[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124752-en":3,"doc-seo-124752-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124752,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","A Review on the Applications of Machine Learning for Tinnitus Diagnosis Using EEG Signals","Tinnitus is a common hearing disorder influenced by factors such as age, hearing loss, loud-noise exposure, infections, tumors, certain medications, injuries, and psychological conditions. Although not all patients seek care, early diagnosis is essential for effective treatment, yet clinical identification often depends on expertise. Recent research increasingly applies electroencephalography (EEG) to capture tinnitus-related oscillatory brain activity, but findings across studies remain inconsistent. This review evaluates 11 papers from 2016–2023 using systematic literature review methods, summarizing approaches, comparing key aspects, and highlighting open challenges and future research directions in EEG-based machine-learning tinnitus recognition and prediction.","A Review on the Applications of Machine Learning for Tinnitus Diagnosis Using EEG Signals  \nFarzaneh Ramezani 1 , Hamidreza Bolhasani2+  \nAbstract-Tinnitus is a prevalent hearing disorder that can be caused by various factors such as age, hearing loss, exposure to loud noises, ear infections or tumors, certain medications, head or neck injuries, and psychological conditions like anxiety and depression. While not every patient requires medical attention, about 20% of sufferers seek clinical intervention. Early diagnosis is crucial for effective treatment. New developments have been made in tinnitus detection to aid in early detection of this illness. Over the past few years, there has been a notable growth in the usage of electroencephalography (EEG) to study variations in oscillatory brain activity related to tinnitus. However, the results obtained from numerous studies vary greatly, leading to conflicting conclusions. Currently, clinicians rely solely on their expertise to identify individuals with tinnitus. Researchers in this field have incorporated various data modalities and machine-learning techniques to aid clinicians in identifying tinnitus characteristics and classifying people with tinnitus. The purpose of writing this article is to review articles that focus on using machine learning (ML) to identify or predict tinnitus patients using EEG signals as input data. We have evaluated 11 articles published between 2016 and 2023 using a systematic literature review (SLR) method. This article arranges perfect summaries of all the research reviewed and compares the significant aspects of each. Additionally, we performed statistical analyses to gain a deeper comprehension of the most recent research in this area. Almost all of the reviewed articles followed a five-step procedure to achieve the goal of tinnitus. Disclosure. Finally, we discuss the open affairs and challenges in this method of tinnitus recognition or prediction and suggest future directions for research.  \nKeywords: Tinnitus; Machine Learning; Deep Learning, Electroencephalography; EEG; Systematic Literature Review  \n1 Department of Psychology, University of Tabriz, Tabriz, Iran  \n2 Department of Computer Engineering, Islamic Azad University Science and Research Branch, Tehran, Iran.  \n+ [Corresponding Author: hamidreza.bolhasani@srbiau.ac.ir](Corresponding Author: hamidreza.bolhasani@srbiau.ac.ir)  \n1. Introduction  \nTinnitus is a sort of phantom perception defective by neural activities associated with the clutter of the auditory system [1] and characterized by hearing undesirable sounds that are not present evidently [2] . Many people encounter a stable noise in their ears, further reported as a whistling or ringing sound in the ears [3] . Tinnitus is one of the three most prevalent clinical issues in otology. It is a common disease, affecting about 10-15% of the world's society and up to 33% of the elderly [1] are pretentious by tinnitus [4], and 10–20% of them indicate that tinnitus interrupts their daily life [5] . Tinnitus can cause insomnia, damaged cognitive capability, and difficulties in mental concentration, Serious people even appear anxious or depressed, disturbing the patient’s routine life [4] . Despite its extensive prevalence, the pathogenesis of tinnitus is uncertain, in most of these occasions, tinnitus is a personal perception that can only be comprehended by the influenced person [6], and clinical assessment and inspection are mostly based on the patient’s medical history, signs, auditory system trial, evaluation scale, psychoacoustic analysis, and absence of more objective recognition and assessment methods [7] . Spacious trials and research investigating the source of tinnitus have direct to the extensively embraced belief that tinnitus may be operated by momentarily anxious and annoying conditions, but turned into an indefinite sign by an unfamiliar mechanism in principle auditory pathways [8], [9]. To restore the loudness of tinnitus, ","cbCaiauScOzJ6laZ","https://ap.wps.com/l/cbCaiauScOzJ6laZ","pdf",948217,1,27,"English","en",105,"# Introduction\n## Tinnitus background and clinical challenges\n## EEG and functional imaging for tinnitus research\n## Machine learning approaches for tinnitus diagnosis","[{\"question\":\"Why is early diagnosis of tinnitus important?\",\"answer\":\"Early diagnosis supports more effective treatment, while clinicians often rely on expertise and patient-based assessments rather than objective measures.\"},{\"question\":\"What role does EEG play in tinnitus research?\",\"answer\":\"EEG records electrical activity from brain structures, enabling study of frequency-band activity and neural abnormalities linked to tinnitus.\"},{\"question\":\"How does this review analyze machine-learning methods for EEG-based tinnitus diagnosis?\",\"answer\":\"It uses a systematic literature review to evaluate 11 articles published between 2016 and 2023, summarizes the reviewed research, compares major aspects, and performs statistical analyses to interpret recent trends.\"}]","A Review on the Applications of Machine Learning for Tinnitus Diagnosis Using EEG Signals | 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is early diagnosis of tinnitus important?","Question",{"text":75,"@type":76},"Early diagnosis supports more effective treatment, while clinicians often rely on expertise and patient-based assessments rather than objective measures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does EEG play in tinnitus research?",{"text":80,"@type":76},"EEG records electrical activity from brain structures, enabling study of frequency-band activity and neural abnormalities linked to tinnitus.",{"name":82,"@type":73,"acceptedAnswer":83},"How does this review analyze machine-learning methods for EEG-based tinnitus diagnosis?",{"text":84,"@type":76},"It uses a systematic literature review to evaluate 11 articles published between 2016 and 2023, summarizes the reviewed research, compares major aspects, and performs statistical analyses to interpret recent 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