دانلود Automated Detection of Myocardial Ischemia Using Statistical Signal Processing and Neural Networks با word
دانلود Automated Detection of Myocardial Ischemia Using Statistical Signal Processing and Neural Networks با word دارای 11 صفحه می باشد و دارای تنظیمات در microsoft word می باشد و آماده پرینت یا چاپ است
فایل ورد دانلود Automated Detection of Myocardial Ischemia Using Statistical Signal Processing and Neural Networks با word کاملا فرمت بندی و تنظیم شده در استاندارد دانشگاه و مراکز دولتی می باشد.
توجه : در صورت مشاهده بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل ورد می باشد و در فایل اصلی دانلود Automated Detection of Myocardial Ischemia Using Statistical Signal Processing and Neural Networks با word،به هیچ وجه بهم ریختگی وجود ندارد
بخشی از متن دانلود Automated Detection of Myocardial Ischemia Using Statistical Signal Processing and Neural Networks با word :
سال انتشار : 1394
نام کنفرانس یا همایش : کنفرانس بین المللی یافته های نوین پژوهشی درمهندسی برق و علوم کامپیوتر
تعداد صفحات : 11
چکیده مقاله:
Abnormal changes in the ST segment of an electrocardiogram (ECG) are very important diagnostic parameters for detecting myocardial ischemia. Myocardial Ischemia is one of the most common causes of death in the world and early diagnosis and treatment of it has a critical importance. In this study, we propose a novel method for the automatic detection of myocardial ischemic events from the electrocardiogram (ECG) signal. The ECG analysis algorithm developed in this study consists of the Discrete Wavelet Transform (DWT), statistical signal processing (PCA & ICA) and Artificial Neural Networks (ANN). The ST-T segment is obtained based on the detection of R peak location, based on the well-known Pan & Tompkins method. A neural network based system was designed for automatic detection of myocardial ischemia. The preprocessing stage (feature extraction) comprises Wavelet transform and statistical signal processing techniques (Principal Component Analysis and Independent Component Analysis) aiming at extracting discriminant information and reduce redundancy in the set of features. Through the proposed system, classification efficiencies of ~99% were achieved and the misclassification of signatures was almost eliminated
دانلود این فایل