Personal Adaptive Method to Assess Mental Tension during Daily Life Using Heart Rate Variability

2012 ◽  
Vol 51 (01) ◽  
pp. 39-44 ◽  
Author(s):  
K. Matsuoka ◽  
K. Yoshino
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Test Data ◽  
Daily Life ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Training Data ◽  
Data Set

SummaryObjectives: The aim of this study is to present a method of assessing psychological tension that is optimized to every individual on the basis of the heart rate variability (HRV) data which, to eliminate the influence of the inter-individual variability, are measured in a long time period during daily life.Methods: HRV and body accelerations were recorded from nine normal subjects for two months of normal daily life. Fourteen HRV indices were calculated with the HRV data at 512 seconds prior to the time of every mental tension level report. Data to be analyzed were limited to those with body accelerations of 30 mG (0.294 m/s2) and lower. Further, the differences from the reference values in the same time zone were calculated with both the mental tension score (Δtension) and HRV index values (ΔHRVI). The multiple linear regression model that estimates Δtension from the scores for principal components of ΔHRVI were then constructed for each individual. The data were divided into training data set and test data set in accordance with the twofold cross validation method. Multiple linear regression coefficients were determined using the training data set, and with the optimized model its generalization capability was checked using the test data set.Results: The subjects’ mean Pearson correlation coefficient was 0.52 with the training data set and 0.40 with the test data set. The subjects’ mean coefficient of determination was 0.28 with the training data set and 0.11 with the test data set.Conclusion: We proposed a method of assessing psychological tension that is optimized to every individual based on HRV data measured over a long period of daily life.

scholarly journals Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yashon O. Ouma ◽  
Clinton O. Okuku ◽  
Evalyne N. Njau
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring

The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.

scholarly journals Housing Price Prediction Based on Multiple Linear Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qingqi Zhang
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Housing Prices ◽  
Housing Price ◽  
Multiple Linear Regression Model ◽  
Data Set ◽  
Factors Affecting ◽  
Price Prediction

In this paper, the author first analyzes the major factors affecting housing prices with Spearman correlation coefficient, selects significant factors influencing general housing prices, and conducts a combined analysis algorithm. Then, the author establishes a multiple linear regression model for housing price prediction and applies the data set of real estate prices in Boston to test the method. Through the data analysis and test in this paper, it can be summarized that the multiple linear regression model can effectively predict and analyze the housing price to some extent, while the algorithm can still be improved through more advanced machine learning methods.

scholarly journals Are Hyoid Bone and Tongue the Risk Factors Contributing to Postoperative Relapse for Mandibular Prognathism?

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Yu-Chuan Tseng ◽  
Steven Lai ◽  
Huey-Er Lee ◽  
Ker-Kong Chen ◽  
Chun-Ming Chen
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Hyoid Bone ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Mandibular Prognathism ◽  
Mandibular Setback ◽  
Lateral Cephalograms ◽  
Intraoral Vertical Ramus Osteotomy ◽  
One Year

Objective. The purpose of this study was to investigate postoperative stability and the correlation between hyoid, tongue, and mandible position following surgery for mandibular prognathism.Materials and Methods. Thirty-seven patients, treated for mandibular prognathism using intraoral vertical ramus osteotomy (IVRO), were evaluated cephalometrically. A set of four standardized lateral cephalograms were obtained from each subject preoperatively (T1), immediately postoperatively (T2), six weeks to three months postoperatively (T3), and more than one year postoperatively (T4). The Studentt-tests, the Pearson correlation coefficient, and the multiple linear regression were used for statistical analysis.Results. Immediately after surgery, menton (Me) setback was 12.8 mm, hyoid (H) setback was 4.9 mm, and vallecula epiglottica (V) setback was 5.8 mm. The postoperative stability significantly correlated (r=-0.512,p<0.01) with the amount of setback. The hyoid bone and tongue did not have significant effects on postoperative stability. Multiple linear regression model (R2=0.2658,p<0.05) showed predictability: Horizontal Relapse Me (T4-T2) = −6.406 − 0.488Me (T2-T1) + 0.069H (T2-T1) − 0.0619V (T2-T1).Conclusion. Mandibular setback surgery may push the hyoid and tongue significantly backward, but this did not correlate with mandibular relapse. Postoperative stability significantly correlated with the amount of mandibular setback.

scholarly journals Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 127
Author(s):  
Christos Mattas ◽  
Lamprini Dimitraki ◽  
Pantazis Georgiou ◽  
Panagiota Venetsanou
Keyword(s):  
Electrical Conductivity ◽  
Factor Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Management Practices ◽  
Pearson Correlation ◽  
Northern Greece ◽  
Data Set ◽  
Short Period ◽  
Artificial Neural

Due to the fact of water resource deterioration from human activities and increased demand over the last few decades, optimization of management practices and policies is required, for which more reliable data are necessary. Cost and time are always of importance; therefore, methods that can provide low-cost data in a short period of time have been developed. In this study, the ability of an artificial neural network (ANN) and a multiple linear regression (MLR) model to predict the electrical conductivity of groundwater samples in the GallikosRiver basin, northern Greece, was examined. A total of 233 samples were collected over the years 2004–2005 from 89 sampling points. Descriptive statistics, Pearson correlation matrix, and factor analysis were applied to select the inputs of the water quality parameters. Input data to the ANN and MLR were Ca, Mg, Na, and Cl. The best results regarding the ANN were provided by a model that included one hidden layer of three neurons. The mean absolute percentage error, modeling efficiency, and root mean square error were used to evaluate the performances of the methods and to compare the prediction capabilities of the ANN and MLR. We concluded that the ANN and MLR models were valid and had similar accuracy (using the same inputs) with a large number of samples, but in the case of a smaller data set, the MLR showed a better performance.

scholarly journals Mindfulness and Its Predictors in Women With Polycystic Ovary Syndrome

2020 ◽  
Vol 24 (3) ◽  
pp. 246-257
Author(s):  
Roghayeh Arbabi Moghaddam ◽  
◽  
Seyedeh Batool Hasanpoor-Azghady ◽  
Leila Amiri Farahani ◽  
Shima Haghani ◽  
...  
Keyword(s):  
Linear Regression ◽  
Polycystic Ovary Syndrome ◽  
Multiple Linear Regression ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Multiple Linear Regression Analysis ◽  
Reproductive Age ◽  
Ovary Syndrome

Background: Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorder in women of reproductive age which can cause many problems such as hyperandrogenic symptoms and fertility problems. Objective: The present study aimed to determine the relationship of mindfulness with hyperandrogenic symptoms and demographic and fertility factors in women with PCOS. Methods: This descriptive correlational study was conducted on 181 women with PCOS referred to Firoozabadi and Firoozgar hospitals in Tehran, Iran who were selected using a continuous sampling method and based on inclusion criteria from June 2018 to August 2019. Data were collected using a demographic/fertility form, the modified Ferriman-Gallwey Scale, Ludwig Hair Loss Scale, and Mindfulness Attention Awareness Scale (MAAS). Data were analyzed using independent t-test, one-way ANOVA, Kruskal-Wallis test, Pearson correlation test, and multiple linear regression analysis. Findings: The mean MAAS score of women was 68.61±9.88 and was significantly correlated with age (P=0.01), wife’s education (P=0.001), wife’s occupation (P=0.005), economic status (P=0.02), husband satisfaction with wife’s body and appearance (P=0.02), body mass index (P=0.01), and duration of marriage (P<0.001). According to the multiple linear regression model, the duration of marriage could predict 22% of the variance in overall MAAS score. Conclusion: Mindfulness is associated with some demographic variables, among which the marriage is its predictor. It is recommended to pay attention to the reported variables in preparation of counseling or educational programs, along with other treatments, for women with PCOS.

scholarly journals Predicting Heart Rate Variability Parameters in Healthy Korean Adults: A Preliminary Study

2021 ◽  
Vol 58 ◽  
pp. 004695802110562
Author(s):  
Sung-Woo Kim ◽  
Hun-Young Park ◽  
Won-Sang Jung ◽  
Kiwon Lim
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Linear Regression ◽  
Regression Model ◽  
Frequency Domain ◽  
Time Domain ◽  
Multiple Linear Regression Model ◽  
Coefficient Of Determination ◽  
Independent Variables

The purpose of the study was to examine the development of a multiple linear regression model to estimate heart rate variability (HRV) parameters using easy-to-measure independent variables in preliminary experiments. HRV parameters (time domain: SDNN, RMSSD, NN50, pNN50; frequency domain: TP, VLF, LF, HF) and the independent variables (e.g., sex, age, body height, body weight, BMI, HR, HRmax, HRR) were measured in 75 healthy adults (male n = 27, female n = 48) for estimating HRV. The HRV estimation multiple linear regression model was developed using the backward elimination technique. The regression model’s coefficient of determination for the time domain variables was significantly high (SDNN = R2: 72.2%, adjusted R2: 69.8%, P < .001; RMSSD = R2: 93.1%, adjusted R2: 92.1%, P < .001; NN50 = R2: 78.0%, adjusted R2: 74.9%, P < .001; pNN50 = R2: 89.1%, adjusted R2: 87.4%, P < .001). The coefficient of determination of the regression model for the frequency domain variable was moderate (TP = R2: 75.6%, adjusted R2: 72.6%, P < .001; VLF = R2: 41.6%, adjusted R2: 40.3%, P < .001; LF = R2: 54.6%, adjusted R2: 49.2%, P < .001; HF = R2: 67.5%, adjusted R2: 63.4%, P < .001). The coefficient of determination of time domain variables in the developed multiple regression models was shown to be very high (adjusted R2: 69.8%–92.1%, P < .001), but the coefficient of determination of frequency domain variables was moderate (adjusted R2: 40.3%–72.6%, P < .001). In addition to the equipment used for measuring HRV in clinical trials, this study confirmed that simple physiological variables could predict HRV.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Evaluation of Importance of Central Effects of Atenolol and Metoprolol Measured by Heart Rate Variability During Mental Performance Tasks, Physical Exercise, and Daily Life in Stable Postinfarct Patients

Circulation ◽  
1995 ◽  
Vol 92 (12) ◽  
pp. 3415-3423 ◽  
Author(s):  
Ype S. Tuininga ◽  
Harry J.G.M. Crijns ◽  
Jan Brouwer ◽  
Maarten P. van den Berg ◽  
Arie J. Man in’t Veld ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Physical Exercise ◽  
Daily Life ◽  
Mental Performance ◽  
Performance Tasks ◽  
Central Effects

Predictive Factors for Hamstring Autograft Diameter in Anterior Cruciate Ligament Reconstruction

2019 ◽  
Author(s):  
Willem M.P. Heijboer ◽  
Mathijs A.M. Suijkerbuijk ◽  
Belle L. van Meer ◽  
Eric W.P. Bakker ◽  
Duncan E. Meuffels
Keyword(s):  
Anterior Cruciate Ligament ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Gracilis Tendon ◽  
Thigh Circumference ◽  
Anterior Cruciate

AbstractMultiple studies found hamstring tendon (HT) autograft diameter to be a risk factor for anterior cruciate ligament (ACL) reconstruction failure. This study aimed to determine which preoperative measurements are associated with HT autograft diameter in ACL reconstruction by directly comparing patient characteristics and cross-sectional area (CSA) measurement of the semitendinosus and gracilis tendon on magnetic resonance imaging (MRI). Fifty-three patients with a primary ACL reconstruction with a four-stranded HT autograft were included in this study. Preoperatively we recorded length, weight, thigh circumference, gender, age, preinjury Tegner activity score, and CSA of the semitendinosus and gracilis tendon on MRI. Total CSA on MRI, weight, height, gender, and thigh circumference were all significantly correlated with HT autograft diameter (p < 0.05). A multiple linear regression model with CSA measurement of the HTs on MRI, weight, and height showed the most explained variance of HT autograft diameter (adjusted R 2 = 44%). A regression equation was derived for an estimation of the expected intraoperative HT autograft diameter: 1.2508 + 0.0400 × total CSA (mm2) + 0.0100 × weight (kg) + 0.0296 × length (cm). The Bland and Altman analysis indicated a 95% limit of agreement of ± 1.14 mm and an error correlation of r = 0.47. Smaller CSA of the semitendinosus and gracilis tendon on MRI, shorter stature, lower weight, smaller thigh circumference, and female gender are associated with a smaller four-stranded HT autograft diameter in ACL reconstruction. Multiple linear regression analysis indicated that the combination of MRI CSA measurement, weight, and height is the strongest predictor.

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