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In this paper, an efficient heart beat classification algorithm suitable for implementation on mobile devices is presented. A simplified ECG model is used for feature extraction in the time domain. The QRS complex is modeled using straight lines, while P and T waves are modeled using parabolas. The model parameters are estimated by minimizing the root mean square (RMS) of the model error. Heart beats...
Obstructive sleep apnea (OSA) is a common sleeping disorder resulting in the temporary blockage of the upper airways that can lead to increased hypertension and risk to other cardiovascular diseases. Most sleep apnea cases currently remain undiagnosed due to cost and resource limitations of overnight polysomnography at sleep laboratories. In this project, we demonstrate a Simulink-based, modular and...
This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature space using principal component analysis, and temporal features from the ECG data. For the pattern recognition unit,...
This paper, presents an intelligent diagnosis system for electrocardiogram (ECG) intensity images using artificial neural network (ANN). Features are extracted from many preprocess such as wavelet decomposition (WD), Edge detection (ED), gray level histogram (GLH), Fast Fourier transform (FFT), and Mean-variance (M-V). The ANN supervised feed-forward back propagation using adaptive learning rate with...
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