Classification of Electrocardiogram Signals with RS and Quantum Neural Networks
نویسندگان
چکیده
In this paper, rough sets (RS) and quantum neural network (QNN) are used to recognize electrocardiogram (ECG) signals. Firstly, wavelet transform (WT) is used as a feature extraction after normalization of these signals. Then the attribute reduction of RS has been applied as preprocessor so that we could delete redundant attributes and conflicting objects from decision making table but remain efficient information lossless. We realized classification modeling and forecasting test based on QNN after that. Finally, the RS-QNN gives us fast and realistic results compared with the BP and RBF. By this method, we could reduce the dimension of feature space and decrease the complexity in the process. Experiment result shows that the classification ability of the RS-QNN is superior to conventional approach.
منابع مشابه
Classification of ECG signals using Hermite functions and MLP neural networks
Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of ...
متن کاملFusion Framework for Emotional Electrocardiogram and Galvanic Skin Response Recognition: Applying Wavelet Transform
Introduction To extract and combine information from different modalities, fusion techniques are commonly applied to promote system performance. In this study, we aimed to examine the effectiveness of fusion techniques in emotion recognition. Materials and Methods Electrocardiogram (ECG) and galvanic skin responses (GSR) of 11 healthy female students (mean age: 22.73±1.68 years) were collected ...
متن کاملA New Method to Improve Automated Classification of Heart Sound Signals: Filter Bank Learning in Convolutional Neural Networks
Introduction: Recent studies have acknowledged the potential of convolutional neural networks (CNNs) in distinguishing healthy and morbid samples by using heart sound analyses. Unfortunately the performance of CNNs is highly dependent on the filtering procedure which is applied to signal in their convolutional layer. The present study aimed to address this problem by a...
متن کاملDetecting and Predicting Muscle Fatigue during Typing By SEMG Signal Processing and Artificial Neural Networks
Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigu...
متن کاملA Comparative Study of Gender and Age Classification in Speech Signals
Accurate gender classification is useful in speech and speaker recognition as well as speech emotion classification, because a better performance has been reported when separate acoustic models are employed for males and females. Gender classification is also apparent in face recognition, video summarization, human-robot interaction, etc. Although gender classification is rather mature in a...
متن کامل