Exhibiting App and Analysis for Biofeedback-Based Mental Health Analyzer

2020 ◽  
pp. 265-286
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Mayank Gupta
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Computer Science ◽  
Experimental Research ◽  
Research Work ◽  
Tension Type Headache ◽  
Data Set ◽  
Closed Set ◽  
Skin Response ◽  
Type Headache

Many apps and analyzers based on machine learning have been designed already to help and cure the stress issue, which is increasing rapidly. The project is based on an experimental research work that the authors have performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In their research work, the correctness and accuracy have been studied and compared for two biofeedback devices, electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes—audio, visual, and audio-visual—with the help of data set of tension type headache (TTH) patients. The authors have realized by their research work that these days people have a lot of stress in their lives so they planned to make an effort for reducing the stress level of people by their technical knowledge of computer science. In their project, the authors have a website that contains a closed set of questionnaires, which have some weight associated with each question.

Automated App for Mental Health Analysis

2021 ◽  
pp. 104-131
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Mayank Gupta ◽  
Parul Singhal
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Experimental Research ◽  
Research Work ◽  
Tension Type Headache ◽  
Data Set ◽  
Closed Set ◽  
Sf 36 ◽  
Skin Response ◽  
Type Headache

Many apps and analyzers based on machine learning have been designed already to help and cure the stress issue, which is an epidemic. The project is based on an experimental research work that the authors have performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In their research work, the correctness and accuracy have been studied and compared for two biofeedback devices named as electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes—audio, visual, and audio-visual—with the help of data set of tension type headache (TTH) patients. They have realized by their research work that these days people have lot of stress in their life so they planned to make an effort for reducing the stress level of people by their technical knowledge of computer science. In their project, the authors have a website that contains a closed set of questionnaires from SF-36, which have some weight associated with each question.

Intelligent Mental Health Analyzer by Biofeedback

2020 ◽  
pp. 127-153 ◽  
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Mayank Gupta ◽  
Parul Singhal
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Experimental Research ◽  
Research Work ◽  
Tension Type Headache ◽  
Data Set ◽  
Closed Set ◽  
Sf 36 ◽  
Skin Response ◽  
Type Headache

Many apps and analyzers based on machine learning have been designed to help and cure the stress issue. The chapter is based on an experimental research work that the authors performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In the research work, the correctness and accuracy have been studied and compared for two biofeedback devices named as electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes—audio, visual, and audio-visual—with the help of data set of tension type headache (TTH) patients. The authors have realized by their research work that these days people have lot of stress in their lives, so they planned to make an effort for reducing the stress level of people by their technical knowledge of computer science. In the chapter, they have a website that contains a closed set of questionnaires from SF-36, which have some weight associated with each question.

Biofeedback-Based Mental Health Software and Its Statistical Analysis

2021 ◽  
pp. 136-155
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Sathiyamoorthi V.
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Statistical Analysis ◽  
Research Work ◽  
Tension Type Headache ◽  
Data Set ◽  
Closed Set ◽  
Sf 36 ◽  
Skin Response ◽  
Type Headache

Many apps and analyzers based on machine learning have been designed already to help and cure the stress issue, which is increasing. The project is based on an experimental research work that the authors have performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In the research work, the correctness and accuracy have been studied and compared for two biofeedback devices named as electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes, audio, visual and audio-visual, with the help of data set of tension type headache (TTH) patients. The authors have realized by their research work that these days people have lot of stress in their life so they planned to make an effort for reducing the stress level of people by their technical knowledge of computer science. In the project they have a website that contains a closed set of questionnaires from SF-36, which have some weight associated with each question.

Mental Health Through Biofeedback Is Important to Analyze

2021 ◽  
pp. 402-423
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Mayank Gupta
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Decision Tree ◽  
Research Work ◽  
Tension Type Headache ◽  
Data Set ◽  
Sf 36 ◽  
Skin Response ◽  
Type Headache ◽  
Visualization Techniques

Many apps and analyzers based on machine learning have been designed to help and cure the stress issue. This chapter is based on an experiment that the authors performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In the research work, the correctness and accuracy have been studied and compared for two biofeedback devices named as electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes: audio, visual and audio-visual with the help of data set of tension type headache (TTH) patients. The authors used some data visualization techniques that EMG (electromyography) in audio mode is best among all other modes, and in this experiment, they have used a data set of SF-36 and successfully clustered them into three clusters (i.e., low, medium, and high) using K-means algorithm. After clustering, they used classification algorithm to classify a user (depending upon the sum of all the weights of questions he had answered) into one of these three class. They have also implemented various algorithms for classifications and compared their accuracy out of which decision tree algorithm has given the best accuracy.

Tension Type Headache

2020 ◽  
pp. 318-358 ◽  
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Mayank Gupta
Keyword(s):  
Quality Of Life ◽  
Internet Of Things ◽  
Daily Life ◽  
Research Work ◽  
Tension Type Headache ◽  
Common Type ◽  
Fast Pace ◽  
Type Headache ◽  
Environment Stress

Development in the field of technology is growing with a fast pace, mainly the IoT (internet of things). It is an interface between machine-to-machine, machine-to-human, machine-to-infrastructure as well as machine-to-environment. Stress, especially TTH (tension type headache), is a serious problem in today's world. Now every person in this world is facing headache and stress-related problems in daily life. To measure the stress level, the authors have introduced the concept of EEG, EMG, and GSR biofeedback. In case of TTH, human is in a state in which one experiences pain like a physical weight or a tight band around one's head. TTH is different from migraine as it can be affected due to everyday activities. The most common type of primitive headache is tension type headache (TTH). The focus of the research work was to compare the impression of EMG-, GSR-, and EEG-integrated biofeedback on stress due to headache and quality of life of the subjects under consideration.

scholarly journals Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
O. Karasch ◽  
M. Schmitz-Buhl ◽  
R. Mennicken ◽  
J. Zielasek ◽  
E. Gouzoulis-Mayfrank
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Prediction Model ◽  
Psychiatric Hospitalization ◽  
Previous Analysis ◽  
Psychiatric Hospitals ◽  
Data Set ◽  
Interaction Detection ◽  
Socioeconomic Data

Abstract Background The purpose of this study was to identify factors associated with a high risk of involuntary psychiatric in-patient hospitalization both on the individual level and on the level of mental health services and the socioeconomic environment that patients live in. Methods The present study expands on a previous analysis of the health records of 5764 cases admitted as in-patients in the four psychiatric hospitals of the Metropolitan City of Cologne, Germany, in the year 2011 (1773 cases treated under the Mental Health Act and 3991 cases treated voluntarily). Our previous analysis had included medical, sociodemographic and socioeconomic data of every case and used a machine learning-based prediction model employing chi-squared automatic interaction detection (CHAID). Our current analysis attempts to improve the previous one through (1) optimizing the machine learning procedures (use of a different type of decision-tree prediction model (Classification and Regression Trees (CART) and application of hyperparameter tuning (HT)), and (2) the addition of patients’ environmental socioeconomic data (ESED) to the data set. Results Compared to our previous analysis, model fit was improved. Main diagnoses of an organic mental or a psychotic disorder (ICD-10 groups F0 and F2), suicidal behavior upon admission, admission outside of regular service hours and absence of outpatient treatment prior to admission were confirmed as powerful predictors of detention. Particularly high risks were shown for (1) patients with an organic mental disorder, specifically if they were retired, admitted outside of regular service hours and lived in assisted housing, (2) patients with suicidal tendencies upon admission who did not suffer from an affective disorder, specifically if it was unclear whether there had been previous suicide attempts, or if the affected person lived in areas with high unemployment rates, and (3) patients with psychosis, specifically those who lived in densely built areas with a large proportion of small or one-person households. Conclusions Certain psychiatric diagnoses and suicidal tendencies are major risk factors for involuntary psychiatric hospitalization. In addition, service-related and environmental socioeconomic factors contribute to the risk for detention. Identifying modifiable risk factors and particularly vulnerable risk groups should help to develop suitable preventive measures.

scholarly journals Identification of risk factors for involuntary psychiatric hospitalization: Using environmental socioeconomic data and methods of machine learning to improve prediction

2020 ◽  
Author(s):  
Olaf Karasch ◽  
Mario Schmitz-Buhl ◽  
R Roman Mennicken ◽  
Jürgen Zielasek ◽  
Euphrosyne Gouzoulis-Mayfrank
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Prediction Model ◽  
Suicide Attempts ◽  
Psychiatric Hospitalization ◽  
Previous Analysis ◽  
Data Set ◽  
Interaction Detection ◽  
Socioeconomic Data

Abstract Background: The purpose of this study was to identify factors associated with a high risk of involuntary psychiatric in-patient hospitalization both on the individual level and on the level of mental health services and the socioeconomic environment that patients live in. Methods: The present study expands on a previous analysis of the health records of 5764 cases admitted as in-patients in the four psy­chiat­ric hospitals of the Metropolitan City of Cologne, Germany, in the year 2011 (1773 cases treated under the Mental Health Act and 3991 cases trea­ted voluntarily). Our previous analysis had included medical, socio­demographic and socioeconomic data of every case and used a machine learning-based prediction model employing chi-squared automatic interaction detection (CHAID). Our current analysis attempts to improve the previous one through (1) optimizing the machine learning procedures (use of a different type of decision-tree prediction model (CART) and application of hyperparameter tuning), and (2) the addition of socioeconomic data on the patients’ environment to the data set. Results: Compared to our previous analysis, model fit was improved. Main diagnoses of an organic mental or a psychotic disorder (ICD-10 groups F0 and F2), suicidal behavior upon admission, admission outside of regular service hours and absence of outpatient treatment prior to admission were confirmed as powerful predictors of detention. Particularly high risks were shown for (1) patients with an organic mental disorder, specifically if they were retired, admitted outside of regular service hours and lived in assisted housing, (2) patients with suicidal tendencies upon admission who did not suffer from an affective disorder, specifically if it was unclear whether there had been previous suicide attempts, or if the affected person lived in areas with high unemployment rates, and (3) patients with psychosis, specifically those who lived in densely built areas with a large proportion of small or one-person households. Conclusions: Certain psychiatric diagnoses and suicidal tendencies are major risk factors for involuntary psychiatric hospitalization. In addition, service-related and environmental socioeconomic factors contribute to the risk for detention. Identifying modifiable risk factors and particularly vulnerable risk groups should help to develop suitable preventive measures.

scholarly journals Calabi-Yau Spaces in the String Landscape

2020 ◽  
Author(s):  
Yang-Hui He
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Data Science ◽  
Theoretical Physics ◽  
Superstring Theory ◽  
Data Set ◽  
Pure Mathematics ◽  
String Landscape ◽  
Natural Solution ◽  
Vacuum Solutions

Calabi-Yau spaces, or Kähler spaces admitting zero Ricci curvature, have played a pivotal role in theoretical physics and pure mathematics for the last half century. In physics, they constituted the first and natural solution to compactification of superstring theory to our 4-dimensional universe, primarily due to one of their equivalent definitions being the admittance of covariantly constant spinors. Since the mid-1980s, physicists and mathematicians have joined forces in creating explicit examples of Calabi-Yau spaces, compiling databases of formidable size, including the complete intersecion (CICY) data set, the weighted hypersurfaces data set, the elliptic-fibration data set, the Kreuzer-Skarke toric hypersurface data set, generalized CICYs, etc., totaling at least on the order of 1010 manifolds. These all contribute to the vast string landscape, the multitude of possible vacuum solutions to string compactification. More recently, this collaboration has been enriched by computer science and data science, the former in bench-marking the complexity of the algorithms in computing geometric quantities, and the latter in applying techniques such as machine learning in extracting unexpected information. These endeavours, inspired by the physics of the string landscape, have rendered the investigation of Calabi-Yau spaces one of the most exciting and interdisciplinary fields.

scholarly journals Big Data Analytics using Swarm Intelligence based Framework for Prediction on Datasets

2019 ◽  
Vol 8 (4) ◽  
pp. 7356-7360
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Information Source ◽  
Research Work ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Raw Data

Data Analytics is a scientific as well as an engineering tool used to investigate the raw data to revamp the information to achieve knowledge. This is normally connected with obtaining knowledge from reliable information source and rapidity in information processing, and future prediction of the data analysis. Big Data analytics is strongly evolving with different features of volume, velocity and Vectors. Most of the organizations are now concentrating on analyzing information or raw data that are fascinated in deploying analytics to survive forthcoming issues and challenges. The prediction model or intelligent model is proposed in this research to apply machine learning algorithms in the data set. Then it is interpreted and to analyze the better forecast value of the study. The major objective of this research work is to find the optimum prediction from the medical data set using the machine learning techniques.

scholarly journals Anomaly Detection using Optimized Features using Genetic Algorithm and MultiEnsemble Classifier

IJOSTHE ◽  
2018 ◽  
Vol 5 (6) ◽  
pp. 7
Author(s):  
Apoorva Deshpande ◽  
Ramnaresh Sharma
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Detection System ◽  
Research Work ◽  
Ensemble Classifier ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Machine Learning Classification

Anomaly detection system plays an important role in network security. Anomaly detection or intrusion detection model is a predictive model used to predict the network data traffic as normal or intrusion. Machine Learning algorithms are used to build accurate models for clustering, classification and prediction. In this paper classification and predictive models for intrusion detection are built by using machine learning classification algorithms namely Random Forest. These algorithms are tested with KDD-99 data set. In this research work the model for anomaly detection is based on normalized reduced feature and multilevel ensemble classifier. The work is performed in divided into two stages. In the first stage data is normalized using mean normalization. In second stage genetic algorithm is used to reduce number of features and further multilevel ensemble classifier is used for classification of data into different attack groups. From result analysis it is analysed that with reduced feature intrusion can be classified more efficiently.

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