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Electroencephalography (EEG) signals are frequently contaminated by ocular, muscle, and cardiac artefacts whose removal normally requires manual inspection or the use of reference channels (EOG, EMG, ECG). We present a novel, fully automatic method for the detection and removal of ECG artefacts that works without a reference ECG channel. Independent Component Analysis (ICA) is applied to the measured data and the independent components are examined for the presence of QRS waveforms using an adaptive threshold-based QRS detection algorithm. Detected peaks are subsequently classified by a rule-based classifier as ECG or non-ECG components. Components manifesting ECG activity are marked for removal, and then the artefact-free signal is reconstructed by removing these components before performing the inverse ICA. The performance of the proposed method is evaluated on a number of EEG datasets and compared to results reported in the literature. The average sensitivity of our ECG artefact removal method is above 99 %, which is better than known literature results.

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Significant state-sponsored efforts have recently successfully elaborated general purpose information systems for the global Hungarian health sector management. However, there is still a significant need for easy-to-use smartphone-based equipment and applications to help citizens directly in achieving healthy nutrition habits and/or monitor people living with elevated health risk. This paper outlines a new generation of intelligent mHealth systems developed in the past few years to improve nutritional habits and quantitatively monitoring risk factors of stroke and the development of the necessary substrate of sudden cardiac arrest. According to the expectations, these solutions may achieve a significant share in the growing world market of smart health instruments.

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