ECG beat classification using a cost sensitive classifier
نویسندگان
چکیده
منابع مشابه
ECG beat classification using neuro-fuzzy network
In this paper we have studied the application on the fuzzy-hybrid neural network for electrocardiogram (ECG) beat classification. Instead of original ECG beat, we have used; autoregressive model coefficients, higher-order cumulant and wavelet transform variances as features. Tested with MIT/BIH arrhytmia database, we observe significant performance enhancement using proposed method. 2004 Elsevi...
متن کاملCost-Sensitive Classifier Evaluation Using Cost Curves
The evaluation of classifier performance in a cost-sensitive setting is straightforward if the operating conditions (misclassification costs and class distributions) are fixed and known. When this is not the case, evaluation requires a method of visualizing classifier performance across the full range of possible operating conditions. This talk outlines the most important requirements for cost-...
متن کاملA patient adaptable ECG beat classifier based on neural networks
A novel supervised neural network-based algorithm is designed to reliably distinguish in electrocardiographic (ECG) records between normal and ischemic beats of the same patient. The basic idea behind this paper is to consider an ECG digital recording of two consecutive R-wave segments (RRR interval) as a noisy sample of an underlying function to be approximated by a fixed number of Radial Basi...
متن کاملClassification cost: An empirical comparison among traditional classifier, Cost-Sensitive Classifier, and MetaCost
Loan fraud is a critical factor in the insolvency of financial institutions, so companies make an effort to reduce the loss from fraud by building a model for proactive fraud prediction. However, there are still two critical problems to be resolved for the fraud detection: (1) the lack of cost sensitivity between type I error and type II error in most prediction models, and (2) highly skewed di...
متن کاملA novel, batch modular learning approach for ECG beat classification
In this paper, we investigate a modular architecture for ECG beat classification. The feature space is divided into distinct regions and individual classifiers are developed for each region. We compare different combination strategies, and feature space partition strategies. We also describe a novel, batch modular learning method that can be used to incrementally improve the performance of the ...
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 Methods and Programs in Biomedicine
سال: 2013
ISSN: 0169-2607
DOI: 10.1016/j.cmpb.2013.05.011