Detection of multiple innervation zones from multi-channel surface EMG recordings with low signal-to-noise ratio using graph-cut segmentation

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the Seventh Framework Programme of the European Union (FP7/2007-2013) under REA grant agreement no. 600388 (TECNIOspring programme), from the Agency for Business Competitiveness of the Government of Catalonia, ACCIÓ, and from Spanish Ministry of Economy and Competitiveness- Spain (project DPI2014-59049-R).Peer ReviewedPostprint (published version

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

A comprehensive review of the movement imaginary brain-computer interface methods: Challenges and future directions

Brain-computer interface (BCI) aims to translate human intention into a control output signal. In motor-imaginary (MI) BCI, the imagination of movement modifies the cortex brain activity. Such activities are then used in pattern recognition to identify certain movement classes. MI-BCI could be used to enhance the life quality of physically impaired subjects. Several challenges exist in MI-BCI, including selecting appropriate channels, usually linked with a suitable classifier choice. The entire procedure must be real time in practical applications. A variety of channel selection and classification methods were used for MI-BCI in the literature. Also, hybrid machine learning (ML) and deep learning (DL) methods were used in the literature. In this chapter, different channel selection, ML and DL methods, validation frameworks, and performance indices of EEG-based methods were investigated. Three hundred and twenty-two papers published between January 2000 and March 2021 were analyzed in this systematic review. Specific challenges and future directions were then provided.Peer ReviewedPostprint (author's final draft

Use of Sine Shaped High-Frequency Rhythmic Visual Stimuli Patterns for SSVEP Response Analysis and Fatigue Rate Evaluation in Normal Subjects

Background: Recent EEG-SSVEP signal based BCI studies have used high frequency square pulse visual stimuli to reduce subjective fatigue. However, the effect of total harmonic distortion (THD) has not been considered. Compared to CRT and LCD monitors, LED screen displays high-frequency wave with better refresh rate. In this study, we present high frequency sine wave simple and rhythmic patterns with low THD rate by LED to analyze SSVEP responses and evaluate subjective fatigue in normal subjects.Materials and Methods: We used patterns of 3-sequence high-frequency sine waves (25, 30, and 35 Hz) to design our visual stimuli. Nine stimuli patterns, 3 simple (repetition of each of above 3 frequencies e.g., P25-25-25) and 6 rhythmic (all of the frequencies in 6 different sequences e.g., P25-30-35) were chosen. A hardware setup with low THD rate (<0.1%) was designed to present these patterns on LED. Twenty two normal subjects (aged 23–30 (25 ± 2.1) yrs) were enrolled. Visual analog scale (VAS) was used for subjective fatigue evaluation after presentation of each stimulus pattern. PSD, CCA, and LASSO methods were employed to analyze SSVEP responses. The data including SSVEP features and fatigue rate for different visual stimuli patterns were statistically evaluated.Results: All 9 visual stimuli patterns elicited SSVEP responses. Overall, obtained accuracy rates were 88.35% for PSD and > 90% for CCA and LASSO (for TWs > 1 s). High frequency rhythmic patterns group with low THD rate showed higher accuracy rate (99.24%) than simple patterns group (98.48%). Repeated measure ANOVA showed significant difference between rhythmic pattern features (P < 0.0005). Overall, there was no significant difference between the VAS of rhythmic [3.85 ± 2.13] compared to the simple patterns group [3.96 ± 2.21], (P = 0.63). Rhythmic group had lower within group VAS variation (min = P25-30-35 [2.90 ± 2.45], max = P35-25-30 [4.81 ± 2.65]) as well as least individual pattern VAS (P25-30-35).Discussion and Conclusion: Overall, rhythmic and simple pattern groups had higher and similar accuracy rates. Rhythmic stimuli patterns showed insignificantly lower fatigue rate than simple patterns. We conclude that both rhythmic and simple visual high frequency sine wave stimuli require further research for human subject SSVEP-BCI studies

surface electromyographic data simulation

<p>The model proposed by Farina <i>et al</i> was used to generate surface EMG signals [<a href="#_ENREF_1">1</a>].  In this model, the volume conductor was described as an anisotropic multilayered cylinder and the source was a spatio-temporal function describing the generation, propagation, and extinction of the intracellular action potential at the end-plate, along the fiber, and at the tendons, respectively. The Inter-Electrode-Distance (IED) was set to 5 mm as recommended in [<a href="#_ENREF_2">2</a>] to locate IZs. The remainder of the model parameters used in our study were in principle the same as  those used by Mesin <i>et al</i>  [<a href="#_ENREF_3">3</a>].  Finally, the number of active MUs in each 60-ms simulated signal interval was between 1 and 5. Signals were zero-phase digitally band-pass filtered [<a href="#_ENREF_4">4</a>] using an overall eighth-order Butterworth filter with cut-off frequencies 20 and 500 Hz.</p> <p> </p> <p>            For each MU number category (1 to 5), sEMG signals with SNR values of -5, 0, 5, 10 and 15 dB were simulated to include very low to moderate quality sEMG signals. Twenty Single-Differential (SD) channels were simulated along the muscle fiber direction and sampling frequency was 4096 Hz. Thirty frames (or images) with up to 5 IZs were simulated for each SNR value. The temporal location of the IZs was created randomly in each frame. The signal SNR for each simulated 60-ms epoch was defined as the RMS of the raw sEMG divided by the standard deviation of the added Gaussian noise, expressed in dB [<a href="#_ENREF_5">5</a>]. Thus, a total of 750 1-D linear array sEMG signals were simulated, considering five SNR values and maximum five MUs . We also provided the gold standard data for the IZ channels and CV values.</p> <p> </p> <p> </p> <p><a>1.         Farina D, Mesin L, Martina S, Merletti R. A surface EMG generation model with multilayer cylindrical description of the volume conductor. IEEE transactions on bio-medical engineering. 2004;51(3):415-26. doi: 10.1109/tbme.2003.820998.</a></p> <p><a>2.         Afsharipour B, Ullah K, Merletti R. Spatial Aliasing and EMG Amplitude in Time and Space: Simulated Action Potential Maps. In: Roa Romero ML, editor. XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013: MEDICON 2013, 25-28 September 2013, Seville, Spain. Cham: Springer International Publishing; 2014. p. 293-6.</a></p> <p><a>3.         Mesin L, Gazzoni M, Merletti R. Automatic localisation of innervation zones: A simulation study of the external anal sphincter. Journal of Electromyography and Kinesiology. 2009;19(6):e413-e21. doi: 10.1016/j.jelekin.2009.02.002.</a></p> <p><a>4.         Gustafsson F. Determining the initial states in forward-backward filtering. IEEE Transactions on Signal Processing. 1996;44(4):988-92. doi: 10.1109/78.492552.</a></p> <p><a>5.         Kay SM. Fundamentals of statistical signal processing. Englewood Cliffs, N.J.: Prentice-Hall PTR; 1993.</a></p> <p> </p> <p> </p

Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction.

Code modulated Visual Evoked Potentials (c-VEP) based BCI studies usually employ m-sequences as a modulating codes for their broadband spectrum and correlation property. However, subjective fatigue of the presented codes has been a problem. In this study, we introduce chaotic codes containing broadband spectrum and similar correlation property. We examined whether the introduced chaotic codes could be decoded from EEG signals and also compared the subjective fatigue level with m-sequence codes in normal subjects. We generated chaotic code from one-dimensional logistic map and used it with conventional 31-bit m-sequence code. In a c-VEP based study in normal subjects (n = 44, 21 females) we presented these codes visually and recorded EEG signals from the corresponding codes for their four lagged versions. Canonical correlation analysis (CCA) and spatiotemporal beamforming (STB) methods were used for target identification and comparison of responses. Additionally, we compared the subjective self-declared fatigue using VAS caused by presented m-sequence and chaotic codes. The introduced chaotic code was decoded from EEG responses with CCA and STB methods. The maximum total accuracy values of 93.6 ± 11.9% and 94 ± 14.4% were achieved with STB method for chaotic and m-sequence codes for all subjects respectively. The achieved accuracies in all subjects were not significantly different in m-sequence and chaotic codes. There was significant reduction in subjective fatigue caused by chaotic codes compared to the m-sequence codes. Both m-sequence and chaotic codes were similar in their accuracies as evaluated by CCA and STB methods. The chaotic codes significantly reduced subjective fatigue compared to the m-sequence codes

The compensation of the breakpoints of the image frames in the pruning step.

<p>(a) the segmented image with discontinuous regions (some breakpoints are shown in rectangular area). (b) The compensated image with fewer breakpoints and intensified regions.</p

The absolute channel error (IED ratio) for the innervation zone detection (MEAN±SD).

<p>The absolute channel error (IED ratio) for the innervation zone detection (MEAN±SD).</p

An example of the propagating region identification procedure (stage IV of the proposed algorithm) and feature extraction.

<p>The slope parameters found by center/edge coordinates are shown by triangles and pentagons, respectively. Bold triangles show the closest distance of edges. The center is defined as the center of each propagating region. The edges are the upper and lower boundaries of such regions. The slope is calculated based on the angle between a virtual line representing the propagation region (bold line) and the horizontal line.</p