Conference Agenda

Wearable electrocardiography monitors, e.g. embedded in textile shirts, offer new approaches in diagnosis but suffers upon limited computational capacities. Hence, we propose and evaluate a lightweight algorithm for electrocardiography denoising via sparse dictionary learning, targeting two types of noise: baseline wander and muscle artifacts. For each type of noise a dictionary is built using K-singular value decomposition. This iterative method alternates between finding a sparse representation for every training signal and then updating every atom of the dictionary on its own. A sparse representation is found using the using the orthogonal matching pursuit algorithm. The atoms are updated exploiting the properties of the singular value decomposition. For further sparse approximation, we use the basis pursuit denoising algorithm.

Electrocardiography data stems from synthetically-generated signals as well as the freely-available Brno University of Technology ECG Quality Database. Noise is added to the signals using the MIT-BIH Noise Stress Database. Our results regarding baseline wander demonstrate that the algorithm outperforms the American Heart Association-recommended bandpass filter w.r.t. signal-to-noise ratio. Moreover, a small number of training data is sufficient for satisfying results which indicates the suitability of the method for wearable hardware with low memory and power specifications.

Neuromuscular fatigue can be monitored using complexity domain analysis of surface electromyography (sEMG) signals. This study provides a technique to detect fatigue induced change in signal complexity using the entropic measurements of its recurrence pattern. For this purpose, fifteen participants have been recruited to perform isometric fatiguing exercise and signals are acquired from the biceps brachii muscle of the dominant hands. Thresholded recurrence plot is constructed from the non-fatigue and fatigue segments of the signal. Bidimensional dispersion entropy (DispEn2D) is calculated to quantify the textural change in the resulting recurrence plot. The results show that the present approach can distinguish between non-fatigue and fatigue states. Decrease in the DispEn2D values in the fatigue state shows the increase in periodic component of sEMG signal in fatiguing contraction.

Efficient personalized ablation strategies for treating atrial arrhythmias remain challenging. Discrepancies in identifying arrhythmogenic areas using characterization methods for the left atrium (LA), such as late gadolinium enhanced magnetic resonance imaging (LGE-MRI) and electroanatomical mapping, require a comparative analysis of local impedance (LI) and LGE-MRI data. This study aims to analyze correlations as basis for improvement of treatment strategies. 16 patients undergoing LA ablation with LGE-MRI acquisition and LI data recording were recruited. LGE-MRI data and LI measurements were normalized to patient- and modality-specific blood pool references. A global mean shape (MS) was generated based on all patient geometries and normalized local impedance (LIN) and image intensity ratio (IIR) data points were co-registered to this geometry for comparison.

Data analysis comprised intra-patient and inter-patient assessments, evaluating differences in LIN values among datasets categorized by their IIR. Due to substantial deviations in LIN values, even within the same patient and IIR-category, discerning the presence or absence of a correlation was challenging, and no statistically significant correlation could be identified.

Our findings underscore the necessity for standardized protocols in data acquisition, processing, and comparison to minimize unquantified confounding effects. While immediate substitution of LI for LGE-MRI seems improbable given the significant LIN variations, this preliminary study lays the groundwork for systematic data acquisition. By ensuring data quality, a meaningful comparison between LI and LGE-MRI data can be facilitated, potentially shaping future strategies for atrial arrhythmia treatment.

Emotion influences the daily activity of human life. The complex interaction between the central nervous system (CNS) and peripheral nervous system (PNS) contributes to emotional experiences. Various studies have investigated this interaction during sleep, meditation, deception, and cognition. However, research focusing exclusively on emotion-related interactions is limited. In this work an attempt has been made to assess the CNS and PNS interaction by analyzing Electroencephalogram (EEG) and Photoplethysmogram (PPG) signals during emotional arousal induced by audio-visual stimuli obtained from the DEAP database . EEG signals are divided into four frequency bands: theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (30-45 Hz). The envelope of EEG and PPG signals is then computed to determine cross-frequency coherence (CFC). The Wilcoxon Rank-sum test is employed to assess the statistical significance of CFC in low (LA) vs. high-arousal(HA) for various electrodes. Results indicate that CFC can discriminate the LA vs HA. Higher CFC is found in HA compared to LA for the beta and gamma bands, while the opposite trend is observed in the theta and alpha bands. The FP1, FC1, and T7 are found to be statistically significant (p<0.05) in differentiating LA with HA. Therefore, this study offers insights into CNS-PNS interaction during emotional arousal.

The quality of remote photoplethysmography (rPPG) signals presents a significant challenge in contactless optical blood pressure measurement. Feature and morphology-based approaches heavily rely on subtle changes in signal characteristics, but rPPG signals are highly susceptible to noise and interference. This study aims to evaluate rPPG signal quality by assessing correlation with a reference PPG and signal-to-noise ratio (SNR) across various color models and rPPG methods. Analyses are performed under different measurement conditions, accounting for common sources of signal artifacts.