Novel characterization method of impedance cardiography signals using time-frequency distributions

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P¿=¿0.780) and the extended modified beta distribution (P¿=¿0.765) provided similar results, higher than the rest of analyzed kernels.Peer ReviewedPostprint (published version

Time-frequency features for impedance cardiography signals during anesthesia using different distribution kernels

Objective: This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia. Materials and Methods: In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied. Results: The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%. Conclusion: The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient’s anesthesia state in a statistically significant way.Peer ReviewedPostprint (author's final draft

Poincaré plot analysis of cerebral blood flow signals : feature extraction and classification methods for apnea detection

Objective: Rheoencephalography is a simple and inexpensive technique for cerebral blood flow assessment, however, it is not used in clinical practice since its correlation to clinical conditions has not yet been extensively proved. The present study investigates the ability of Poincaré Plot descriptors from rheoencephalography signals to detect apneas in volunteers. Methods:A group of 16 subjects participated in the study. Rheoencephalography data from baseline and apnea periods were recorded and Poincaré Plot descriptors were extracted from the reconstructed attractors with different time lags (t). Among the set of extracted features, those presenting significant differences between baseline and apnea recordings were used as inputs to four different classifiers to optimize the apnea detection. Results:Three features showed significant differences between apnea and baseline signals: the Poincaré Plot ratio (SDratio), its correlation (R) and the Complex Correlation Measure (CCM). Those differences were optimized for time lags smaller than those recommended in previous works for other biomedical signals, all of them being lower than the threshold established by the position of the inflection point in the CCM curves. The classifier showing the best performance was the classification tree, with 81% accuracy and an area under the curve of the receiver operating characteristic of 0.927. This performance was obtained using a single input parameter, either SDratio or R. Conclusions Poincaré Plot features extracted from the attractors of rheoencephalographic signals were able to track cerebral blood flow changes provoked by breath holding. Even though further validation with independent datasets is needed, those results suggest that nonlinear analysis of rheoencephalography might be a useful approach to assess the correlation of cerebral impedance with clinical changesPeer ReviewedPostprint (published version

Comparison of the qCON and qNOX indices for the assessment of unconsciousness level and noxious stimulation response during surgery

The objective of this work is to compare the performances of two electroencephalogram based indices for detecting loss of consciousness and loss of response to nociceptive stimulation. Specifically, their behaviour after drug induction and during recovery of consciousness was pointed out. Data was recorded from 140 patients scheduled for general anaesthesia with a combination of propofol and remifentanil. The qCON 2000 monitor (Quantium Medical, Barcelona, Spain) was used to calculate the qCON and qNOX. Loss of response to verbal command and loss of eye-lash reflex were assessed during the transition from awake to anesthetized, defining the state of loss of consciousness. Movement as a response to laryngeal mask (LMA) insertion was interpreted as the response to the nociceptive stimuli. The patients were classified as movers or non-movers. The values of qCON and qNOX were statistically compared. Their fall times and rise times defined at the start and at the end of the surgery were calculated and compared. The results showed that the qCON was able to predict loss of consciousness such as loss of verbal command and eyelash reflex better than qNOX, while the qNOX has a better predictive value for response to noxious stimulation such as LMA insertion. From the analysis of the fall and rise times, it was found that the qNOX fall time (median: 217 s) was significantly longer (p value <0.05) than the qCON fall time (median: 150 s). At the end of the surgery, the qNOX started to increase in median at 45 s before the first annotation related to response to stimuli or recovery of consciousness, while the qCON at 88 s after the first annotation related to response to stimuli or recovery of consciousness (p value <0.05). The indices qCON and qNOX showed different performances in the detection of loss of consciousness and loss of response to stimuli during induction and recovery of consciousness. Furthermore, the qCON showed faster decrease during induction. This behaviour is associated with the hypothesis that the loss of response to stimuli (analgesic effect) might be reached after the loss of consciousness (hypnotic effect). On the contrary, the qNOX showed a faster increase at the end of the surgery, associated with the hypothesis that a higher probability of response to stimuli might be reached before the recovery of consciousness.Postprint (author's final draft

Refined multiscale entropy using fuzzy metrics: validation and application to nociception assessmentt

The refined multiscale entropy (RMSE) approach is commonly applied to assess complexity as a function of the time scale. RMSE is normally based on the computation of sample entropy (SampEn) estimating complexity as conditional entropy. However, SampEn is dependent on the length and standard deviation of the data. Recently, fuzzy entropy (FuzEn) has been proposed, including several refinements, as an alternative to counteract these limitations. In this work, FuzEn, translated FuzEn (TFuzEn), translated-reflected FuzEn (TRFuzEn), inherent FuzEn (IFuzEn), and inherent translated FuzEn (ITFuzEn) were exploited as entropy-based measures in the computation of RMSE and their performance was compared to that of SampEn. FuzEn metrics were applied to synthetic time series of different lengths to evaluate the consistency of the different approaches. In addition, electroencephalograms of patients under sedation-analgesia procedure were analyzed based on the patient’s response after the application of painful stimulation, such as nail bed compression or endoscopy tube insertion. Significant differences in FuzEn metrics were observed over simulations and real data as a function of the data length and the pain responses. Findings indicated that FuzEn, when exploited in RMSE applications, showed similar behavior to SampEn in long series, but its consistency was better than that of SampEn in short series both over simulations and real data. Conversely, its variants should be utilized with more caution, especially whether processes exhibit an important deterministic component and/or in nociception prediction at long scalesPeer ReviewedPostprint (published version

Generación de bioseñales sintéticas mediante series de Fourier variantes en el tiempo

[Resumen] La selección de la técnica más adecuada para el análisis de una señal tiempo-frecuencia depende en gran medida de la propia naturaleza de la señal objeto de análisis. Para ello, resulta adecuado utilizar señales sintéticas con un contenido tiempo-frecuencia conocido. En este trabajo se ha propuesto la construcción de una base de datos de señales biomédicas sintéticas a partir de la clasificación en patrones de señales reales. El objetivo de esta base de datos ha sido disponer de señales sintéticas con características tiempo-frecuencia predeterminadas y modificables con un comportamiento lo más realista posible.Este trabajo ha sido financiado dentro del programa de doctorado industrial DI-2014 de la Generalitat de Catalunya (España)https://doi.org/10.17979/spudc.978849749808

Functional MRI and Diffusion Tensor Imaging of Brain Reorganization After Experimental Stroke

The potential of the adult brain to reorganize after ischemic injury is critical for functional recovery and provides a significant target for therapeutic strategies to promote brain repair. Despite the accumulating evidence of brain plasticity, the interaction and significance of morphological and physiological modifications in post-stroke brain tissue remain mostly unclear. Neuroimaging techniques such as functional MRI (fMRI) and diffusion tensor imaging (DTI) enable in vivo assessment of the spatial and temporal pattern of functional and structural changes inside and outside ischemic lesion areas. This can contribute to the elucidation of critical aspects in post-stroke brain remodeling. Task/stimulus-related fMRI, resting-state fMRI, or pharmacological MRI enables direct or indirect measurement of neuronal activation, functional connectivity, or neurotransmitter system responses, respectively. DTI allows estimation of the structural integrity and connectivity of white matter tracts. Together, these MRI methods provide an unprecedented means to (a) measure longitudinal changes in tissue structure and function close by and remote from ischemic lesion areas, (b) evaluate the organizational profile of neural networks after stroke, and (c) identify degenerative and restorative processes that affect post-stroke functional outcome. Besides, the availability of MRI in clinical institutions as well as research laboratories provides an optimal basis for translational research on stroke recovery. This review gives an overview of the current status and perspectives of fMRI and DTI applications to study brain reorganization in experimental stroke models

Assessment of nerve involvement in the lumbar spine: agreement between magnetic resonance imaging, physical examination and pain drawing findings

<p>Abstract</p> <p>Background</p> <p>Detection of nerve involvement originating in the spine is a primary concern in the assessment of spine symptoms. Magnetic resonance imaging (MRI) has become the diagnostic method of choice for this detection. However, the agreement between MRI and other diagnostic methods for detecting nerve involvement has not been fully evaluated. The aim of this diagnostic study was to evaluate the agreement between nerve involvement visible in MRI and findings of nerve involvement detected in a structured physical examination and a simplified pain drawing.</p> <p>Methods</p> <p>Sixty-one consecutive patients referred for MRI of the lumbar spine were - without knowledge of MRI findings - assessed for nerve involvement with a simplified pain drawing and a structured physical examination. Agreement between findings was calculated as overall agreement, the p value for McNemar's exact test, specificity, sensitivity, and positive and negative predictive values.</p> <p>Results</p> <p>MRI-visible nerve involvement was significantly less common than, and showed weak agreement with, physical examination and pain drawing findings of nerve involvement in corresponding body segments. In spine segment L4-5, where most findings of nerve involvement were detected, the mean sensitivity of MRI-visible nerve involvement to a positive neurological test in the physical examination ranged from 16-37%. The mean specificity of MRI-visible nerve involvement in the same segment ranged from 61-77%. Positive and negative predictive values of MRI-visible nerve involvement in segment L4-5 ranged from 22-78% and 28-56% respectively.</p> <p>Conclusion</p> <p>In patients with long-standing nerve root symptoms referred for lumbar MRI, MRI-visible nerve involvement significantly underestimates the presence of nerve involvement detected by a physical examination and a pain drawing. A structured physical examination and a simplified pain drawing may reveal that many patients with "MRI-invisible" lumbar symptoms need treatment aimed at nerve involvement. Factors other than present MRI-visible nerve involvement may be responsible for findings of nerve involvement in the physical examination and the pain drawing.</p

Improving crop yield potential: Underlying biological processes and future prospects

The growing world population and global increases in the standard of living both result in an increasing demand for food, feed and other plant‐derived products. In the coming years, plant‐based research will be among the major drivers ensuring food security and the expansion of the bio‐based economy. Crop productivity is determined by several factors, including the available physical and agricultural resources, crop management, and the resource use efficiency, quality and intrinsic yield potential of the chosen crop. This review focuses on intrinsic yield potential, since understanding its determinants and their biological basis will allow to maximize the plant's potential in food and energy production. Yield potential is determined by a variety of complex traits that integrate strictly regulated processes and their underlying gene regulatory networks. Due to this inherent complexity, numerous potential targets have been identified that could be exploited to increase crop yield. These encompass diverse metabolic and physical processes at the cellular, organ and canopy level. We present an overview of some of the distinct biological processes considered to be crucial for yield determination that could further be exploited to improve future crop productivity

A united statement of the global chiropractic research community against the pseudoscientific claim that chiropractic care boosts immunity.

BACKGROUND: In the midst of the coronavirus pandemic, the International Chiropractors Association (ICA) posted reports claiming that chiropractic care can impact the immune system. These claims clash with recommendations from the World Health Organization and World Federation of Chiropractic. We discuss the scientific validity of the claims made in these ICA reports. MAIN BODY: We reviewed the two reports posted by the ICA on their website on March 20 and March 28, 2020. We explored the method used to develop the claim that chiropractic adjustments impact the immune system and discuss the scientific merit of that claim. We provide a response to the ICA reports and explain why this claim lacks scientific credibility and is dangerous to the public. More than 150 researchers from 11 countries reviewed and endorsed our response. CONCLUSION: In their reports, the ICA provided no valid clinical scientific evidence that chiropractic care can impact the immune system. We call on regulatory authorities and professional leaders to take robust political and regulatory action against those claiming that chiropractic adjustments have a clinical impact on the immune system