کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
404575 677438 2016 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study
ترجمه فارسی عنوان
تشخیص خودکار اختلال سیگنال با استفاده از ویژگی های زمانی و فرکانسی و یادگیری ماشین: یک مورد مطالعه تشنج EEG نوزاد تازه متولد شده
کلمات کلیدی
تشنج EEG نوزاد؛ تجزیه و تحلیل زمان و فرکانس؛ استخراج ویژگی؛ انتخاب ویژگی؛TFD با وضوح بالا ؛ تصمیم گیری بالینی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Time-frequency (TF) based machine learning methodologies can improve the design of classification systems for non-stationary signals. Using selected TF distributions (TFDs), TF feature extraction is performed on multi-channel recordings using channel fusion and feature fusion approaches. Following the findings of previous studies, a TF feature set is defined to include three complementary categories: signal related features, statistical features and image features. Multi-class strategies are then used to improve the classification algorithm robustness to artifacts. The optimal subset of TF features is selected using the wrapper method with sequential forward feature selection (SFFS). In addition, a new proposed measure for TF feature selection is shown to improve the sensitivity of the classifier (while slightly reducing total accuracy and specificity). As an illustration, the TF approach is applied to the design of a system for detection of seizure activity in real newborn EEG signals. Experimental results indicate that: (1) The compact kernel distribution (CKD) outperforms other TFDs in classification accuracy; (2) a feature fusion strategy gives better classification than a channel fusion strategy; e.g. using all TF features, the CKD achieves a classification accuracy of 82% with feature fusion, which is 4% more than the channel fusion approach; (3) the SFFS wrapper feature selection method improves the classification performance for all TFDs; e.g. total accuracy is improved by 4.6%; (4) the multi-class strategy improves the seizure detection accuracy in the presence of artifacts; e.g. a total accuracy of 86.61% with one vs. one multi-class approach is achieved i.e. 0.91% more than the binary classification approach. The results obtained on a large practical real data set confirm the improved performance capability of TF features for knowledge based systems.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 106, 15 August 2016, Pages 38–50
نویسندگان
, ,