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The electroencephalogram (EEG) is an important bioelectric signal for studying human brain characteristics as well as detection of abnormalities like epilepsy. However, the EEG recorded from frontal channels, often contain strong artifacts produced by eye movements. Existing regression-based methods for removing artifacts require various procedures for pre-processing and calibration that are inconvenient...
Wearable ambulatory ECG (A-ECG) signals obtained using wearable ECG recorders inherently contain the motion artifacts due to various physicals activities of the subject. Classification of four such physical activities (PAs) - left arm up-down, right arm up-down, waist twisting and walking- of five healthy subjects has been performed using neuro-fuzzy classifier (NFC). The Gabor energy feature vectors...
Ambulatory ECG signal (A-ECG) is useful when long term cardiac monitoring of a patient is necessary. Ambulatory ECG monitoring provides electrical activity of the heart while a person is involved in doing his or her normal routine activities. Thus, the recorded ECG signal consists of cardiac signal along with motion artifacts introduced due to person's body movements during routine activities. This...
Wearable ambulatory ECG (A-ECG) signals obtained using wearable ECG recorders inherently contain the motion artifacts due to various body movements of the subject. Classification of four such body movement activities (BMA) — left arm up-down, right arm up-down, waist twisting and walking-of five healthy subjects has been performed using artificial neural networks (ANN). The accelerometer data and...
Compressed Sensing (CS) is a novel approach of reconstructing a sparse signal much below the significant Nyquist rate of sampling. Due to the fact that ECG signals can be well approximated by the few linear combinations of wavelet basis, this work introduces a comparison of the reconstructed 10 ECG signals based on different wavelet families, by evaluating the performance measures as MSE (Mean Square...
Compressed Sensing (CS) is a novel approach of reconstructing a sparse signal much below the significant Nyquist rate of sampling. Due to the fact that ECG signals can be well approximated by the few linear combinations of wavelet basis, this work introduces a comparison of the reconstructed ECG signal based on different wavelet families, by evaluating the performance measures as MSE (Mean Square...
Medical image fusion has been used to derive useful information from multimodality medical image data. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, magnetic resonance imaging (MRI) provides better information on soft tissue whereas computed tomography (CT) provides better information about denser tissue. Fusing...
In this paper QRS complex detection algorithms based on the first and second derivatives have been studied and implemented. The threshold values for detecting R-peak candidate points mentioned in previous work have been modified for accuracy point of view. The derivative based QRS detection algorithms have been found not only computationally simple but exceptionally effective also on variety of ECG...
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