0% Complete
صفحه اصلی
/
سی و سومین کنفرانس بین المللی مهندسی برق
A New Protocol to Improve Effect of repetitive Transcranial Magnetic Stimulation in Treatment of Alzheimer's Disease
نویسندگان :
Ali Abedi
1
Gholamreza Moradi
2
Reza Sarraf Shirazi
3
Mehran Jahed
4
1- دانشگاه صنعتی امیرکبیر
2- دانشگاه صنعتی امیرکبیر
3- دانشگاه صنعتی امیرکبیر
4- دانشگاه صنعتی امیرکبیر
کلمات کلیدی :
repetitive Transcranial Magnetic Stimulation،Machine Learning،Alzheimer Disease،Support Vector Machine،Electroencephalography
چکیده :
Despite significant breakthroughs in the clinical and instrumental evaluation of Alzheimer's Disease (AD) diagnosis, as well as therapeutic efficacy achieved to date, we still face challenges for early public classification. Recent studies show that the use of electroencephalographic (EEG) network analysis allows dynamic brain connectivity to be frozen, and that this is successful in increasing classification accuracy when EEG signals are used together with neuropsychological tests. In conclusion, this study sought to evaluate the therapeutic potential of rTMS using an innovative protocol on cognitive performances in AD treatment. Using an SVM-based classifier on EEG data, we obtained excellent sensitivity = (97%±3%), specificity = (97%±2%), and accuracy = (98%±2%), with AUC= (0.98±0.05) for the classification of healthy controls and AD patients. We have established, to our knowledge, a unique modulation of pulse train, interpulse intervals, and pulse width in rTMS protocol, which will potentiate its therapeutic response. Further, we adopted the Common Mode Features (CMF) approach to delineate common biomarkers between Alzheimer's disease and Parkinson's disease as well as between Huntington's Disease with Amyotrophic Lateral Sclerosis. This method can improve SVM classifier performance by securing diagnostic as well as pan-condition biomarkers and therefore could enhance classification power in a clinical setting. This study was conducted with a total sample of 59 subjects (34 healthy, and 25 AD), proving that rTMS combined with EEG and machine learning can serve as an inexpensive and non-invasive individualized approach to diagnosis improvement or treatment augmentation in cases of Alzheimer's disease.
لیست مقالات
لیست مقالات بایگانی شده
Design of an Optical Current Transformer for High-Voltage Gas-Insulated Switchgear-Part I: Focus on Optical Sensor Design
Reza Babaei - Asghar Akbari - Arash Moradi
Dynamic State Estimation of Power System Using Gauss-Seidel Cubature Kalman Filter
Atiyeh Keshavarz-Mohammadiyan
مرتب سازی اسپایک های عصبی با استخراج ویژگی مبتنی بر شبکه عمیق خود رمزگذار
شیدا معجونی - حسین حسینی نژاد محبتی - امین نیک انجام
پنل بازیابی: نرم افزار بازیابی سیستمهای قدرت با قیود امنیتی
سجاد نجفی روادانق - رسول اسماعیل زاده - رضا فرتاش
An Analysis of Nash Equilibrium Learning through Myopic Decision-making in Incomplete Information Double Sided Auction Games within Markets
Hesam Farzaneh - Parsa Zholideh
Fatigue Detection in SSVEP-Based BCIs Using Biomarkers: A Comparative Study
Maedeh Azadi Moghadam - Ali Maleki
A New Unsupervised Feature Learning Method for Object Recognition using Prior-Knowledge Data
Ashkan Farrokhi - Hadi Seyedarabi
Exploring the Impact of Machine Translation on Fake News Detection: A Case Study on Persian Tweets about COVID-19
Masood Hamed Saghayan - Seyedeh Fatemeh Ebrahimi - Mohammad Bahrani
Virtual power plant participation in day-ahead and futures markets with a deep learning approach
Farzin Ghasemi Olanlari - Mohammad Fazel Dehghanniri - Turaj Amraee
بررسی و شبیه سازی اضافه ولتاژهای صاعقه در نیروگاه خورشیدی برق خراسان و ارائه سیستم حفاظتی مناسب
هادی علی آبادی - بهزاد کرمانی
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 41.7.4