A New Method to Detect Driver Fatigue Based on EMG and ECG Collected by Portable Non-Contact Sensors

Recently, detection and prediction on driver fatigue have become interest of research worldwide. In the present work, a new method is built to effectively evaluate driver fatigue based on electromyography (EMG) and electrocardiogram (ECG) collected by portable real-time and non-contact sensors. First, under the non-disturbance condition for driver’s attention, mixed physiological signals (EMG, ECG and artefacts) are collected by non-contact sensors located in a cushion on the driver’s seat. EMG and ECG are effectively separated by FastICA, and de-noised by empirical mode decomposition (EMD). Then, three physiological features, complexity of EMG, complexity of ECG, and sample entropy (SampEn) of ECG, are extracted and analysed. Principal components are obtained by principal components analysis (PCA) and are used as independent variables. Finally, a mathematical model of driver fatigue is built, and the accuracy of the model is up to 91%. Moreover, based on the questionnaire, the calculation results of model are consistent with real fatigue felt by the participants. Therefore, this model can effectively detect driver fatigue.</p

The stationary wavelet transform as an efficient reductor of powerline interference for atrial bipolar electrograms in cardiac electrophysiology

[EN] Objective :The most relevant source of signal contamination in the cardiac electrophysiology (EP) laboratory is the ubiquitous powerline interference (PLI). To reduce this perturbation, algorithms including common fixed-bandwidth and adaptive-notch filters have been proposed. Although such methods have proven to add artificial fractionation to intra-atrial electrograms (EGMs), they are still frequently used. However, such morphological alteration can conceal the accurate interpretation of EGMs, specially to evaluate the mechanisms supporting atrial fibrillation (AF), which is the most common cardiac arrhythmia. Given the clinical relevance of AF, a novel algorithm aimed at reducing PLI on highly contaminated bipolar EGMs and, simultaneously, preserving their morphology is proposed. Approach: The method is based on the wavelet shrinkage and has been validated through customized indices on a set of synthesized EGMs to accurately quantify the achieved level of PLI reduction and signal morphology alteration. Visual validation of the algorithm¿s performance has also been included for some real EGM excerpts. Main results: The method has outperformed common filtering-based and wavelet-based strategies in the analyzed scenario. Moreover, it possesses advantages such as insensitivity to amplitude and frequency variations in the PLI, and the capability of joint removal of several interferences. Significance: The use of this algorithm in routine cardiac EP studies may enable improved and truthful evaluation of AF mechanisms.Research supported by grants DPI2017-83952-C3 MINECO/AEI/FEDER, UE and SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha.Martinez-Iniesta, M.; Ródenas, J.; Rieta, JJ.; Alcaraz, R. (2019). 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Waveform Integrity in Atrial Fibrillation: The Forgotten Issue of Cardiac Electrophysiology

[EN] Atrial fibrillation (AF) is the most common arrhythmia in clinical practice with an increasing prevalence of about 15% in the elderly. Despite other alternatives, catheter ablation is currently considered as the first-line therapy for the treatment of AF. This strategy relies on cardiac electrophysiology systems, which use intracardiac electrograms (EGM) as the basis to determine the cardiac structures contributing to sustain the arrhythmia. However, the noise-free acquisition of these recordings is impossible and they are often contaminated by different perturbations. Although suppression of nuisance signals without affecting the original EGM pattern is essential for any other later analysis, not much attention has been paid to this issue, being frequently considered as trivial. The present work introduces the first thorough study on the significant fallout that regular filtering, aimed at reducing acquisition noise, provokes on EGM pattern morphology. This approach has been compared with more refined denoising strategies. Performance has been assessed both in time and frequency by well established parameters for EGM characterization. The study comprised synthesized and real EGMs with unipolar and bipolar recordings. Results reported that regular filtering altered substantially atrial waveform morphology and was unable to remove moderate amounts of noise, thus turning time and spectral characterization of the EGM notably inaccurate. Methods based on Wavelet transform provided the highest ability to preserve EGM morphology with improvements between 20 and beyond 40%, to minimize dominant atrial frequency estimation error with up to 25% reduction, as well as to reduce huge levels of noise with up to 10 dB better reduction. Consequently, these algorithms are recommended as a replacement of regular filtering to avoid significant alterations in the EGMs. This could lead to more accurate and truthful analyses of atrial activity dynamics aimed at understanding and locating the sources of AF.This work was supported by the projects TEC201452250-R from the Spanish Ministry of Economy and Competitiveness and PPII-2014-026-P from Junta de Comunidades de Castilla-La Mancha.Martinez-Iniesta, M.; Rodenas, J.; Alcaraz, R.; Rieta, JJ. (2017). Waveform Integrity in Atrial Fibrillation: The Forgotten Issue of Cardiac Electrophysiology. Annals of Biomedical Engineering. 45(8):1890-1907. https://doi.org/10.1007/s10439-017-1832-6S18901907458Addison, P. S. Wavelet transforms and the ECG: a review. Physiol. Meas. 26(5):R155–R199, 2005.Aksu, T., T. E. Guler, K. Yalin, and A. Oto. Unanswered questions in complex fractionated atrial electrogram ablation. Pacing Clin. Electrophysiol. 39(11):1269–1278, 2016.Alcaraz, R., F. 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