One of the most common neurodegenerative disorders of the present age is Parkinson’s Disease or Parkinsonism. To estimate its advancement in the patient, huge amounts of data are being collected and studied to draw out inferences. The types of data generally studied towards that end are vocal data, body movement data, eye movement data, handwriting and drawing patterns, etc. In this work, the use of a Deep Neural Network has been proposed which can predict the Unified Parkinson's Disease Rating Scale (UPDRS) both motor and total by studying vocal data from UCI Machine Learning Repository. Both 2 layered as well as 3 layered networks were studied and it was found that the performance of 3-layer Deep Neural Network having 10, 20, 10 neurons in different layers was found to be the best with an accuracy of 97% and 99.62% for motor UPDRS and total UPDRS respectively. The other three parameters MSE, MAE and RMSE also showed improvement in the 3 layered model as compared to the 2 layered model.
{"title":"Prediction of Parkinson's Disease Using Deep Learning in TensorFlow","authors":"Sameena Naaz, Arooj Hussain, Farheen Siddiqui","doi":"10.4018/ijbce.290389","DOIUrl":"https://doi.org/10.4018/ijbce.290389","url":null,"abstract":"One of the most common neurodegenerative disorders of the present age is Parkinson’s Disease or Parkinsonism. To estimate its advancement in the patient, huge amounts of data are being collected and studied to draw out inferences. The types of data generally studied towards that end are vocal data, body movement data, eye movement data, handwriting and drawing patterns, etc. In this work, the use of a Deep Neural Network has been proposed which can predict the Unified Parkinson's Disease Rating Scale (UPDRS) both motor and total by studying vocal data from UCI Machine Learning Repository. Both 2 layered as well as 3 layered networks were studied and it was found that the performance of 3-layer Deep Neural Network having 10, 20, 10 neurons in different layers was found to be the best with an accuracy of 97% and 99.62% for motor UPDRS and total UPDRS respectively. The other three parameters MSE, MAE and RMSE also showed improvement in the 3 layered model as compared to the 2 layered model.","PeriodicalId":73426,"journal":{"name":"International journal of biomedical engineering and clinical science","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76016527","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}
Hmayag Partamian, Fouad Khnaisser, Mohamad Mansour, Reem A. Mahmoud, H.M. Hajj, F. Karameh
During seizures, different types of communication between different parts of the brain are characterized by many state of the art connectivity measures. We propose to employ a set of undirected (spectral matrix, the inverse of the spectral matrix, coherence, partial coherence, and phase-locking value) and directed features (directed coherence, the partial directed coherence) to detect seizures using a deep neural network. Taking our data as a sequence of ten sub-windows, an optimal deep sequence learning architecture using attention, CNN, BiLstm, and fully connected neural networks is designed to output the detection label and the relevance of the features. The relevance is computed using the weights of the model in the activation values of the receptive fields at a particular layer. The best model resulted in 97.03% accuracy using balanced MIT-BIH data subset. Finally, an analysis of the relevance of the features is reported.
{"title":"A Deep Model for EEG Seizure Detection with Explainable AI using Connectivity Features","authors":"Hmayag Partamian, Fouad Khnaisser, Mohamad Mansour, Reem A. Mahmoud, H.M. Hajj, F. Karameh","doi":"10.5121/ijbes.2021.8401","DOIUrl":"https://doi.org/10.5121/ijbes.2021.8401","url":null,"abstract":"During seizures, different types of communication between different parts of the brain are characterized by many state of the art connectivity measures. We propose to employ a set of undirected (spectral matrix, the inverse of the spectral matrix, coherence, partial coherence, and phase-locking value) and directed features (directed coherence, the partial directed coherence) to detect seizures using a deep neural network. Taking our data as a sequence of ten sub-windows, an optimal deep sequence learning architecture using attention, CNN, BiLstm, and fully connected neural networks is designed to output the detection label and the relevance of the features. The relevance is computed using the weights of the model in the activation values of the receptive fields at a particular layer. The best model resulted in 97.03% accuracy using balanced MIT-BIH data subset. Finally, an analysis of the relevance of the features is reported.","PeriodicalId":73426,"journal":{"name":"International journal of biomedical engineering and clinical science","volume":"189 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75072709","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}
Pub Date : 2021-08-31DOI: 10.11648/J.IJBECS.20210703.13
Stephin V. Mathew, Santhosh Uttangi, D. Noble, M. Ravi, Stephy K. Mathew, J. Venkatesh
INTRODUCTION: Kidney disease is becoming a worldwide public health problem with an increase in incidence and prevalence, poor outcomes and high cost. Rational prescription is necessary in kidney disease patients. These patients are at higher risk of developing drug related problems since they need complex therapeutic regimens that include comorbid conditions like diabetes mellitus, hypertension, coronary artery disease and infection that require frequent monitoring and dosage adjustment. Inappropriate use of medications can increase adverse drug effects, which can be reflected by excessive length of hospital stays, excessive health care utilization and cost. OBJECTIVES: The objective of the study was to assess, evaluate and analyze the prescribing pattern of drugs in kidney disease and their dose adjustments in medicine and emergency department of tertiary care teaching hospital. METHODOLOGY: The study was conducted for a period of 6 months. Ethical clearance was obtained from Institutional Ethical Committee of S C S College of Pharmacy, Harapanahalli. Collected data was analyzed to identify the current prescribing trend and dosage regimen in the management of renal failure patients and to know whether the prescribing rationality was obtained in Medicine and Emergency unit in hospital by using KDIGO guidelines. RESULTS: A total of 140 patients were enrolled in the study according to the inclusion criteria in which 104 were males and 36 were females. 134 CKD cases and 6 AKI cases were found. In the study, 105 (75%) patients were hypertensive, 62 (44.28%) patients were anemic, 54 (38.57%) patients were diabetic and dyslipidemia was associated with 21 (15%) patients. 87 patients were on hemodialysis. On the basis of ATC classification of drugs, cardiovascular system (35.7%) class of drugs was the commonly prescribed followed by drugs for alimentary tract and metabolism (25.97%), anti-infective (10.11%) and blood and blood forming agents (7.97%). Out of 1028 studied drugs, only 105 (10.21%) required dose adjustment where 76 (72.38%) were adjusted and 29 (27.61%) were not adjusted. CONCLUSION: This study illustrates the need for proper dose adjustment and drug utilization pattern in patients with renal failure. Appropriate dosing of antibiotics as well as other drugs, including narrow therapeutic drugs play a vital role in preventing dose related adverse reactions and toxicities. This study will provide an outline for management strategies and will influence the decision making process in clinical practice.
肾脏疾病正成为一个全球性的公共卫生问题，其发病率和流行率不断上升，预后差，成本高。肾病患者合理处方是必要的。这些患者出现药物相关问题的风险更高，因为他们需要复杂的治疗方案，其中包括糖尿病、高血压、冠状动脉疾病和感染等合并症，需要经常监测和调整剂量。药物使用不当会增加药物不良反应，这可以通过住院时间过长、过度的医疗保健利用和费用来反映。目的:对三级护理教学医院内科及急诊科肾脏疾病用药模式及剂量调整情况进行评估、评价和分析。方法:本研究为期6个月。获得Harapanahalli S C S药学院机构伦理委员会的伦理许可。对收集到的资料进行分析，以了解目前在肾衰竭患者管理中使用KDIGO指南的处方趋势和给药方案，了解医院内科和急诊科的处方是否合理。结果:按照纳入标准共纳入140例患者，其中男性104例，女性36例。其中CKD 134例，AKI 6例。其中高血压105例(75%)，贫血62例(44.28%)，糖尿病54例(38.57%)，血脂异常21例(15%)。87例患者进行血液透析。在ATC药物分类中，常用的是心血管系统类药物(35.7%)，其次是消化道及代谢类药物(25.97%)、抗感染类药物(10.11%)和血液及造血剂(7.97%)。1028种药物中，需要调整剂量的只有105种(10.21%)，其中调整剂量的有76种(72.38%)，未调整剂量的有29种(27.61%)。结论:本研究说明了肾衰患者需要适当的剂量调整和药物使用模式。适当使用抗生素和其他药物，包括狭义治疗药物，在预防剂量相关的不良反应和毒性方面起着至关重要的作用。本研究将提供管理策略大纲，并将影响临床实践中的决策过程。
{"title":"Drug Utilization Evaluation Study and Dose Adjustment in Patients with Kidney Disease in Tertiary Care Hospital","authors":"Stephin V. Mathew, Santhosh Uttangi, D. Noble, M. Ravi, Stephy K. Mathew, J. Venkatesh","doi":"10.11648/J.IJBECS.20210703.13","DOIUrl":"https://doi.org/10.11648/J.IJBECS.20210703.13","url":null,"abstract":"INTRODUCTION: Kidney disease is becoming a worldwide public health problem with an increase in incidence and prevalence, poor outcomes and high cost. Rational prescription is necessary in kidney disease patients. These patients are at higher risk of developing drug related problems since they need complex therapeutic regimens that include comorbid conditions like diabetes mellitus, hypertension, coronary artery disease and infection that require frequent monitoring and dosage adjustment. Inappropriate use of medications can increase adverse drug effects, which can be reflected by excessive length of hospital stays, excessive health care utilization and cost. OBJECTIVES: The objective of the study was to assess, evaluate and analyze the prescribing pattern of drugs in kidney disease and their dose adjustments in medicine and emergency department of tertiary care teaching hospital. METHODOLOGY: The study was conducted for a period of 6 months. Ethical clearance was obtained from Institutional Ethical Committee of S C S College of Pharmacy, Harapanahalli. Collected data was analyzed to identify the current prescribing trend and dosage regimen in the management of renal failure patients and to know whether the prescribing rationality was obtained in Medicine and Emergency unit in hospital by using KDIGO guidelines. RESULTS: A total of 140 patients were enrolled in the study according to the inclusion criteria in which 104 were males and 36 were females. 134 CKD cases and 6 AKI cases were found. In the study, 105 (75%) patients were hypertensive, 62 (44.28%) patients were anemic, 54 (38.57%) patients were diabetic and dyslipidemia was associated with 21 (15%) patients. 87 patients were on hemodialysis. On the basis of ATC classification of drugs, cardiovascular system (35.7%) class of drugs was the commonly prescribed followed by drugs for alimentary tract and metabolism (25.97%), anti-infective (10.11%) and blood and blood forming agents (7.97%). Out of 1028 studied drugs, only 105 (10.21%) required dose adjustment where 76 (72.38%) were adjusted and 29 (27.61%) were not adjusted. CONCLUSION: This study illustrates the need for proper dose adjustment and drug utilization pattern in patients with renal failure. Appropriate dosing of antibiotics as well as other drugs, including narrow therapeutic drugs play a vital role in preventing dose related adverse reactions and toxicities. This study will provide an outline for management strategies and will influence the decision making process in clinical practice.","PeriodicalId":73426,"journal":{"name":"International journal of biomedical engineering and clinical science","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87494512","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}
Pub Date : 2021-08-23DOI: 10.11648/J.IJBECS.20210703.12
Solmaz Razi, Katayoon Ghasemi, M. Masoodi
Recent studies have shown the diagnostic value of fecal as well as serum calprotectin in predicting the severity and activity of inflammatory bowel disease. Given the strong familial and inherited predisposition to inflammatory bowel disease, it is assumed that changes in calprotectin levels are also influenced by familial predispositions. Therefore, the present study aimed to evaluate the level of fecal calprotectin in patients and their first-degree relatives in order to determine the relationship between changes in this marker and its possible familial orientation. The study participants were the first-degree relatives (n = 100) of the patients (n = 33) with the definitive diagnosis of ulcerative colitis who referred to Rasoul-e-Akram hospital in 2018 and 2019. The fecal value of calprotectin was assessed using the ELISA method in both patients and the relatives. Fecal calprotectin level in patients was estimated to be 232.09±44.16μg/g. Fecal calprotectin level in the parents was 86.06±12.66μg/g, in siblings was 58.02±7.24μg/g and in the patient's children was 47.40±4.77μg/g. Fecal calprotectin levels were not affected by baseline indices such as gender, age, or BMI (either in patients or their relatives) and therefore these baseline factors had no effect on fecal calprotectin levels. Although fecal calprotectin levels are significantly longer in patients with ulcerative colitis than in healthy controls, the higher level of this marker among first-degree relatives of patients than healthy individuals also indicates the inherited tendency of changes in this marker in terms of high risk of disease in first-degree relatives of patients. These changes in fecal calprotectin levels will be independent of gender, age, and BMI
{"title":"Evaluation of Calprotectin Levels in First-Degree Relatives of Patients with Ulcerative Colitis","authors":"Solmaz Razi, Katayoon Ghasemi, M. Masoodi","doi":"10.11648/J.IJBECS.20210703.12","DOIUrl":"https://doi.org/10.11648/J.IJBECS.20210703.12","url":null,"abstract":"Recent studies have shown the diagnostic value of fecal as well as serum calprotectin in predicting the severity and activity of inflammatory bowel disease. Given the strong familial and inherited predisposition to inflammatory bowel disease, it is assumed that changes in calprotectin levels are also influenced by familial predispositions. Therefore, the present study aimed to evaluate the level of fecal calprotectin in patients and their first-degree relatives in order to determine the relationship between changes in this marker and its possible familial orientation. The study participants were the first-degree relatives (n = 100) of the patients (n = 33) with the definitive diagnosis of ulcerative colitis who referred to Rasoul-e-Akram hospital in 2018 and 2019. The fecal value of calprotectin was assessed using the ELISA method in both patients and the relatives. Fecal calprotectin level in patients was estimated to be 232.09±44.16μg/g. Fecal calprotectin level in the parents was 86.06±12.66μg/g, in siblings was 58.02±7.24μg/g and in the patient's children was 47.40±4.77μg/g. Fecal calprotectin levels were not affected by baseline indices such as gender, age, or BMI (either in patients or their relatives) and therefore these baseline factors had no effect on fecal calprotectin levels. Although fecal calprotectin levels are significantly longer in patients with ulcerative colitis than in healthy controls, the higher level of this marker among first-degree relatives of patients than healthy individuals also indicates the inherited tendency of changes in this marker in terms of high risk of disease in first-degree relatives of patients. These changes in fecal calprotectin levels will be independent of gender, age, and BMI","PeriodicalId":73426,"journal":{"name":"International journal of biomedical engineering and clinical science","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74410528","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}
Pub Date : 2021-08-23DOI: 10.11648/J.IJBECS.20210703.11
D. Teshome, T. Tessema, S. Kumsa, Misgana Naramo
Epidemiological survey of gastrointestinal (GI) helminthes parasites in small ruminant in four districts (Arero, Moyale, Teltele and Yabello) of Borana lowland, Southern Oromia, was conducted during the period of October 2016 to June 2017 to estimate the prevalence, to identify the species of parasite involved and to access the risk factors of GI helminthes parasites in small ruminant. For this study a total of 939 faecal samples (655 sheep and 284 goats) were collected directly from the rectum and examined based parasitological procedures. In this study an overall prevalence of helminthosis was 597 (63.6%) in small ruminants whereas 423 (64.6%) in sheep and 174 (61.3%) in goats harbor one or more GI helminthes parasites. Strongyles were the most prevalent parasites observed. The prevalence is higher in Moyale (70.8%), followed by 66%, 60.5%, and 47.1% in Yabello, Arero and Teltele respectively. The occurrence of helminthosis in the four districts was found statistically significant (P 0.05). Breed and Sex was also not significantly (P>0.05) associated with the occurrence of small ruminant helminthosis. The study indicates that helminthes parasites are the major constraints that affect productivity of small ruminant. Awareness creation to the pastoralists in the study area about the effect of small ruminant helminthosis and designing appropriate control methods has a paramount importance to improve the productivity of small ruminant.
{"title":"Epidemiological Study of Small Ruminant Gastrointestinal Helminthosis in Borana Lowlands, Southern Oromia, Ethiopia","authors":"D. Teshome, T. Tessema, S. Kumsa, Misgana Naramo","doi":"10.11648/J.IJBECS.20210703.11","DOIUrl":"https://doi.org/10.11648/J.IJBECS.20210703.11","url":null,"abstract":"Epidemiological survey of gastrointestinal (GI) helminthes parasites in small ruminant in four districts (Arero, Moyale, Teltele and Yabello) of Borana lowland, Southern Oromia, was conducted during the period of October 2016 to June 2017 to estimate the prevalence, to identify the species of parasite involved and to access the risk factors of GI helminthes parasites in small ruminant. For this study a total of 939 faecal samples (655 sheep and 284 goats) were collected directly from the rectum and examined based parasitological procedures. In this study an overall prevalence of helminthosis was 597 (63.6%) in small ruminants whereas 423 (64.6%) in sheep and 174 (61.3%) in goats harbor one or more GI helminthes parasites. Strongyles were the most prevalent parasites observed. The prevalence is higher in Moyale (70.8%), followed by 66%, 60.5%, and 47.1% in Yabello, Arero and Teltele respectively. The occurrence of helminthosis in the four districts was found statistically significant (P 0.05). Breed and Sex was also not significantly (P>0.05) associated with the occurrence of small ruminant helminthosis. The study indicates that helminthes parasites are the major constraints that affect productivity of small ruminant. Awareness creation to the pastoralists in the study area about the effect of small ruminant helminthosis and designing appropriate control methods has a paramount importance to improve the productivity of small ruminant.","PeriodicalId":73426,"journal":{"name":"International journal of biomedical engineering and clinical science","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73298059","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}
Pub Date : 2021-07-01DOI: 10.4018/IJBCE.2021070103
R. Rastogi, S. Sagar, N. Tandon, Priyanshi Garg, Mukund Rastogi
The mantra becomes more powerful when that sound is chanted in front of purified fire and light; sound and heat energy mixtures are converted into high level of energy and spread around the atmosphere. Through this paper, the well experienced author team of various domains is continuously working in experimenting in joint collaboration with different GoI departments. They have observed the slow but continuous progress in different ails on many subjects through scientific study and approach. The main case studies where the patients got significant benefits through this alternate therapy have been systematically presented here. The power of yajna and mantra has attracted the intellectuals of this era. In the future, we may expect some automated intelligent healthcare expert system using this way of life. Yagya science and its treatment power of different diseases is surprising; the need is that current science should accept it logically with an open mind and heart and let the humanity take the complete benefit of it.
{"title":"Treatment Case Studies and Emissions Analysis of Wood in Yagya","authors":"R. Rastogi, S. Sagar, N. Tandon, Priyanshi Garg, Mukund Rastogi","doi":"10.4018/IJBCE.2021070103","DOIUrl":"https://doi.org/10.4018/IJBCE.2021070103","url":null,"abstract":"The mantra becomes more powerful when that sound is chanted in front of purified fire and light; sound and heat energy mixtures are converted into high level of energy and spread around the atmosphere. Through this paper, the well experienced author team of various domains is continuously working in experimenting in joint collaboration with different GoI departments. They have observed the slow but continuous progress in different ails on many subjects through scientific study and approach. The main case studies where the patients got significant benefits through this alternate therapy have been systematically presented here. The power of yajna and mantra has attracted the intellectuals of this era. In the future, we may expect some automated intelligent healthcare expert system using this way of life. Yagya science and its treatment power of different diseases is surprising; the need is that current science should accept it logically with an open mind and heart and let the humanity take the complete benefit of it.","PeriodicalId":73426,"journal":{"name":"International journal of biomedical engineering and clinical science","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82259731","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}
Pub Date : 2021-07-01DOI: 10.4018/IJBCE.2021070101
Geetha Vaithianathan, Rajkumar E.
Medical image processing is a complex exercise and involves a number of stages to identify the disease in the arena of medical imaging. Irritable bowel syndrome is an acute disorder that causes intense abdominal pain and leads to changes in the bowel system. It gives rise to various indications like bleeding, bloating, celiac disease, gastric cancer, ulcer, etc. The system proposed here seeks to segment and classify each symptom of the irritable bowel syndrome individually with the aid of supervoxel segmentation algorithm. Features are extracted depending on the color, shape, and texture of the object. The extracted features are fed into the multi-support vector machine to identify the specific region in the medical image. The experiment provides the result of a test set 100 images stored in the data set which improves accuracy that refines the final output.
{"title":"Automatic Detection of Irritable Bowel Syndrome for 3D Images Using Supervoxel and Graph Cut Algorithm","authors":"Geetha Vaithianathan, Rajkumar E.","doi":"10.4018/IJBCE.2021070101","DOIUrl":"https://doi.org/10.4018/IJBCE.2021070101","url":null,"abstract":"Medical image processing is a complex exercise and involves a number of stages to identify the disease in the arena of medical imaging. Irritable bowel syndrome is an acute disorder that causes intense abdominal pain and leads to changes in the bowel system. It gives rise to various indications like bleeding, bloating, celiac disease, gastric cancer, ulcer, etc. The system proposed here seeks to segment and classify each symptom of the irritable bowel syndrome individually with the aid of supervoxel segmentation algorithm. Features are extracted depending on the color, shape, and texture of the object. The extracted features are fed into the multi-support vector machine to identify the specific region in the medical image. The experiment provides the result of a test set 100 images stored in the data set which improves accuracy that refines the final output.","PeriodicalId":73426,"journal":{"name":"International journal of biomedical engineering and clinical science","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90075129","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}
Pub Date : 2021-07-01DOI: 10.4018/IJBCE.2021070104
K. Karmakar, Sohail Saif, S. Biswas, S. Neogy
Remote health monitoring framework using wireless body area network with ubiquitous support is gaining popularity. However, faulty sensor data may prove to be critical. Hence, faulty sensor detection is necessary in sensor-based health monitoring. In this paper, an artificial neural network (ANN)-based framework for learning about health condition of patients as well as fault detection in the sensors is proposed. This experiment is done based on human cardiac condition monitoring setup. Related physiological parameters have been collected using wearable sensors from different people. These data are then analyzed using ANN for health condition identification and faulty node detection. Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2 sensors such as ECG sensor, pulse oximeter sensor, and body temperature sensor have been used for data collection and ARDINO UNO R3 as microcontroller device. ANN method detects faulty sensor data with classification accuracy of 98%. Experimental results and analyses are given to prove the claim.
利用无所不在的无线体域网络进行远程健康监测的框架越来越受欢迎。然而，错误的传感器数据可能是至关重要的。因此，在基于传感器的健康监测中，故障传感器检测是必要的。本文提出了一种基于人工神经网络(ANN)的传感器健康状况学习和故障检测框架。本实验是基于人体心脏状况监测装置进行的。使用不同人的可穿戴传感器收集相关生理参数。然后使用人工神经网络对这些数据进行分析，用于健康状况识别和故障节点检测。Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2传感器如心电传感器、脉搏血氧仪传感器、体温传感器等用于数据采集，ARDINO UNO R3作为微控制器器件。人工神经网络方法检测故障传感器数据，分类准确率达98%。最后给出了实验结果和分析。
{"title":"A WBAN-Based Framework for Health Condition Monitoring and Faulty Sensor Node Detection Applying ANN","authors":"K. Karmakar, Sohail Saif, S. Biswas, S. Neogy","doi":"10.4018/IJBCE.2021070104","DOIUrl":"https://doi.org/10.4018/IJBCE.2021070104","url":null,"abstract":"Remote health monitoring framework using wireless body area network with ubiquitous support is gaining popularity. However, faulty sensor data may prove to be critical. Hence, faulty sensor detection is necessary in sensor-based health monitoring. In this paper, an artificial neural network (ANN)-based framework for learning about health condition of patients as well as fault detection in the sensors is proposed. This experiment is done based on human cardiac condition monitoring setup. Related physiological parameters have been collected using wearable sensors from different people. These data are then analyzed using ANN for health condition identification and faulty node detection. Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2 sensors such as ECG sensor, pulse oximeter sensor, and body temperature sensor have been used for data collection and ARDINO UNO R3 as microcontroller device. ANN method detects faulty sensor data with classification accuracy of 98%. Experimental results and analyses are given to prove the claim.","PeriodicalId":73426,"journal":{"name":"International journal of biomedical engineering and clinical science","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87823454","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}
Pub Date : 2021-07-01DOI: 10.4018/IJBCE.2021070102
Mohanavelu Kalathe, Sakshi Agarwal, Vinutha Sampaath, J. Daniel
Locomotion is an essential aspect of day-to-day human life. Advancement in wearable robotic technology enhances capabilities for maintaining the locomotion of people with disabilities. The exoskeleton, being one of them, meets the growing demands in the rehabilitation industry and enhanced locomotion requirements. Depending on the need and disability, various types of exoskeletons are designed. The design aspect of the exoskeleton includes various sensor systems, mechanical structure, mechanism, and control strategy used. Detection of gait events depends on the disability of the wearer and is very critical to decide the appropriate gait event that needs to be activated either by powering the actuators actively or passively. These interfaces should have a minimum possible response time to control the exoskeleton system to follow the wearer's gait. This review paper describes various sensing system incorporated in the control of various exoskeleton systems for the detection of gait events.
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Pub Date : 2021-06-04DOI: 10.11648/J.IJBECS.20210702.14
Mustefa Mohammedhussein, Mohammedamin Hajure, J. Ebrahim, Aman Dule
Background: During infectious disease pandemic, patients with chronic medical diseases were at increased risk of mental health problems. Therefore, the study assessed posttraumatic stress symptoms amid the COVID-19 pandemic among patients with chronic medical diseases. Methods: A facility-based cross-sectional study was conducted from August 1- 20, 2020. Systematic random sampling was used to select 422 patients with chronic medical diseases (diabetes, hypertension, and HIV). Impact of the event scale revised was used to assess posttraumatic stress symptoms. Data were analyzed by using SPSS version 23. Multivariable logistic regression analysis with 95% CI and odds ratio were fitted to declare the significantly associated variables at P value < 0.05. Results: 230 (54.5%) of the participants were reported to have posttraumatic stress symptoms. Being female, AOR=3.65 (95% CI 2.08, 6.40), Duration of illness greater than five-year AOR=3.12 (95% CI 1.73, 5.65), presence of anxiety AOR=6.52 (95% CI 3.71, 11.47), Age ≥55 year AOR=3.45 (95% CI 1.49, 7.98), diagnosis of diabetes AOR=7.49 (95% CI 3.65, 15.35), hypertension AOR=4.45 (95% CI 2.29, 8.64) and poor social support AOR=2.16 (95% CI 1.26, 3.68) were observed to have significant association with posttraumatic stress symptoms. Conclusion: Significant posttraumatic stress symptoms were reported by more than half of the patients with chronic medical diseases. This was of considerable concern indicating a significant impact of COVID-19 pandemic on this group, which seeks attention for early psychological intervention.
背景:在传染病大流行期间，慢性内科疾病患者出现心理健康问题的风险增加。因此，本研究评估了慢性疾病患者在COVID-19大流行期间的创伤后应激症状。方法:于2020年8月1日至20日进行了一项基于设施的横断面研究。采用系统随机抽样的方法，选取慢性内科疾病(糖尿病、高血压、HIV)患者422例。采用修订后的事件影响量表评估创伤后应激症状。数据分析采用SPSS 23版。采用95% CI和比值比进行多变量logistic回归分析，P < 0.05为显著相关变量。结果:230人(54.5%)报告有创伤后应激症状。女性AOR=3.65 (95% CI 2.08, 6.40)，病程大于5年AOR=3.12 (95% CI 1.73, 5.65)，存在焦虑AOR=6.52 (95% CI 3.71, 11.47)，年龄≥55岁AOR=3.45 (95% CI 1.49, 7.98)，诊断为糖尿病AOR=7.49 (95% CI 3.65, 15.35)，高血压AOR=4.45 (95% CI 2.29, 8.64)，社会支持不良AOR=2.16 (95% CI 1.26, 3.68)与创伤后应激症状有显著相关性。结论:半数以上的慢性内科疾病患者存在明显的创伤后应激症状。这表明新冠肺炎大流行对这一群体产生了重大影响，这一群体需要关注早期心理干预。
{"title":"Posttraumatic Stress Symptoms Among Patients with Chronic Medical Disease Amid Covid-19 Pandemic in Southwest Ethiopia","authors":"Mustefa Mohammedhussein, Mohammedamin Hajure, J. Ebrahim, Aman Dule","doi":"10.11648/J.IJBECS.20210702.14","DOIUrl":"https://doi.org/10.11648/J.IJBECS.20210702.14","url":null,"abstract":"Background: During infectious disease pandemic, patients with chronic medical diseases were at increased risk of mental health problems. Therefore, the study assessed posttraumatic stress symptoms amid the COVID-19 pandemic among patients with chronic medical diseases. Methods: A facility-based cross-sectional study was conducted from August 1- 20, 2020. Systematic random sampling was used to select 422 patients with chronic medical diseases (diabetes, hypertension, and HIV). Impact of the event scale revised was used to assess posttraumatic stress symptoms. Data were analyzed by using SPSS version 23. Multivariable logistic regression analysis with 95% CI and odds ratio were fitted to declare the significantly associated variables at P value < 0.05. Results: 230 (54.5%) of the participants were reported to have posttraumatic stress symptoms. Being female, AOR=3.65 (95% CI 2.08, 6.40), Duration of illness greater than five-year AOR=3.12 (95% CI 1.73, 5.65), presence of anxiety AOR=6.52 (95% CI 3.71, 11.47), Age ≥55 year AOR=3.45 (95% CI 1.49, 7.98), diagnosis of diabetes AOR=7.49 (95% CI 3.65, 15.35), hypertension AOR=4.45 (95% CI 2.29, 8.64) and poor social support AOR=2.16 (95% CI 1.26, 3.68) were observed to have significant association with posttraumatic stress symptoms. Conclusion: Significant posttraumatic stress symptoms were reported by more than half of the patients with chronic medical diseases. This was of considerable concern indicating a significant impact of COVID-19 pandemic on this group, which seeks attention for early psychological intervention.","PeriodicalId":73426,"journal":{"name":"International journal of biomedical engineering and clinical science","volume":"86 16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84012004","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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