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Paroxysmal Atrial Fibrillation (PAF), a really life threatening disease, is the result of irregular and repeated depolarization of the atria. In this paper, patients with PAF disease and their different episodes can be detected by extracting statistical and morphological features from ECG signals and classifying them by applying artificial neural network (ANN), Bayes optimal classifier and K-nearest...
Automatic detection of life threatening abnormal beats in electrocardiogram (ECG) signal is of importance in many healthcare applications. The ECG beat signal variations in both shape and time impose great challenges to automatic detection tasks. To address those challenges and for high accuracy automatic detection, we present here a two stage abnormal beats detection algorithm. Normal and abnormal...
The aim of this study is to develop an algorithm to detect and classify six types of electrocardiogram (ECG) signal beats including normal beats (N), atrial premature beats (A), right bundle branch block beats (R), left bundle branch block beats (L), paced beats (P), and premature ventricular contraction beats (PVC or V) using a neural network classifier. In order to prepare an appropriate input vector...
In this paper, a new method of arrhythmia classification is proposed. At first we extract twenty two features from electrocardiogram signal. We propose a novel classification system based on genetic algorithm to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the best value of the parameters that tune its...
This research is on presenting a new approach for cardiac arrhythmia disease classification. The proposed method combines both support vector machine (SVM) and genetic algorithm approaches. First, twenty two features from electrocardiogram signal are extracted. These features are obtained semiautomatically from time-voltage of R, S, T, P, Q features of an Electro Cardiagram signals. Genetic algorithm...
Emotion recognition based on physiological signals is an important research fields with promising application future. This paper firstly carried out the work of affective (joy and sadness) electrocardiogram (ECG) signal acquisition obtained from 391 subjects through stimulation of film clips. The automatic location of P-QRS-T wave was performed by use of discrete wavelet transform (DWT), which was...
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