Objective: Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation of the brain activity. This paper proposes an efficient algorithm, VME-DWT, to remove eye blinks in a short segment of the single EEG channel. Method : The proposed algorithm: (a) locates eye blink intervals using Variational Mode Extraction (VME) and (b) filters only contaminated EEG interval using an automatic Discrete Wavelet Transform (DWT) algorithm. The performance of VME-DWT is compared with an automatic Variational Mode Decomposition (AVMD) and a DWT-based algorithms, proposed for suppressing eye blinks in a short segment of the single EEG channel. Results: The VME-DWT detects and filters 95% of the eye blinks from the contaminated EEG signals with SNR ranging from −8 to +3 dB. The VME-DWT shows superiority to the AVMD and DWT with the higher mean value of correlation coefficient (0.92 vs. 0.83, 0.58) and lower mean value of RRMSE (0.42 vs. 0.59, 0.87). Significance: The VME-DWT can be a suitable algorithm for removal of eye blinks in low-cost single-channel EEG systems as it is: (a) computationally-efficient, the contaminated EEG signal is filtered in millisecond time resolution, (b) automatic, no human intervention is required, (c) low-invasive, EEG intervals without contamination remained unaltered, and (d) low-complexity, without need to the artifact reference.

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Hemodialysis (HD) patients have a higher risk of sudden death due to cardiac arrhythmias, which commonly occur during the long interdialytic interval (LII) as a result of electrolyte fluctuations (EFs). Noninvasive monitoring of EFs would enable restoring normal serum electrolyte levels (SELs) by performing early HD before the onset of arrhythmias. In this study, we propose an ECGderived descriptor, θ µ , that is noise robust and capable of capturing EFs during HD and the LII. To investigate the variation of θ µ , ECG and blood samples of 3 patients were acquired continuously, starting at Friday's HD and ending at Monday's HD. Results show that the increase of θ µ during Friday's HD is correlated with the decrease of SELs. Moreover, θ µ tends to decrease during the LII (no blood samples were obtained) and further increases during Monday's HD. If results in larger databases are confirmed, θ µ might be suitable for noninvasive monitoring of EFs during the LII.

Hemodialysis patients are susceptible to life-threatening arrhythmias whose incidence is markedly higher during the long interdialytic interval due to electrolyte fluctuations. Noninvasive monitoring of electrolyte fluctuations, particularly those of potassium, would enable restoring electrolyte balance before the onset of arrhythmias. This study investigates the feasibility of continuous long-term monitoring of potassium fluctuations using a single-lead electrocardiogram. We evaluate patient-specific T-wave morphology changes in the electrocardiogram using two descriptors: (i) a model-based descriptor, , developed to account for overall morphology changes, and (ii) the currently available descriptor, , sensitive to potassium levels in single-lead electrocardiograms. Electrocardiograms of 15 hemodialysis patients with pre-existent cardiac diseases were acquired continuously over the long interdialytic interval along with blood samples at predetermined time instants. Results reveal that and respond concordantly with potassium levels, and reacts to potassium lowering medication. The overlapping index of the daily distributions of and are moderately correlated with changes in potassium levels (= − 0.56 and = − 0.57, respectively). exhibits circadian variation, peaking amidst morning and decreasing until evening. appears to be less affected by motion-induced noise, which is preferable for ambulatory monitoring. Although long-term monitoring of potassium fluctuations is feasible even in complicated hemodialysis patients, the presence of concomitant electrolyte (calcium and bicarbonate) imbalances should be accounted for since it can hamper a reliable estimation. Considering that intradialytic T-wave morphologies may differ from the ones manifested between hemodialysis sessions, future studies should also strive to collect blood samples outside of hemodialysis to improve electrolyte estimation methods.

The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector’s coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.

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