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Wearable devices for real-time ECG monitoring and analysis require an accurate but simple QRS detection algorithm which does not consume too much computational load. Dual-threshold QRS detection techniques are one of the promising solutions to overcome this problem. This study investigates the performance and robustness of a dual-threshold QRS detection method under different levels of noise and physical...
A new method for ECG artifacts detection from noncardiovascular physiological signals namely Electroencephalogram (EEG), Electrooculogram (EOG) and Electromyogram (EMG), without the need of any additional synchronous ECG channel, is being proposed. This ECG artifacts (R peaks) detection method uses Slope Sum Function and Teager Kaiser Energy operator with an adaptive threshold. The performance of...
Starting point for the heart rate variability analysis is the ECG signal, which ensures the most precise way of detecting heartbeats. However, very often devices used to record ECG also record at the same time many other physiological signals containing useful information about heart rate. In the case of the poor ECG quality or its absence information about beats is lost. This raises the need for...
We present a novel approach to single-channel ECG-EMG signal separation by means of enhanced non-negative matrix factorization (NMF). The approach is based on a linear decomposition of the input signal spectrogram in two non-negative components, which represent the ECG and EMG spectrogram estimates. As ECG and EMG have different time-frequency (TF) patterns, the decomposition is enhanced by reshaping...
Electroencephalogram (EEG) is the neurophysiologic measurement of the electrical action of the brain, acquired by recording from electrodes located on the scalp. EEG is a vital clinical tool for diagnosing, monitoring and managing neurological disorders. EEG signal is contaminated with various artifacts such as Electroocculogram (EOG), Electrocardiogram (ECG) and Electromyogram (EMG). In this paper,...
The paper developed a block-wise approach for ICA algorithms which can improve the computational efficiency of ICA without the degradation of performance for the separation of biomedical signals. Source signals including electrocardiogram (ECG), electromyogram (EMG) and 60-Hz sinusoid are linearly mixed for experimental tests. The mean-square errors (MSE) between the original sources and the separated...
Blind Source Separation (BSS) techniques are frequently needed in the processing of biomedical signals. This need comes from the fact that these signals are often composed of many different sources, which are mixed in the measured signal. However, we are usually only interested in examining one or a limited set of sources of interest separately. A variety of algorithms exist for separating multichannel...
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