Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Expert Systems with Applications: An International Journal
Heartbeat time series classification with support vector machines
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm
EMS '09 Proceedings of the 2009 Third UKSim European Symposium on Computer Modeling and Simulation
IEEE Transactions on Information Technology in Biomedicine
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Arrhytmia is one of the most crucial problem in cardiology. It can be diagnosed by a standard electrocardiogram (ECG). So far, many methode have been develop for arrhytmia detection, recognition and classification But many methode for arrhytmia beat classification have been yet able to solve unknown category data. This paper will discusses about how to determine the type of arrhythmia in computerize way through classification process that able to solve unkown categorical beat. We use FLVQ to solve the weakness of the other methode of calssification such as to classified unknown category beat. This process is divided into three steps: data preprocessing, feature extraction and classification. Data preprocessing related to how the initial data prepared, in this case, we will reduce the baseline noise with cubic spline, then we cut the signal beat by beat using pivot R peak, while for the feature extraction and selection, we using wavelet algorithm. ECG signal will be classified into four classes: PVC, LBBB, NOR, RBBB and one unknown category beat; using two following algorithms Back-Propagation and Fuzzy Neuro Learning Vector Quantization (FLVQ). The classification will be devided on two phase, at ones phase we will find best fitur for out system. At this phase we use known category beat, the best fiture for out study is 50 fitur, it is from wavelet decompotition level 3. Second phase is added unkown category beat on data test, the unknown category beat is not included on data train. Accuracy of FLVQ in our study is 95.5% for data without unknown category beat at testing step and 87.6% for data with unknown category beat.