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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...
Most of the cardiac disorders are diagnosed by analysis of electrocardiogram (ECG) of the subject. Noise sources in ECG can either be cardiac or extra cardiac, resulting in the distribution of artifacts throughout the original signal. Non-ideal conditions such as electromagnetic interference caused by power cables of the monitoring equipment and muscle or electrode movements corrupt the ECG. This...
This paper presents a novel method for ECG baseline drift removal while preserving the integrity of the ST segment. Baseline estimation is achieved by tracking 3 isoelectric points within the ECG waveform as fiducial markers used in an interpolation filter. These points are determined relative to the QRS complex, which is extracted using a known method (Pan-Tompkins algorithm). The proposed algorithm...
Since the QRS complex in electrocardiogram signals is one of the most important tasks to describe the operation of heart, high accuracy detection for this complex should be considered. In this study one of the newest methods of QRS complex detection combined with several artifact sources reduction methods has been performed. QRS detection algorithm includes baseline drift removal, Butterworth filtering,...
The performance of speech recognition systems is commonly degraded by either speech-related disabilities or by real-world factors such as the environmentpsilas noise level and reverberation. In this work, we propose a subvocal speech recognition system based on EMG signal for subvocal acquisition, Independent Component Analysis (ICA) for feature extraction and Neural Networks for classification. We...
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