The impact of skin preparation method on electrocardiogram quality in horses.

Objective: Several skin preparation techniques are used in electrocardiogram (ECG) monitoring of horses. The objective of this study was to determine which methods produce the greatest signal quality using textile electrodes and standard silver/silver chloride (Ag/AgCl) electrodes. Animals and samples: Electrocardiogram data were collected using textile and Ag/AgCl electrodes simultaneously for 4 skin preparation techniques in 6 horses. Procedure: The effects of skin preparation (cleansing with isopropyl alcohol, with or without shaving the hair) and the effects of the application of a conductive gel were assessed using metrics of signal quality. Results: Shaving and cleansing with alcohol had no effect on signal quality for either electrode type. The Ag/AgCl electrodes contain a solid gel, and the application of additional gel did not affect signal quality. Data quality was significantly improved when gel was applied to textile electrodes. Furthermore, there was no difference in signal quality between electrode types when gel was used. Conclusion and clinical relevance: This study suggests that skin preparation by cleansing and/or shaving does not have a significant effect on equine ECG signal quality. When gel is used, textile electrodes are a practical alternative for Ag/AgCl electrodes, as they produce ECG recordings of the same quality.

Impact de la méthode de préparation de la peau sur la qualité de l'électrocardiogramme chez le cheval. Objectif: Plusieurs techniques de préparation de la peau sont utilisées lors de la surveillance électrocardiographique (ECG) des chevaux. L'objectif de cette étude était de déterminer quelles méthodes produisent la meilleure qualité de signal en utilisant des électrodes textiles et des électrodes standard argent/chlorure d'argent (Ag/AgCl). Animaux et échantillons: Les données d'électrocardiogramme ont été obtenues simultanément à l'aide d'électrodes textiles et d'électrodes Ag/AgCl pour 4 techniques de préparation cutanée chez 6 chevaux. Procédure: Les effets de la préparation de la peau (nettoyage à l'alcool isopropylique, avec ou sans rasage des cheveux) et les effets de l'application d'un gel conducteur ont été évalués à l'aide de métriques de qualité du signal. Résultats: Le rasage et le nettoyage à l'alcool n'ont eu aucun effet sur la qualité du signal pour les deux types d'électrodes. Les électrodes Ag/AgCl contiennent un gel solide et l'application de gel supplémentaire n'a pas affecté la qualité du signal. La qualité des données a été considérablement améliorée lorsque le gel a été appliqué sur des électrodes textiles. De plus, il n'y avait aucune différence dans la qualité du signal entre les types d'électrodes lorsque du gel était utilisé. Conclusion et pertinence clinique: Cette étude suggère que la préparation de la peau par nettoyage et/ou rasage n'a pas d'effet significatif sur la qualité du signal ECG équin. Lorsque du gel est utilisé, les électrodes textiles constituent une alternative pratique aux électrodes Ag/AgCl, car elles produisent des enregistrements ECG de même qualité.(Traduit par Dr Serge Messier).

Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features.

Photoplethysmography (PPG) is used to measure blood volume changes in the microvascular bed of tissue. Information about these changes along time can be used for estimation of various physiological parameters, such as heart rate variability, arterial stiffness, and blood pressure, to name a few. As a result, PPG has become a popular biological modality and is widely used in wearable health devices. However, accurate measurement of various physiological parameters requires good-quality PPG signals. Therefore, various signal quality indexes (SQIs) for PPG signals have been proposed. These metrics have usually been based on statistical, frequency, and/or template analyses. The modulation spectrogram representation, however, captures the second-order periodicities of a signal and has been shown to provide useful quality cues for electrocardiograms and speech signals. In this work, we propose a new PPG quality metric based on properties of the modulation spectrum. The proposed metric is tested using data collected from subjects while they performed various activity tasks contaminating the PPG signals. Experiments on this multi-wavelength PPG dataset show the combination of proposed and benchmark measures significantly outperforming several benchmark SQIs with improvements of 21.3% BACC (balanced accuracy) for green, 21.6% BACC for red, and 19.0% BACC for infrared wavelengths, respectively, for PPG quality detection tasks. The proposed metrics also generalize for cross-wavelength PPG quality detection tasks.

Validation of an Equine Smart Textile System for Heart Rate Variability: A Preliminary Study.

Electrocardiograms (ECGs), and associated heart rate (HR) and heart rate variability (HRV) measurements, are essential in assessing equine cardiovascular health and fitness. Smart textiles have gained popularity, but limited validation work has been conducted. Therefore, the objective of this study was to compare HR and HRV data obtained using a smart textile system (Myant) to the gold-standard telemetric device (Televet). Simultaneous ECGs were obtained using both systems in seven horses at rest and during a submaximal exercise test. Bland-Altman tests were used to assess agreement between the two devices. Strong to perfect correlations without significant differences between the two devices were observed for all metrics assessed. During exercise, mean biases of 0.31 bpm (95% limits of agreement: -1.99 to 2.61) for HR, 1.43 ms (-11.48 to 14.33) for standard deviation of R-R intervals (SDRR), and 0.04 (-2.30 to 2.38) for the HRV triangular index (TI) were observed. A very strong correlation was found between the two devices for HR (r = 0.9993, p < 0.0001) and for HRV parameters (SDRR r = 0.8765, p < 0.0001; TI r = 0.8712, p < 0.0001). This study demonstrates that a smart textile system is reliable for assessment of HR and HRV of horses at rest and during submaximal exercise.

A Smart Textile Band Achieves High-Quality Electrocardiograms in Unrestrained Horses.

Electrocardiography (ECG) is an essential tool in assessing equine health and fitness. However, standard ECG devices are expensive and rely on the use of adhesive electrodes, which may become detached and are associated with reduced ECG quality over time. Smart textile electrodes composed of stainless-steel fibers have previously been shown to be a suitable alternative in horses at rest and during exercise. The objective of this study was to compare ECG quality using a smart textile girth band knit with silver and carbon yarns to standard adhesive silver/silver chloride (Ag/AgCl) electrodes. Simultaneous three-lead ECGs were recorded using a smart textile band and Ag/AgCl electrodes in 22 healthy, mixed-breed horses that were unrestrained in stalls. ECGs were compared using the following quality metrics: Kurtosis (k) value, Kurtosis signal quality index (kSQI), percentage of motion artifacts (%MA), peak signal amplitude, and heart rate (HR). Two-way ANOVA with Tukey's multiple comparison tests was conducted to compare each metric. No significant differences were found in any of the assessed metrics between the smart textile band and Ag/AgCl electrodes, with the exception of peak amplitude. Kurtosis and kSQI values were excellent for both methods (textile mean k = 21.8 ± 6.1, median kSQI = 0.98 [0.92−1.0]; Ag/AgCl k = 21.2 ± 7.6, kSQI = 0.99 [0.97−1.0]) with <0.5% (<1 min) of the recording being corrupted by MAs for both. This study demonstrates that smart textiles are a practical and reliable alternative to the standard electrodes typically used in ECG monitoring of horses.

Exploring textile-based electrode materials for electromyography smart garments.

INTRODUCTION: In recent years, electromyography (EMG) has been increasingly studied for wearable applications. Conventional gel electrodes for electrophysiological recordings have limited use in everyday applications such as prosthetic control or muscular therapy at home. This study investigates the efficacy and feasibility of dry-contact electrode materials employed in smart textiles for EMG recordings. METHODS: Dry-contact electrode materials were selected and implemented on textile substrates. Using these electrodes, EMG was recorded from the forearm of able-bodied subjects. 25% and 50% isometric maximum voluntary contractions were captured. A comparative investigation was performed against gel electrodes, assessing the effect of material properties on signal fidelity and strength compared. RESULTS: When isolating for electrode surface area and pressure, 31 of the 40 materials demonstrated strong positive correlations in their mean PSD with gel electrodes (r > 95, p < 0.001). The inclusion of ionic liquids in the material composition, and using raised or flat electrodes, did not demonstrate a significant effect in signal quality. CONCLUSIONS: For EMG dry-contact electrodes, comparing the performance against gel electrodes for the application with the selected material is important. Other factors recommended to be studied are electrodes' durability and long-term stability.

Modeling and Reproducing Textile Sensor Noise: Implications for Textile-Compatible Signal Processing Algorithms.

Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). Signal processing algorithms that can extract ECG signals out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The synthetic textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 trials (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs.

Evaluation of dry textile electrodes for long-term electrocardiographic monitoring.

BACKGROUND: Continuous long-term electrocardiography monitoring has been increasingly recognized for early diagnosis and management of different types of cardiovascular diseases. To find an alternative to Ag/AgCl gel electrodes that are improper for this application scenario, many efforts have been undertaken to develop novel flexible dry textile electrodes integrated into the everyday garments. With significant progresses made to address the potential issues (e.g., low signal-to-noise ratio, high skin-electrode impedance, motion artifact, and low durability), the lack of standard evaluation procedure hinders the further development of dry electrodes (mainly the design and optimization). RESULTS: A standard testing procedure and framework for skin-electrode impedance measurement is demonstrated for the development of novel dry textile electrodes. Different representative electrode materials have been screen-printed on textile substrates. To verify the performance of dry textile electrodes, impedance measurements are conducted on an agar skin model using a universal setup with consistent frequency and pressure. In addition, they are demonstrated for ECG signals acquisition, in comparison to those obtained using conventional gel electrodes. CONCLUSIONS: Dry textile electrodes demonstrated similar impedance when in raised or flat structures. The tested pressure variations had an insignificant impact on electrode impedance. Looking at the effect of impedance on ECG signals, a noticeable effect on ECG signal performance metrics was not observed. Therefore, it is suggested that impedance alone is possibly not the primary indicator of signal quality. As well, the developed methods can also serve as useful guidelines for future textile dry-electrode design and testing for practical ECG monitoring applications.

Toward New Modalities in VEP-Based BCI Applications Using Dynamical Stimuli: Introducing Quasi-Periodic and Chaotic VEP-Based BCI.

Visual evoked potentials (VEPs) to periodic stimuli are commonly used in brain computer interfaces for their favorable properties such as high target identification accuracy, less training time, and low surrounding target interference. Conventional periodic stimuli can lead to subjective visual fatigue due to continuous and high contrast stimulation. In this study, we compared quasi-periodic and chaotic complex stimuli to common periodic stimuli for use with VEP-based brain computer interfaces (BCIs). Canonical correlation analysis (CCA) and coherence methods were used to evaluate the performance of the three stimulus groups. Subjective fatigue caused by the presented stimuli was evaluated by the Visual Analogue Scale (VAS). Using CCA with the M2 template approach, target identification accuracy was highest for the chaotic stimuli (M = 86.8, SE = 1.8) compared to the quasi-periodic (M = 78.1, SE = 2.6, p = 0.008) and periodic (M = 64.3, SE = 1.9, p = 0.0001) stimulus groups. The evaluation of fatigue rates revealed that the chaotic stimuli caused less fatigue compared to the quasi-periodic (p = 0.001) and periodic (p = 0.0001) stimulus groups. In addition, the quasi-periodic stimuli led to lower fatigue rates compared to the periodic stimuli (p = 0.011). We conclude that the target identification results were better for the chaotic group compared to the other two stimulus groups with CCA. In addition, the chaotic stimuli led to a less subjective visual fatigue compared to the periodic and quasi-periodic stimuli and can be suitable for designing new comfortable VEP-based BCIs.

Optimization of Visual Stimulus Sequence in a Brain-Computer Interface Based on Code Modulated Visual Evoked Potentials.

Brain-computer interfaces based on code-modulated visual evoked potentials provide high information transfer rates, which make them promising alternative communication tools. Circular shifts of a binary sequence are used as the flickering pattern of several visual stimuli, where the minimum correlation between them is critical for recognizing the target by analyzing the EEG signal. Implemented sequences have been borrowed from communication theory without considering visual system physiology and related ergonomics. Here, an approach is proposed to design optimum stimulus sequences considering physiological factors, and their superior performance was demonstrated for a 6-target c-VEP BCI system. This was achieved by defining a time-factor index on the frequency response of the sequence, while the autocorrelation index ensured a low correlation between circular shifts. A modified version of the non-dominated sorting genetic algorithm II (NSGAII) multi-objective optimization technique was implemented to find, for the first time, 63-bit sequences with simultaneously optimized autocorrelation and time-factor indexes. The selected optimum sequences for general (TFO) and 6-target (6TO) BCI systems, were then compared with m-sequence by conducting experiments on 16 participants. Friedman tests showed a significant difference in perceived eye irritation between TFO and m-sequence (p = 0.024). Generalized estimating equations (GEE) statistical test showed significantly higher accuracy for 6TO compared to m-sequence (p = 0.006). Evaluation of EEG responses showed enhanced SNR for the new sequences compared to m-sequence, confirming the proposed approach for optimizing the stimulus sequence. Incorporating physiological factors to select sequence(s) used for c-VEP BCI systems improves their performance and applicability.

Modeling and Reconstructing Textile Sensor Noise: Implications for Wearable Technology.

Wearable sensors enable the simultaneous recording of several electrophysiological signals from the human body in a non-invasive and continuous manner. Textile sensors are garnering substantial interest in the wearable technology because they can be knitted directly into the daily-used objects like underwear, bra, dress, etc. However, accurate processing of signals recorded by textile sensors is extremely challenging due to the very low signal-to-noise ratio (SNR). Systematic classification of textile sensor noise (TSN) is necessary to: (i) identify different types of noise and their statistical characteristics, (ii) explore how each type of noise influences the electrophysiological signal, (iii) develop optimal textile-specific electronics that suppress TSN, and (iv) reproduce TSN and create large dataset of textile sensors to validate various machine learning and signal processing algorithms. In this paper, we develop a novel technique to classify textile sensor artifacts in ECG signals. By simultaneously recording signals from the waist (textile sensors) and chest (gel electrode), we extract TSN by removing the chest ECG signal from the recorded textile data. We classify TSN based on its morphological and statistical features in two main categories, namely, slow and fast. Linear prediction coding (LPC) is utilized to model each class of TSN by auto-regression coefficients and residues. The residual signal can be approximated by Gaussian distribution which enables reproducing slow and fast artifacts that not only preserve the similar morphological features but also fulfill the statistical properties of TSN. By reproducing TSN and adding them to clean ECG signals, we create a textile-like ECG signal which can be used to develop and validate different signal processing algorithms.