Classification of ECG Signals Using Extreme Learning Machine
نویسندگان
چکیده
منابع مشابه
Classification of ECG Signals Using Extreme Learning Machine
An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. This ECG can be classified as normal and abnormal signals. The classification of the ECG signals is presently performed with the support vector machine. The generalization performance of the SVM classifier is not sufficient for the correct classification of ECG signals. To over...
متن کاملRobust algorithm for arrhythmia classification in ECG using extreme learning machine
BACKGROUND Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such...
متن کاملMonotonic classification extreme learning machine
Monotonic classification problems mean that both feature values and class labels are ordered and monotonicity relationships exist between some features and the decision label. Extreme Learning Machine (ELM) is a singlehidden layer feedforward neural network with fast training rate and good generalization capability, but due to the existence of training error, ELM cannot be directly used to hand...
متن کاملA New Classification Method of Epileptic Eeg Signals Using Differential Evolution Optimally Pruned Extreme Learning Machine
An epileptic seizure is a transient event of symptoms due to abnormal neuronal action in the brain. Electroencephalography (EEG) is the neuro physiological measurement of electrical activity in the brain as recorded by electrodes placed in the cerebral cortex. An epilepsy EEG is based on three approaches. First, a scaling and wavelet function of the Multi Wavelet Transform (MWT) offers orthogon...
متن کاملClassification of Mental Tasks from Eeg Signals Using Extreme Learning Machine
In this paper, a recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) is used to classify five mental tasks from different subjects using electroencephalogram (EEG) signals available from a well-known database. Performance of ELM is compared in terms of training time and classification accuracy with a Backpropagation Neural Network (BPNN) classifier and al...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computer and Information Science
سال: 2011
ISSN: 1913-8997,1913-8989
DOI: 10.5539/cis.v4n1p42