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.

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.

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.

IoT Applications to Cure TTH Using Different Biofeedback

2020 ◽  
pp. 174-224
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi
Keyword(s):  
Quality Of Life ◽  
Skin Resistance ◽  
Research Work ◽  
Tension Type Headache ◽  
Common Type ◽  
Emg Biofeedback ◽  
Starting Point ◽  
Sf 36 ◽  
Type Headache

The most common type of primitive headache is the tension type headache (TTH). The focus of complete 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. EMG biofeedback (BF) and galvanic skin resistance (GSR) are considered effective therapy for headaches. There are no such comparative effects of visual and auditory EMG biofeedback for headaches. Twenty subjects were subjected to EEG therapy. The variables for stress (pain) and SF-36(Quality of life) scores were recorded at starting point, 30 days and 90 days after the starting of GSR and EMG-BF therapy.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

2012 ◽  
pp. 155-165
Author(s):  
S. Prasanthi ◽  
S.Durga Bhavani ◽  
T. Sobha Rani ◽  
Raju S. Bapi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Learning Techniques

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.

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.

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