 0.05 ). Conclusions. The changes in parasympathetic nerve function in patients with PBTH have a circadian rhythm, which is characterized by increased activity during the nighttime. At the same time, the autonomic nerve activity of people with PBTH during the daytime is unbalanced, and the decrease of LF/HF ratio during the day is an independent high risk factor for PBTH.

scholarly journals Pain Monitoring Using Heart Rate Variability and Photoplethysmograph-Derived Parameters by Binary Logistic Regression

2021 ◽  
Author(s):  
D. F. Jhang ◽  
Y. S. Chu ◽  
J. H. Cai ◽  
Y. Y. Tai ◽  
C. C. Chuang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Rating Scale ◽  
Binary Logistic Regression ◽  
Numeric Rating Scale ◽  
Classification Model ◽  
Generation Process ◽  
Postoperative Intensive Care ◽  
Postoperative Intensive Care Unit

Abstract Purpose To construct a pain classification model using binary logistic regression to calculate pain probability and monitor pain based on heart rate variability (HRV) and photoplethysmography (PPG) parameters. Methods Heat stimulation was used to simulate pain for modeling the pain generation process, and electrocardiography and PPG signals were recorded simultaneously. After signal analysis, statistical analysis was performed using SPSS to determine the parameters that were significant for pain. Thereafter, a pain classification model with HRV and PPG parameters was established using binary logistic regression. Results The sensitivity and specificity of the pain classification model were 60.0% and 72.0%, respectively. When pain occurred, the probability calculated using the pain classification model increased from < 50% to > 50%. When the pain was relieved, the probability decreased to < 50%. The probability of pain was consistent with the numeric rating scale value, which indicated that the model can correctly determine the presence of pain. Conclusion This pain classification model has sufficient robustness and adaptability to be applied to different healthy people for classification and monitoring. This model is helpful in establishing a real-time pain monitoring system to improve pain management for patients in the postoperative intensive care unit and patient-controlled analgesia and provide a reference for doctors regarding medication.

scholarly journals Early prognostication of neurological outcome by heart rate variability in adult patients with out-of-hospital sudden cardiac arrest

Critical Care ◽  
2019 ◽  
Vol 23 (1) ◽  
Author(s):  
Hiroshi Endoh ◽  
Natuo Kamimura ◽  
Hiroyuki Honda ◽  
Masakazu Nitta
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Cardiac Arrest ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Neurological Outcome ◽  
Sudden Cardiac Arrest ◽  
Poor Outcome ◽  
Hrv Analysis

Abstract Background Most deaths of comatose survivors of out-of-hospital sudden cardiac arrest result from withdrawal of life-sustaining treatment (WLST) decisions based on poor neurological prognostication and the family’s intention. Thus, accurate prognostication is crucial to avoid premature WLST decisions. However, targeted temperature management (TTM) with sedation or neuromuscular blockade against shivering significantly affects early prognostication. In this study, we investigated whether heart rate variability (HRV) analysis could prognosticate poor neurological outcome in comatose patients undergoing hypothermic TTM. Methods Between January 2015 and December 2017, adult patients with out-of-hospital sudden cardiac arrest, successfully resuscitated in the emergency department and admitted to the intensive care unit of the Niigata University in Japan, were prospectively included. All patients had an initial Glasgow Coma Scale motor score of 1 and received hypothermic TTM (at 34 °C). Twenty HRV-related variables (deceleration capacity; 4 time-, 3 geometric-, and 7 frequency-domain; and 5 complexity variables) were computed based on RR intervals between 0:00 and 8:00 am within 24 h after return of spontaneous circulation (ROSC). Based on Glasgow Outcome Scale (GOS) at 2 weeks after ROSC, patients were divided into good outcome (GOS 1–2) and poor outcome (GOS 3–5) groups. Results Seventy-six patients were recruited and allocated to the good (n = 22) or poor (n = 54) outcome groups. Of the 20 HRV-related variables, ln very-low frequency (ln VLF) power, detrended fluctuation analysis (DFA) (α1), and multiscale entropy (MSE) index significantly differed between the groups (p = 0.001), with a statistically significant odds ratio (OR) by univariate logistic regression analysis (p = 0.001). Multivariate logistic regression analysis of the 3 variables identified ln VLF power and DFA (α1) as significant predictors for poor outcome (OR = 0.436, p = 0.006 and OR = 0.709, p = 0.024, respectively). The area under the receiver operating characteristic curve for ln VLF power and DFA (α1) in predicting poor outcome was 0.84 and 0.82, respectively. In addition, the minimum value of ln VLF power or DFA (α1) for the good outcome group predicted poor outcome with sensitivity = 61% and specificity = 100%. Conclusions The present data indicate that HRV analysis could be useful for prognostication for comatose patients during hypothermic TTM.

scholarly journals Predictive model of cardiac arrest in smokers using machine learning technique based on Heart Rate Variability parameter

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
R. Shashikant ◽  
P. Chetankumar
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Logistic Regression ◽  
Cardiac Arrest ◽  
Random Forest ◽  
Decision Tree ◽  
Machine Learning Technique ◽  
Forest Model ◽  
Learning Technique

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.

scholarly journals Heart Rate Variability Enhances the Accuracy of Non-invasive Continuous Blood Pressure Estimation Under Blood Loss

2020 ◽  
Author(s):  
Guang Zhang ◽  
Zongge Wang ◽  
Feixiang Hou ◽  
Huiquan Wang ◽  
Feng Chen ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Blood Loss ◽  
Pearson Correlation ◽  
Pulse Transit Time ◽  
Control Group ◽  
Effective Parameters ◽  
Ptt Model ◽  
Estimation Models

Abstract Background: To propose a new method for real-time monitoring of human blood pressure under blood loss (BPBL), this article combines pulse transit time (PTT) and heart rate variability (HRV) as input parameters in order to establish a model for the estimation of BPBL.Methods: Effective parameters such as PTT, R-R internal (RRI), and HRV were extracted and used to establish the blood pressure (BP) estimation. Three BP estimation models were established: the PTT model, the RRI model, and the HRV model, and they were divided into experimental group and control group. Finally, the effects of different estimation models on the accuracy of BPBL estimation were evaluated based on the experimental results.Results: The Pearson correlation coefficients R were 0.7731, 0.8943 and 0.9169 for the PTT model, the RRI model, and the HRV model, respectively. The root means square error of the estimation set (RMSEP) were 16.83 mmHg, 11.87 mmHg and 10.59 mmHg, respectively.Conclusion: The results suggest that the accuracy of the BPBL estimated by the RRI and HRV models is better than that of the PTT model, which means that both RRI and HRV can enhance the accuracy of BPBL estimation, and HRV seems to be more effective in improving the accuracy of BP prediction compared to RRI. These results provide a new idea for other scholars in the field of BPBL estimation research.

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