Kishore Kanna R, B. Panigrahi, S. Sahoo, Anugu Rohith Reddy, Yugandhar Manchala, Nirmal Keshari Swain
INTRODUCTION: Because it has various benefits in areas such psychology, human-computer interaction, and marketing, the recognition of facial expressions has gained a lot of attention lately. OBJECTIVES: Convolutional neural networks (CNNs) have shown enormous potential for enhancing the accuracy of facial emotion identification systems. In this study, a CNN-based approach for recognizing facial expressions is provided. METHODS: To boost the model's generalizability, transfer learning and data augmentation procedures are applied. The recommended strategy defeated the existing state- of-the-art models when examined on multiple benchmark datasets, including the FER-2013, CK+, and JAFFE databases. RESULTS: The results suggest that the CNN-based approach is fairly excellent at properly recognizing face emotions and has a lot of potential for usage in detecting facial emotions in practical scenarios. CONCLUSION: Several diverse forms of information, including oral, textual, and visual, maybe applied to comprehend emotions. In order to increase prediction accuracy and decrease loss, this research recommended a deep CNN model for emotion prediction from facial expression.
{"title":"CNN Based Face Emotion Recognition System for Healthcare Application","authors":"Kishore Kanna R, B. Panigrahi, S. Sahoo, Anugu Rohith Reddy, Yugandhar Manchala, Nirmal Keshari Swain","doi":"10.4108/eetpht.10.5458","DOIUrl":"https://doi.org/10.4108/eetpht.10.5458","url":null,"abstract":"INTRODUCTION: Because it has various benefits in areas such psychology, human-computer interaction, and marketing, the recognition of facial expressions has gained a lot of attention lately. \u0000OBJECTIVES: Convolutional neural networks (CNNs) have shown enormous potential for enhancing the accuracy of facial emotion identification systems. In this study, a CNN-based approach for recognizing facial expressions is provided. METHODS: To boost the model's generalizability, transfer learning and data augmentation procedures are applied. The recommended strategy defeated the existing state- of-the-art models when examined on multiple benchmark datasets, including the FER-2013, CK+, and JAFFE databases. \u0000RESULTS: The results suggest that the CNN-based approach is fairly excellent at properly recognizing face emotions and has a lot of potential for usage in detecting facial emotions in practical scenarios. \u0000CONCLUSION: Several diverse forms of information, including oral, textual, and visual, maybe applied to comprehend emotions. In order to increase prediction accuracy and decrease loss, this research recommended a deep CNN model for emotion prediction from facial expression.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"40 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140231425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kishore Kanna R, R. Ravindraiah, C. Priya, R. Gomalavalli, Nimmagadda Muralikrishna
INTRODUCTION: The field of medical diagnostics is currently confronted with a significant obstacle in the shape of cancer, a disease that tragically results in the loss of millions of lives each year. Ensuring the administration of appropriate treatment to cancer patients is of paramount significance for medical practitioners. OBJECTIVES: Hence, the accurate identification of cancer cells holds significant importance. The timely identification of a condition can facilitates prompt diagnosis and intervention. Numerous researchers have devised multiple methodologies for the early detection of cancer. METHODS: The accurate anticipation of cancer has consistently posed a significant and formidable undertaking for medical professionals and researchers. This article examines various neural network technologies utilised in the diagnosis of cancer. RESULTS: Neural networks have emerged as a prominent area of research within the medical science field, particularly in disciplines such as cardiology, radiology, and oncology, among others. CONCLUSION: The findings of this survey indicate that neural network technologies demonstrate a high level of efficacy in the diagnosis of cancer. A significant proportion of neural networks exhibit exceptional precision when it comes to categorizing tumours cells.
{"title":"Clinical Application of Neural Network for Cancer Detection Application","authors":"Kishore Kanna R, R. Ravindraiah, C. Priya, R. Gomalavalli, Nimmagadda Muralikrishna","doi":"10.4108/eetpht.10.5454","DOIUrl":"https://doi.org/10.4108/eetpht.10.5454","url":null,"abstract":" \u0000INTRODUCTION: The field of medical diagnostics is currently confronted with a significant obstacle in the shape of cancer, a disease that tragically results in the loss of millions of lives each year. Ensuring the administration of appropriate treatment to cancer patients is of paramount significance for medical practitioners. \u0000OBJECTIVES: Hence, the accurate identification of cancer cells holds significant importance. The timely identification of a condition can facilitates prompt diagnosis and intervention. Numerous researchers have devised multiple methodologies for the early detection of cancer. \u0000METHODS: The accurate anticipation of cancer has consistently posed a significant and formidable undertaking for medical professionals and researchers. This article examines various neural network technologies utilised in the diagnosis of cancer. \u0000RESULTS: Neural networks have emerged as a prominent area of research within the medical science field, particularly in disciplines such as cardiology, radiology, and oncology, among others. \u0000CONCLUSION: The findings of this survey indicate that neural network technologies demonstrate a high level of efficacy in the diagnosis of cancer. A significant proportion of neural networks exhibit exceptional precision when it comes to categorizing tumours cells.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"39 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140234458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Khalid Been, Badruzzaman Biplob, Musabbir Hasan Sammak, Abu Kowshir Bitto, Imran Mahmud
INTRODUCTION: Pandemics and epidemics have frequently led to a significant increase in the suicide rate in affected regions. However, these unnecessary deaths can be prevented by identifying the risk factors and intervening earlier with those at risk. Numerous empirical studies have exhaustively documented multiple suicide risk factors. In addition, many evidence-based approaches have employed machine learning models to diagnose vulnerable groups, a task that would otherwise be challenging if only human cognition were employed. To date, to the best of our knowledge, no research has been conducted on COVID-19-related suicide prediction.OBJECTIVES: This research, aims to develop a machine-learning model capable of identifying individuals who are contemplating suicide due to COVID-19-related complexities and assessing the potential risk factors.METHODS: We trained a gradient-boosting model based on tree-based learners on 10067 data consisting of 76 features, which were primarily responses to socio-demographic, behavioural, and psychological questions about COVID-19 and suicidal behaviours.RESULTS: The final model predicted individuals at risk with an auROC score of 0.77 and a 95% confidence interval of 0.77 to 0.88. The optimal cutoff produced a sensitivity of 31.37 percent and a specificity of 82.35 percent in predicting suicidal tendencies. However, the auPRC was only 0.26, with a 95 percent confidence interval of 0.13 to 0.38, as the class distribution was extremely unbalanced. Consequently, the scores for precision and recall were 0.35 and 0.31, respectively.CONCLUSION: We investigated the risk factors, the majority of which were associated with sleeping difficulties, fear of COVID-19, social interactions, and other socio-demographic factors. The identified risk factors can be considered when formulating a policy to prevent COVID-19-related suicides, which can impose a long-term economic and health burden on society.
{"title":"COVID-19 and Suicide Tendency: Prediction and Risk Factor Analysis Using Machine Learning and Explainable AI","authors":"Khalid Been, Badruzzaman Biplob, Musabbir Hasan Sammak, Abu Kowshir Bitto, Imran Mahmud","doi":"10.4108/eetpht.10.3070","DOIUrl":"https://doi.org/10.4108/eetpht.10.3070","url":null,"abstract":"INTRODUCTION: Pandemics and epidemics have frequently led to a significant increase in the suicide rate in affected regions. However, these unnecessary deaths can be prevented by identifying the risk factors and intervening earlier with those at risk. Numerous empirical studies have exhaustively documented multiple suicide risk factors. In addition, many evidence-based approaches have employed machine learning models to diagnose vulnerable groups, a task that would otherwise be challenging if only human cognition were employed. To date, to the best of our knowledge, no research has been conducted on COVID-19-related suicide prediction.OBJECTIVES: This research, aims to develop a machine-learning model capable of identifying individuals who are contemplating suicide due to COVID-19-related complexities and assessing the potential risk factors.METHODS: We trained a gradient-boosting model based on tree-based learners on 10067 data consisting of 76 features, which were primarily responses to socio-demographic, behavioural, and psychological questions about COVID-19 and suicidal behaviours.RESULTS: The final model predicted individuals at risk with an auROC score of 0.77 and a 95% confidence interval of 0.77 to 0.88. The optimal cutoff produced a sensitivity of 31.37 percent and a specificity of 82.35 percent in predicting suicidal tendencies. However, the auPRC was only 0.26, with a 95 percent confidence interval of 0.13 to 0.38, as the class distribution was extremely unbalanced. Consequently, the scores for precision and recall were 0.35 and 0.31, respectively.CONCLUSION: We investigated the risk factors, the majority of which were associated with sleeping difficulties, fear of COVID-19, social interactions, and other socio-demographic factors. The identified risk factors can be considered when formulating a policy to prevent COVID-19-related suicides, which can impose a long-term economic and health burden on society.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"138 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140233895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Malaria, an infectious illness spread by mosquitoes, is a serious hazard to humans and animals, with an increasing number of cases recorded yearly. Prompt and precise diagnosis, as well as preventative actions, are critical for effectively combating this condition. Malaria is now diagnosed using standard techniques. Microscopy of blood smears, which consists of small pictures, is used by trained specialists to identify diseased cells and define their life phases. The World Health Organisation (WHO) has approved this microscopy-based malaria diagnostic method. Drawing a blood sample from the finger, pricking it, spreading it onto a clean glass slide, and allowing it to dry naturally are all steps in the method. Thin blood smears were previously used to identify parasites under the microscope, but thick blood smears are utilized when parasite levels are low. OBJECTIVES: Due to its reliance on medical knowledge, high prices, time-consuming nature, and unsatisfactory outcomes, this technique has significant disadvantages. However, as deep learning algorithms progress, these activities may be completed more effectively and with fewer human resources. METHODS: This study demonstrates the usefulness of transfer learning, a type of deep learning, in categorizing microscopic pictures of parasitized versus uninfected malaria cells. Six models were evaluated using the publicly accessible NIH dataset, proving the usefulness of the suggested technique. RESULTS: VGG19 model fared better than its competitors, obtaining 95.05% accuracy, 92.83% precision, 96.88% sensitivity, 93.46% specificity, and 94.81% F1-score. CONCLUSION: This categorization of malaria cell photos will benefit microscopists in particular, as it will improve their workflow and provide a viable alternative for detecting malaria using microscopic cell images.
{"title":"Automated Life Stage Classification of Malaria Using Deep Learning","authors":"Janjhyam Venkata Naga Ramesh, Raghav Agarwal, Harshitha Jyasta, Bommisetty Sivani, Palacholla Anuradha Sri Tulasi Mounika, Bollineni Bhargavi","doi":"10.4108/eetpht.10.5439","DOIUrl":"https://doi.org/10.4108/eetpht.10.5439","url":null,"abstract":"INTRODUCTION: Malaria, an infectious illness spread by mosquitoes, is a serious hazard to humans and animals, with an increasing number of cases recorded yearly. Prompt and precise diagnosis, as well as preventative actions, are critical for effectively combating this condition. Malaria is now diagnosed using standard techniques. Microscopy of blood smears, which consists of small pictures, is used by trained specialists to identify diseased cells and define their life phases. The World Health Organisation (WHO) has approved this microscopy-based malaria diagnostic method. Drawing a blood sample from the finger, pricking it, spreading it onto a clean glass slide, and allowing it to dry naturally are all steps in the method. Thin blood smears were previously used to identify parasites under the microscope, but thick blood smears are utilized when parasite levels are low. \u0000OBJECTIVES: Due to its reliance on medical knowledge, high prices, time-consuming nature, and unsatisfactory outcomes, this technique has significant disadvantages. However, as deep learning algorithms progress, these activities may be completed more effectively and with fewer human resources. \u0000METHODS: This study demonstrates the usefulness of transfer learning, a type of deep learning, in categorizing microscopic pictures of parasitized versus uninfected malaria cells. Six models were evaluated using the publicly accessible NIH dataset, proving the usefulness of the suggested technique. \u0000RESULTS: VGG19 model fared better than its competitors, obtaining 95.05% accuracy, 92.83% precision, 96.88% sensitivity, 93.46% specificity, and 94.81% F1-score. \u0000CONCLUSION: This categorization of malaria cell photos will benefit microscopists in particular, as it will improve their workflow and provide a viable alternative for detecting malaria using microscopic cell images.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140239894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Basketball, as a high-intensity sport, has attracted much attention for its effects on the cardiovascular system of athletes. The anterior and posterior portal veins are some of the vital blood vessels in the human circulatory system, and their blood flow is closely related to the athletes' physical status. Doppler ultrasound technology is widely used in sports medicine and provides a powerful tool for an in-depth understanding of the effects of basketball on portal vein blood flow. This study aimed to explore the potential of sports medicine technology in assessing cardiovascular adaptations in athletes through portal Doppler imaging before and after basketball exercise.OBJECTIVES: The primary objective of this study was to analyze the effects of basketball exercise on portal vein blood flow in athletes before and after basketball exercise through the use of Doppler ultrasound technology. Specifically, this study aimed to explore the dynamics of pre- and post-exercise Doppler imaging of the posterior and posterior veins in order to assess the cardiovascular adaptations of athletes during exercise more comprehensively and objectively.METHODS: A group of healthy professional basketball players were selected as the study subjects, and Doppler ultrasound instruments were utilized to obtain portal Doppler images before, during, and after exercise. The functional status of the vasculature was assessed by analyzing parameters such as portal blood flow velocity and resistance index. At the same time, the physiological parameters of the athletes, such as heart rate and blood pressure, were combined to gain a comprehensive understanding of the effects of basketball on portal blood flow.RESULTS: The results of the study showed that the anterior and posterior portal blood flow velocities of the athletes changed significantly during basketball exercise. Before the exercise, the blood flow velocity was relatively low, while it rapidly increased and reached the peak state during the exercise. After exercise, blood flow velocity gradually dropped back to the baseline level. In addition, the change in resistance index also indicated that portal blood vessels experienced a particular stress and adaptation process during exercise.CONCLUSION: This study revealed the effects of exercise on the cardiovascular system of athletes by analyzing the Doppler images of the portal vein before and after basketball exercise. Basketball exercise leads to significant changes in portal hemodynamics, which provides a new perspective for sports medicine. These findings are of guiding significance for the development of training programs for athletes and the prevention of exercise-related cardiovascular problems and provide a valuable reference for further research in the field of sports medicine.
{"title":"Basketball Anterior and Posterior Portal Veins Doppler Imaging of Sports Medicine Technique Exploration","authors":"Wei Zhu","doi":"10.4108/eetpht.10.5152","DOIUrl":"https://doi.org/10.4108/eetpht.10.5152","url":null,"abstract":"INTRODUCTION: Basketball, as a high-intensity sport, has attracted much attention for its effects on the cardiovascular system of athletes. The anterior and posterior portal veins are some of the vital blood vessels in the human circulatory system, and their blood flow is closely related to the athletes' physical status. Doppler ultrasound technology is widely used in sports medicine and provides a powerful tool for an in-depth understanding of the effects of basketball on portal vein blood flow. This study aimed to explore the potential of sports medicine technology in assessing cardiovascular adaptations in athletes through portal Doppler imaging before and after basketball exercise.OBJECTIVES: The primary objective of this study was to analyze the effects of basketball exercise on portal vein blood flow in athletes before and after basketball exercise through the use of Doppler ultrasound technology. Specifically, this study aimed to explore the dynamics of pre- and post-exercise Doppler imaging of the posterior and posterior veins in order to assess the cardiovascular adaptations of athletes during exercise more comprehensively and objectively.METHODS: A group of healthy professional basketball players were selected as the study subjects, and Doppler ultrasound instruments were utilized to obtain portal Doppler images before, during, and after exercise. The functional status of the vasculature was assessed by analyzing parameters such as portal blood flow velocity and resistance index. At the same time, the physiological parameters of the athletes, such as heart rate and blood pressure, were combined to gain a comprehensive understanding of the effects of basketball on portal blood flow.RESULTS: The results of the study showed that the anterior and posterior portal blood flow velocities of the athletes changed significantly during basketball exercise. Before the exercise, the blood flow velocity was relatively low, while it rapidly increased and reached the peak state during the exercise. After exercise, blood flow velocity gradually dropped back to the baseline level. In addition, the change in resistance index also indicated that portal blood vessels experienced a particular stress and adaptation process during exercise.CONCLUSION: This study revealed the effects of exercise on the cardiovascular system of athletes by analyzing the Doppler images of the portal vein before and after basketball exercise. Basketball exercise leads to significant changes in portal hemodynamics, which provides a new perspective for sports medicine. These findings are of guiding significance for the development of training programs for athletes and the prevention of exercise-related cardiovascular problems and provide a valuable reference for further research in the field of sports medicine.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"30 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Lung cancer, a fatal disease characterized by abnormal cell growth, ranks as the second most lethal worldwide, as observed in recent research conducted in India and other regions. Early detection is crucial for effective treatment, and manual differentiation of nodule types in CT images poses challenges for radiologists. OBJECTIVES: To enhance accuracy and efficiency, deep learning algorithms are proposed for early lung cancer detection. Transfer learning-based computer recognition algorithms have shown promise in providing radiologists with additional insights. METHODS: The dataset used in this study comprises 1000 CT scan images representing lung large cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, and normal lung cases. A preprocessing phase, including picture rescaling and modification, is applied to the input CT scan images of the lungs, followed by the utilization of a specific transfer learning model to develop a lung cancer detection system. RESULTS: The performance of various transfer learning strategies is evaluated using measures such as accuracy, precision, recall, specificity, area under the curve, and F1-score. CONCLUSION: Comparative analysis indicates that VGG16 outperforms other models in accurately categorizing different types of lung cancer.
{"title":"Application of Several Transfer Learning Approach for Early Classification of Lung Cancer","authors":"Janjhyam Venkata Naga Ramesh, Raghav Agarwal, Polireddy Deekshita, Shaik Aashik Elahi, Saladi Hima Surya Bindu, Juluru Sai Pavani","doi":"10.4108/eetpht.10.5434","DOIUrl":"https://doi.org/10.4108/eetpht.10.5434","url":null,"abstract":" \u0000INTRODUCTION: Lung cancer, a fatal disease characterized by abnormal cell growth, ranks as the second most lethal worldwide, as observed in recent research conducted in India and other regions. Early detection is crucial for effective treatment, and manual differentiation of nodule types in CT images poses challenges for radiologists. \u0000OBJECTIVES: To enhance accuracy and efficiency, deep learning algorithms are proposed for early lung cancer detection. Transfer learning-based computer recognition algorithms have shown promise in providing radiologists with additional insights. \u0000METHODS: The dataset used in this study comprises 1000 CT scan images representing lung large cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, and normal lung cases. A preprocessing phase, including picture rescaling and modification, is applied to the input CT scan images of the lungs, followed by the utilization of a specific transfer learning model to develop a lung cancer detection system. \u0000RESULTS: The performance of various transfer learning strategies is evaluated using measures such as accuracy, precision, recall, specificity, area under the curve, and F1-score. \u0000CONCLUSION: Comparative analysis indicates that VGG16 outperforms other models in accurately categorizing different types of lung cancer.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"17 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140240610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kishore Kanna R, Shashikant V Athawale, M. Naniwadekar, C. S. Choudhari, N. Talhar, S. Dhengre
INTRODUCTION: This study aims to investigate the correlation between the oscillations of electroencephalography (EEG) bands and the level of anxiety in a sample of sixteen youth athletes aged 17–21. The research utilizes a mobile EEG system to collect data on EEG band oscillations. OBJECTIVES: The aim of this research study is to investigate the brain wave oscillations during relaxation, specifically comparing the contrast between eyes open and eyes closed state Electroencephalography (EEG) using a state-of-the-art wireless EEG headset system. METHODS: The system incorporates dry, non-interacting EEG sensor electrodes, developed exclusively by NeuroSky. In addition, the addition of the ThinkGear module and MindCap XL skull facilitated EEG recording. The aim of the present study was to investigate the effect of eyes open and eyes closed conditions on alpha-band activity in the prefrontal cortex The results showed a statistically significant difference (p≤0.006); appeared between these two states. The present study examined the relationship between the alpha band of the prefrontal cortex and anxiety levels. Specifically, we examined the relationship between these variables in the eyes-closed condition. RESULTS: Our analysis revealed a statistically significant correlation, with the alpha band showing a negative slope (p≤0.029). The present study examines the comparison of data obtained from single-channel wireless devices with data obtained from conventional laboratories The findings of this study show a striking similarity between the results obtained with both types of devices. The aim of the present study was to investigate the specific characteristics of the correlation between electroencephalographic (EEG) alphaband oscillations in the prefrontal cortex in relation to eye position and anxiety levels in young athletes. CONCLUSION: This study seeks to shed light on the possible relationship between this vibration and individuals' internal cognitive and affective states.
{"title":"Anxiety Controlling Application using EEG Neurofeedback System","authors":"Kishore Kanna R, Shashikant V Athawale, M. Naniwadekar, C. S. Choudhari, N. Talhar, S. Dhengre","doi":"10.4108/eetpht.10.5432","DOIUrl":"https://doi.org/10.4108/eetpht.10.5432","url":null,"abstract":"INTRODUCTION: This study aims to investigate the correlation between the oscillations of electroencephalography (EEG) bands and the level of anxiety in a sample of sixteen youth athletes aged 17–21. The research utilizes a mobile EEG system to collect data on EEG band oscillations. \u0000OBJECTIVES: The aim of this research study is to investigate the brain wave oscillations during relaxation, specifically comparing the contrast between eyes open and eyes closed state Electroencephalography (EEG) using a state-of-the-art wireless EEG headset system. \u0000METHODS: The system incorporates dry, non-interacting EEG sensor electrodes, developed exclusively by NeuroSky. In addition, the addition of the ThinkGear module and MindCap XL skull facilitated EEG recording. The aim of the present study was to investigate the effect of eyes open and eyes closed conditions on alpha-band activity in the prefrontal cortex The results showed a statistically significant difference (p≤0.006); appeared between these two states. The present study examined the relationship between the alpha band of the prefrontal cortex and anxiety levels. Specifically, we examined the relationship between these variables in the eyes-closed condition. \u0000RESULTS: Our analysis revealed a statistically significant correlation, with the alpha band showing a negative slope (p≤0.029). The present study examines the comparison of data obtained from single-channel wireless devices with data obtained from conventional laboratories The findings of this study show a striking similarity between the results obtained with both types of devices. The aim of the present study was to investigate the specific characteristics of the correlation between electroencephalographic (EEG) alphaband oscillations in the prefrontal cortex in relation to eye position and anxiety levels in young athletes. \u0000CONCLUSION: This study seeks to shed light on the possible relationship between this vibration and individuals' internal cognitive and affective states.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"12 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140240911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sirisha Potluri, Bikash Chandra Sahoo, S. Satapathy, Shruti Mishra, Janjhyam Venkata Naga Ramesh, S. Mohanty
INTRODUCTION: The cardiopulmonary arrest is a major issue in any country. Gone are the days when it used to happen to those who are aged but now it is a major concern emerging among adolescents as well. According to the World Health Organization (WHO), cardiac arrest and stroke is still a major concern and remains a public health crisis. In past years India has witnessed many cases of heart related issues which used to occur predominantly among people having high cholesterol. But now the scenario has changed, and cases have been observed in people having normal cholesterol levels. There are several factors involved in heart stroke such as age, sex, blood pressure, etc. which are used by doctors to monitor and diagnose the same. OBJECTIVES: This paper focuses on different predictive models and ways to improve the accuracy of prediction by analyzing datasets on how they affect the accuracy of certain algorithms. METHODS: The factors contributing to heart issues can be used as a beacon to predict the stroke and help an individual to further consult a doctor beforehand. The idea is to target the datasets and the prediction algorithms of deep learning including advanced ones to improvise it and attain a better result. RESULTS: This paper brings out the comparative analysis among neural network techniques like ANN, Transfer Learning, MAML and LRP in which ANN showed the best result by giving the highest accuracy of 94%. CONCLUSION: Furthermore, it discusses a new attribute called “gamma prime fibrinogen” which could be used in the future to boost prediction performance.
导言：心肺骤停在任何国家都是一个重大问题。过去，心肺骤停只发生在老年人身上，但现在，青少年中也出现了心肺骤停。据世界卫生组织（WHO）称，心脏骤停和中风仍然是一个重大问题，也仍然是一个公共卫生危机。过去几年，印度发生了许多心脏相关疾病的病例，这些疾病主要发生在高胆固醇人群中。但现在情况发生了变化，胆固醇水平正常的人中也出现了病例。心脏中风涉及多种因素，如年龄、性别、血压等，医生会根据这些因素进行监测和诊断。目的：本文通过分析数据集如何影响某些算法的准确性，重点探讨不同的预测模型和提高预测准确性的方法。方法：导致心脏问题的因素可作为预测中风的信标，帮助个人提前咨询医生。我们的想法是针对深度学习的数据集和预测算法（包括高级算法）进行改进，以获得更好的结果。结果：本文对 ANN、迁移学习、MAML 和 LRP 等神经网络技术进行了比较分析，其中 ANN 的准确率最高，达到 94%，显示出最佳效果。结论：此外，本文还讨论了一种名为 "γ原纤维蛋白原 "的新属性，未来可用于提高预测性能。
{"title":"A Deep Learning Framework for Prediction of Cardiopulmonary Arrest","authors":"Sirisha Potluri, Bikash Chandra Sahoo, S. Satapathy, Shruti Mishra, Janjhyam Venkata Naga Ramesh, S. Mohanty","doi":"10.4108/eetpht.10.5420","DOIUrl":"https://doi.org/10.4108/eetpht.10.5420","url":null,"abstract":"INTRODUCTION: The cardiopulmonary arrest is a major issue in any country. Gone are the days when it used to happen to those who are aged but now it is a major concern emerging among adolescents as well. According to the World Health Organization (WHO), cardiac arrest and stroke is still a major concern and remains a public health crisis. In past years India has witnessed many cases of heart related issues which used to occur predominantly among people having high cholesterol. But now the scenario has changed, and cases have been observed in people having normal cholesterol levels. There are several factors involved in heart stroke such as age, sex, blood pressure, etc. which are used by doctors to monitor and diagnose the same. \u0000OBJECTIVES: This paper focuses on different predictive models and ways to improve the accuracy of prediction by analyzing datasets on how they affect the accuracy of certain algorithms. \u0000METHODS: The factors contributing to heart issues can be used as a beacon to predict the stroke and help an individual to further consult a doctor beforehand. The idea is to target the datasets and the prediction algorithms of deep learning including advanced ones to improvise it and attain a better result. \u0000RESULTS: This paper brings out the comparative analysis among neural network techniques like ANN, Transfer Learning, MAML and LRP in which ANN showed the best result by giving the highest accuracy of 94%. \u0000CONCLUSION: Furthermore, it discusses a new attribute called “gamma prime fibrinogen” which could be used in the future to boost prediction performance.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"14 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anila M, G. K. Kumar, D. Rani, M. V. V. Prasad Kantipudi, D. Jayaram
INTRODUCTION: A neurological condition known as Parkinson's disease (PD); it affected millions of individuals worldwide. An early diagnosis can help enhance the quality of life for those who are affected with this disease. This paper presents a novel Deep neural network model based on Long Short-Term Memory (LSTM) design for the identification of PD using voice features. OBJECTIVES: This research work aims to Identify the presence of PD using voice features of individuals. To achieve this, a Deep neural Network with LSTM is to be designed. Objective of the work is to analyse the voice data and implement the model with good accuracy. METHODS: The proposed model is a Deep Neural Network with LSTM. RESULTS: The proposed method uses the features gleaned from voice signals for training phase of LSTM model which achieved an accuracy of 89.23%, precision value as 0.898, F1-score of 0.965, and recall value as 0.931and is observed as best when compared to existing models. CONCLUSION: Deep Neural Networks are more powerful than ANNs ahd when associated with LSTM , the model outperformed the job of identifying PD using voice data.
{"title":"An LSTM based DNN Model for Neurological Disease Prediction Using Voice Characteristics","authors":"Anila M, G. K. Kumar, D. Rani, M. V. V. Prasad Kantipudi, D. Jayaram","doi":"10.4108/eetpht.10.5424","DOIUrl":"https://doi.org/10.4108/eetpht.10.5424","url":null,"abstract":"INTRODUCTION: A neurological condition known as Parkinson's disease (PD); it affected millions of individuals worldwide. An early diagnosis can help enhance the quality of life for those who are affected with this disease. This paper presents a novel Deep neural network model based on Long Short-Term Memory (LSTM) design for the identification of PD using voice features. \u0000OBJECTIVES: This research work aims to Identify the presence of PD using voice features of individuals. To achieve this, a Deep neural Network with LSTM is to be designed. Objective of the work is to analyse the voice data and implement the model with good accuracy. \u0000METHODS: The proposed model is a Deep Neural Network with LSTM. \u0000RESULTS: The proposed method uses the features gleaned from voice signals for training phase of LSTM model which achieved an accuracy of 89.23%, precision value as 0.898, F1-score of 0.965, and recall value as 0.931and is observed as best when compared to existing models. \u0000CONCLUSION: Deep Neural Networks are more powerful than ANNs ahd when associated with LSTM , the model outperformed the job of identifying PD using voice data.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"31 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140245063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Cardiovascular disease (CVD) is the most common cause of death worldwide, and its prevalence is rising in low-resource settings and among those with lower incomes. OBJECTIVES: Machine learning (ML) algorithms are quickly evolving and being implemented in medical procedures for CVD diagnosis and treatment decisions. Every day, the healthcare business creates massive amounts of data. However, the majority of it is inadequately utilized. Efficient techniques for extracting knowledge from these datasets for clinical diagnosis or other uses are scarce. METHODS: ML is being applied in the healthcare industry all over the world. In the health dataset, ML approaches useful in the prevention of locomotor disorders and heart disease. RESULTS: The revelation of such vital information allows researchers to acquire significant insight into how to use the proper treatment and diagnosis for a specific patient. Researchers study enormous volumes of complex healthcare data using various ML approaches, which improves healthcare professionals in disease prediction. CONCLUSION: The goal of this study is to summarize some of the current research on predicting heart diseases utilizing machine learning and data mining techniques, analyze the various mining algorithm combinations employed, and determine which techniques are useful and efficient. Future directions in prediction systems have also been considered.
导言：心血管疾病（CVD）是全球最常见的死亡原因，其发病率在资源匮乏地区和低收入人群中呈上升趋势。目的机器学习（ML）算法正在迅速发展，并被应用于心血管疾病诊断和治疗决策的医疗程序中。每天，医疗保健行业都会产生大量数据。然而，大部分数据都没有得到充分利用。从这些数据集中提取知识用于临床诊断或其他用途的高效技术十分匮乏。方法：ML 正被应用于世界各地的医疗保健行业。在健康数据集中，ML 方法有助于预防运动障碍和心脏病。结果：这些重要信息的揭示使研究人员能够深入了解如何对特定患者进行正确的治疗和诊断。研究人员利用各种 ML 方法研究大量复杂的医疗保健数据，从而提高医疗保健专业人员的疾病预测能力。结论：本研究的目的是总结当前利用机器学习和数据挖掘技术预测心脏病的一些研究，分析所采用的各种挖掘算法组合，并确定哪些技术是有用和有效的。同时还考虑了预测系统的未来发展方向。
{"title":"A Review: Machine Learning and Data Mining Approaches for Cardiovascular Disease Diagnosis and Prediction","authors":"Gorapalli Srinivasa Rao, G. Muneeswari","doi":"10.4108/eetpht.10.5411","DOIUrl":"https://doi.org/10.4108/eetpht.10.5411","url":null,"abstract":"INTRODUCTION: Cardiovascular disease (CVD) is the most common cause of death worldwide, and its prevalence is rising in low-resource settings and among those with lower incomes. \u0000OBJECTIVES: Machine learning (ML) algorithms are quickly evolving and being implemented in medical procedures for CVD diagnosis and treatment decisions. Every day, the healthcare business creates massive amounts of data. However, the majority of it is inadequately utilized. Efficient techniques for extracting knowledge from these datasets for clinical diagnosis or other uses are scarce. \u0000METHODS: ML is being applied in the healthcare industry all over the world. In the health dataset, ML approaches useful in the prevention of locomotor disorders and heart disease. \u0000RESULTS: The revelation of such vital information allows researchers to acquire significant insight into how to use the proper treatment and diagnosis for a specific patient. Researchers study enormous volumes of complex healthcare data using various ML approaches, which improves healthcare professionals in disease prediction. \u0000CONCLUSION: The goal of this study is to summarize some of the current research on predicting heart diseases utilizing machine learning and data mining techniques, analyze the various mining algorithm combinations employed, and determine which techniques are useful and efficient. Future directions in prediction systems have also been considered.","PeriodicalId":36936,"journal":{"name":"EAI Endorsed Transactions on Pervasive Health and Technology","volume":"221 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140245435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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