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Early Detection of Neurological Disorders Using Machine Learning Systems最新文献

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Social Media Analytics to Predict Depression Level in the Users 社交媒体分析预测用户抑郁程度
Early Detection of Neurological Disorders Using Machine Learning Systems Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-8567-1.CH011
Mohd. Shahid Husain
{"title":"Social Media Analytics to Predict Depression Level in the Users","authors":"Mohd. Shahid Husain","doi":"10.4018/978-1-5225-8567-1.CH011","DOIUrl":"https://doi.org/10.4018/978-1-5225-8567-1.CH011","url":null,"abstract":"As people around the world are spending increasing amounts of time online, the question of how online experiences are linked to health and wellbeing is essential. Depression has become a public health concern around the world. Traditional methods for detecting depression rely on self-report techniques, which suffer from inefficient data collection and processing. Research shows that symptoms linked to mental illness are detectable on social media like Twitter, Facebook, and web forums, and automatic methods are more and more able to locate inactivity and other mental disease. The pattern of social media usage can be very helpful to predict the mental state of a user. This chapter also presents how activities on Facebook are associated with the depressive states of users. Based on online logs, we can predict the mental state of users.","PeriodicalId":374218,"journal":{"name":"Early Detection of Neurological Disorders Using Machine Learning Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126148763","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}
引用次数: 2
Brain Tumor and Its Segmentation From Brain MRI Sequences 脑肿瘤及其脑MRI序列分割
Early Detection of Neurological Disorders Using Machine Learning Systems Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-8567-1.CH004
Sanjay Saxena, Puspanjali Mohapatra, S. Pattnaik
{"title":"Brain Tumor and Its Segmentation From Brain MRI Sequences","authors":"Sanjay Saxena, Puspanjali Mohapatra, S. Pattnaik","doi":"10.4018/978-1-5225-8567-1.CH004","DOIUrl":"https://doi.org/10.4018/978-1-5225-8567-1.CH004","url":null,"abstract":"Automated segmentation of tumorous region from the brain magnetic resonance image (MRI) is the procedure of extrication anomalous tissues from regular tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The process of accurate and efficient segmentation is still exigent because of the diversity of location, size, and shape of the tumorous region. Brain MRI provides metabolic process, psychological process, and descriptive information of the brain. Brain tumor segmentation using MRI is drawing the attention of the researchers due to its non-invasive nature and good soft tissue contrast of MRI sequences. The main motive of this chapter is to provide a broad overview of the methods of brain tumor segmentation based on MRI. This chapter provides the information of the brain tumor, its types, brief introduction of the MRI, and its diverse types, and lastly, this chapter gives the brief overview with benefits and limitations about diverse techniques used for brain tumor segmentation by different researchers and scientists.","PeriodicalId":374218,"journal":{"name":"Early Detection of Neurological Disorders Using Machine Learning Systems","volume":"86 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127990443","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}
引用次数: 4
Epileptic Seizure Detection and Classification Using Machine Learning 使用机器学习的癫痫发作检测和分类
Early Detection of Neurological Disorders Using Machine Learning Systems Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-8567-1.CH009
R. Janghel, Y. Rathore, Gautam Tatiparti
{"title":"Epileptic Seizure Detection and Classification Using Machine Learning","authors":"R. Janghel, Y. Rathore, Gautam Tatiparti","doi":"10.4018/978-1-5225-8567-1.CH009","DOIUrl":"https://doi.org/10.4018/978-1-5225-8567-1.CH009","url":null,"abstract":"Epilepsy is a brain ailment identified by unpredictable interruptions of normal brain activity. Around 1% of mankind experience epileptic seizures. Around 10% of the United States population experiences at least a single seizure in their life. Epilepsy is distinguished by the tendency of the brain to generate unexpected bursts of unusual electrical activity that disrupts the normal functioning of the brain. As seizures usually occur rarely and are unforeseeable, seizure recognition systems are recommended for seizure detection during long-term electroencephalography (EEG). In this chapter, ANN models, namely, BPA, RNN, CL, PNN, and LVQ, have been implemented. A prominent dataset was employed to assess the proposed method. The proposed method is capable of achieving an accuracy of 97.5%; the high accuracy obtained has confirmed the great success of the method.","PeriodicalId":374218,"journal":{"name":"Early Detection of Neurological Disorders Using Machine Learning Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117309962","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}
引用次数: 3
Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications 脑电信号在脑机接口中的运动图像分类
Early Detection of Neurological Disorders Using Machine Learning Systems Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-8567-1.CH013
S. Mazumdar, Rohit Chaudhary, Suruchi Suruchi, S. Mohanty, D. Kumari, A. Swetapadma
{"title":"Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications","authors":"S. Mazumdar, Rohit Chaudhary, Suruchi Suruchi, S. Mohanty, D. Kumari, A. Swetapadma","doi":"10.4018/978-1-5225-8567-1.CH013","DOIUrl":"https://doi.org/10.4018/978-1-5225-8567-1.CH013","url":null,"abstract":"In this chapter, a nearest neighbor (k-NN)-based method for efficient classification of motor imagery using EEG for brain-computer interfacing (BCI) applications has been proposed. Electroencephalogram (EEG) signals are obtained from multiple channels from brain. These EEG signals are taken as input features and given to the k-NN-based classifier to classify motor imagery. More specifically, the chapter gives an outline of the Berlin brain-computer interface that can be operated with minimal subject change. All the design and simulation works are carried out with MATLAB software. k-NN-based classifier is trained with data from continuous signals of EEG channels. After the network is trained, it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy, which is found to be 99.25%. The result suggested that the proposed method is accurate for BCI applications.","PeriodicalId":374218,"journal":{"name":"Early Detection of Neurological Disorders Using Machine Learning Systems","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129783544","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}
引用次数: 1
Medical Image Segmentation 医学图像分割
Early Detection of Neurological Disorders Using Machine Learning Systems Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-8567-1.CH015
R. Kashyap, Surendra Rahamatkar
{"title":"Medical Image Segmentation","authors":"R. Kashyap, Surendra Rahamatkar","doi":"10.4018/978-1-5225-8567-1.CH015","DOIUrl":"https://doi.org/10.4018/978-1-5225-8567-1.CH015","url":null,"abstract":"Today, IoT in therapeutic administrations has ended up being more productive in light of the fact that the correspondence among authorities and patients has been improved with versatile applications. These applications are made by the associations with the objective that the pros can screen the patient's prosperity. If any issue has hopped out at the patient, by then the authority approaches the patient and gives the correct treatment. In this proposition, particular focus is given to infant human administrations, in light of the fact that the greatest fear of gatekeepers is that they would lose their infant kids at whatever point. Therefore, in this part, a business contraption has been recognized which screens the consistent information about the infant's heart rate, oxygen levels, resting position. In case anything happens to the tyke, the information will get to the adaptable application, which has been made by an association and is mechanically available by finishing a representation field test for the kid; the information is recorded and examined.","PeriodicalId":374218,"journal":{"name":"Early Detection of Neurological Disorders Using Machine Learning Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122418736","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}
引用次数: 4
Linguistic Markers in Individuals With Symptoms of Depression in Bi-Multilingual Context 双语多语背景下抑郁症状个体的语言标记
Early Detection of Neurological Disorders Using Machine Learning Systems Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-8567-1.CH012
Anbu Savekar, Shashikanta Tarai, Moksha Singh
{"title":"Linguistic Markers in Individuals With Symptoms of Depression in Bi-Multilingual Context","authors":"Anbu Savekar, Shashikanta Tarai, Moksha Singh","doi":"10.4018/978-1-5225-8567-1.CH012","DOIUrl":"https://doi.org/10.4018/978-1-5225-8567-1.CH012","url":null,"abstract":"Depression has been identified as the most prevalent mental disorder worldwide. Due to the stigma of mental illness, the population remains unidentified, undiagnosed, and untreated. Various studies have been carried out to detect and track depression following symptoms of dichotomous thinking, absolutist thinking, linguistic markers, and linguistic behavior. However, there is little study focused on the linguistic behavior of bilingual and multilingual with anxiety and depression. This chapter aims to identify the bi-multilingual linguistic markers by analyzing the recorded verbal content of depressive discourse resulting from life situations and stressors causing anxiety, depression, and suicidal ideation. Different contextual domains of word usage, content words, function words (pronouns), and negative valance words have been identified as indicators of psychological process affecting cognitive behavior, emotional health, and mental illness. These findings are discussed within the framework of Beck's model of depression to support the linguistic connection to mental illness-depression.","PeriodicalId":374218,"journal":{"name":"Early Detection of Neurological Disorders Using Machine Learning Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123453993","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}
引用次数: 4
Neurocognitive Mechanisms for Detecting Early Phase of Depressive Disorder 检测抑郁症早期阶段的神经认知机制
Early Detection of Neurological Disorders Using Machine Learning Systems Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-8567-1.CH010
Shashikanta Tarai
{"title":"Neurocognitive Mechanisms for Detecting Early Phase of Depressive Disorder","authors":"Shashikanta Tarai","doi":"10.4018/978-1-5225-8567-1.CH010","DOIUrl":"https://doi.org/10.4018/978-1-5225-8567-1.CH010","url":null,"abstract":"This chapter discusses neurocognitive mechanisms in terms of latency and amplitudes of EEG signals in depression that are presented in the form of event-related potentials (ERPs). Reviewing the available literature on depression, this chapter classifies early P100, ERN, N100, N170, P200, N200, and late P300 ERP components in frontal, mid-frontal, temporal, and parietal lobes. Using auditory oddball paradigm, most of the studies testing depressive patients have found robust P300 amplitude reduction. Proposing EEG methods and summarizing behavioral, neuroanatomical, and electrophysiological findings, this chapter discusses how the different tasks, paradigms, and stimuli contribute to the cohesiveness of neural signatures and psychobiological markers for identifying the patients with depression. Existing research gaps are directed to conduct ERP studies following go/no-go, flanker interference, and Stroop tasks on global and local attentional stimuli associated with happy and sad emotions to examine anterior cingulate cortex (ACC) dysfunction in depression.","PeriodicalId":374218,"journal":{"name":"Early Detection of Neurological Disorders Using Machine Learning Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128881682","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}
引用次数: 0
Neurofeedback Neurofeedback
Early Detection of Neurological Disorders Using Machine Learning Systems Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-8567-1.CH002
Meena Gupta, D. Bhatia
{"title":"Neurofeedback","authors":"Meena Gupta, D. Bhatia","doi":"10.4018/978-1-5225-8567-1.CH002","DOIUrl":"https://doi.org/10.4018/978-1-5225-8567-1.CH002","url":null,"abstract":"Neurofeedback (NF) is a type of brain wave training based on operant learning. NF has been employed in research and clinical settings for the investigation and treatment of a growing number of psychological illnesses. This technique involves detection of electroencephalographic (EEG) information from the surface of the scalp of a subject by separating its frequency decomposition into its component waveform (alpha, beta, theta, gamma, and delta) and making these components visible usually as polygraphic traces on a computer screen. Neurofeedback is being considered as a promising new method for restoring brain function in a large number of mental disorder cases. NF takes into account behavioral, cognitive, and subjective aspects as well as the brain activity of the concerned individual. About 25 years ago, NF was employed for clinical and research purposes in psychological illness. These psychological illnesses include attention deficit disorder, addiction to drug, depression, stress, and eating disorders.","PeriodicalId":374218,"journal":{"name":"Early Detection of Neurological Disorders Using Machine Learning Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116734292","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}
引用次数: 0
Early Detection of Parkinson's Disease 帕金森病的早期检测
Early Detection of Neurological Disorders Using Machine Learning Systems Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-8567-1.CH005
D. Devi, S. Biswas, B. Purkayastha
{"title":"Early Detection of Parkinson's Disease","authors":"D. Devi, S. Biswas, B. Purkayastha","doi":"10.4018/978-1-5225-8567-1.CH005","DOIUrl":"https://doi.org/10.4018/978-1-5225-8567-1.CH005","url":null,"abstract":"Parkinson's disease (PD) is a neurodegenerative disorder that occurs due to corrosion of the substantia nigra, located in the thalamic region of the human brain, and is responsible for transmission of neural signals throughout the human body by means of a brain chemical, termed as “dopamine.” Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Through this chapter, an intelligent diagnostic system is proposed by integrating one-class SVM, extreme learning machine, and data preprocessing technique. The proposed diagnostic model is validated with six existing techniques and four learning models. The experimental results prove the combination of proposed method with ELM learning model to be highly effective in case of early detection of Parkinson's disease, even in presence of underlying data issues.","PeriodicalId":374218,"journal":{"name":"Early Detection of Neurological Disorders Using Machine Learning Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127309190","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}
引用次数: 6
Mapping the Intellectual Structure of the Field Neurological Disorders 绘制神经疾病领域的智力结构
Early Detection of Neurological Disorders Using Machine Learning Systems Pub Date : 1900-01-01 DOI: 10.4018/978-1-5225-8567-1.CH001
S. Ravikumar
{"title":"Mapping the Intellectual Structure of the Field Neurological Disorders","authors":"S. Ravikumar","doi":"10.4018/978-1-5225-8567-1.CH001","DOIUrl":"https://doi.org/10.4018/978-1-5225-8567-1.CH001","url":null,"abstract":"The chapter utilizes bibliometric tools to explore papers in the field of neurological disorders and to examine the scientific development in the above subject domain. The research data were retrieved from the WOS database, which consists of 16,830 papers on the above phrase, but for the current study was limited to only those articles that have received more than four citations. Using this criterion, the data was narrowed to 10,694 as of 25/6/18. Using bibliometric tools, the author has identified the most productive authors, most productive countries, annual scientific production with an average growth rate of 4.82, and average article citations per year was 44.85. Network analysis was carried out to find co-citation network pattern, and with co-word analysis, found the conceptual structure of a field of neurological disorders.","PeriodicalId":374218,"journal":{"name":"Early Detection of Neurological Disorders Using Machine Learning Systems","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130562375","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}
引用次数: 0
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