110 results found
Kaya K, Zavriyev AI, Orihuela-Espina F, et al., 2022, Intraoperative Cerebral Hemodynamic Monitoring during Carotid Endarterectomy via Diffuse Correlation Spectroscopy and Near-Infrared Spectroscopy, Brain Sciences, Vol: 12
Objective: This pilot study aims to show the feasibility of noninvasive and real-time cerebral hemodynamic monitoring during carotid endarterectomy (CEA) via diffuse correlation spectroscopy (DCS) and near-infrared spectroscopy (NIRS). Methods: Cerebral blood flow index (CBFi) was measured unilaterally in seven patients and bilaterally in seventeen patients via DCS. In fourteen patients, hemoglobin oxygenation changes were measured bilaterally and simultaneously via NIRS. Cerebral autoregulation (CAR) and cerebrovascular resistance (CVR) were estimated using CBFi and arterial blood pressure data. Further, compensatory responses to the ipsilateral hemisphere were investigated at different contralateral stenosis levels. Results: Clamping of carotid arteries caused a sharp increase of CVR (~70%) and a marked decrease of ipsilateral CBFi (57%). From the initial drop, we observed partial recovery in CBFi, an increase of blood volume, and a reduction in CVR in the ipsilateral hemisphere. There were no significant changes in compensatory responses between different contralateral stenosis levels as CAR was intact in both hemispheres throughout the CEA phase. A comparison between hemispheric CBFi showed lower ipsilateral levels during the CEA and post-CEA phases (p < 0.001, 0.03). Conclusion: DCS alone or combined with NIRS is a useful monitoring technique for real-time assessment of cerebral hemodynamic changes and allows individualized strategies to improve cerebral perfusion during CEA by identifying different hemodynamic metrics.
Ayaz H, Baker WB, Blaney G, et al., 2022, Optical imaging and spectroscopy for the study of the human brain: status report, Neurophotonics, Vol: 9, ISSN: 2329-423X
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
Sunwoo J, Zavriyev A, Kaya K, et al., 2022, Diffuse correlation spectroscopy blood flow monitoring for intraventricular hemorrhage vulnerability in extremely low gestational age newborns, SCIENTIFIC REPORTS, Vol: 12, ISSN: 2045-2322
- Author Web Link
- Citations: 1
Rivas JJ, Lara MDC, Castrejon L, et al., 2022, Multi-label and multimodal classifier for affective states recognition in virtual rehabilitation, IEEE Transactions on Affective Computing, Vol: 13, Pages: 1183-1194, ISSN: 1949-3045
Computational systems that process multiple affective states may benefit from explicitly considering the interaction between the states to enhance their recognition performance. This work proposes the combination of a multi-label classifier, Circular Classifier Chain (CCC), with a multimodal classifier, Fusion using a Semi-Naive Bayesian classifier (FSNBC), to include explicitly the dependencies between multiple affective states during the automatic recognition process. This combination of classifiers is applied to a virtual rehabilitation context of post-stroke patients. We collected data from post-stroke patients, which include finger pressure, hand movements, and facial expressions during ten longitudinal sessions. Videos of the sessions were labelled by clinicians to recognize four states: tiredness, anxiety, pain, and engagement. Each state was modelled by the FSNBC receiving the information of finger pressure, hand movements, and facial expressions. The four FSNBCs were linked in the CCC to exploit the dependency relationships between the states. The convergence of CCC was reached by 5 iterations at most for all the patients. Results (ROC AUC)) of CCC with the FSNBC are over 0.940±0.045 ( mean±std.deviation ) for the four states. Relationships of mutual exclusion between engagement and all the other states and co-occurrences between pain and anxiety were detected and discussed.
Zaqueros-Martinez J, Rodríguez-Gómez G, Tlelo-Cuautle E, et al., 2022, Trigonometric Polynomials Methods to Simulate Oscillating Chaotic Systems, ISSN: 0094-243X
Numerical simulations are an important tool in the understanding of chaotic dynamic systems. Limitations inherent to the simulations may lead to erroneous interpretations of the behavior of these systems. Among these limitations are superstability (suppression of the chaos of chaotic systems), computational chaos (induction of chaos in non-chaotic systems) and apparent contradictions between theoretical predictions and observed responses in the simulations. These issues are present whether the solver is fixed-step or variable-step. If we are to trust the output of numerical approaches when solving dynamic systems, it is of utmost importance to know whether any simulations are yielding the correct answers, or whether such answers have been affected by errors. Such errors are either inherent to the numerical approach or due to rounding. In both cases, they may alter the outcome to the point of not representing the true dynamics of the system. Simulations ought to reflect the system’s true behavior. The outcomes of the simulations must be coherent with the simulated system. If one wants to avoid numerical approaches that may induce computational chaos, superstability or theoretical discrepancies, it is necessary to choose the right numerical method. This research investigates the potential selection of a numerical method capitalizing on known characteristics of the dynamic system. The use of special methods that tap on some known features of the chaotic dynamic system at hand has received little attention thus far. In this work, we propose the use of trigonometric polynomials methods (TPM’s) derived by Gautschi . Gautschi’s method leverages on the oscillatory response inherent to chaotic systems. We hypothesize that using Gautschi’s method should result in simulations that adhere more accurately to the real solution. This is in contrast to the use of traditional methods commonly applied for these kinds of simulations. We give evidence that th
Garcia-Mendoza J-L, Villasenor-Pineda L, Orihuela-Espina F, 2022, Risks of misinterpretation in the evaluation of Distant Supervision for Relation Extraction, PROCESAMIENTO DEL LENGUAJE NATURAL, Pages: 71-83, ISSN: 1135-5948
Garcia-Mendoza J-L, Villasenor-Pineda L, Orihuela-Espina F, et al., 2022, An autoencoder-based representation for noise reduction in distant supervision of relation extraction, JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, Vol: 42, Pages: 4523-4529, ISSN: 1064-1246
García-Mendoza JL, Villaseñor-Pineda L, Buscaldi D, et al., 2022, Evaluation of a New Representation for Noise Reduction in Distant Supervision, Pages: 101-113, ISSN: 0302-9743
Distant Supervision is a relation extraction approach that allows automatic labeling of a dataset. However, this labeling introduces noise in the labels (e.g., when two entities in a sentence are automatically labeled with an invalid relation). Noise in labels makes difficult the relation extraction task. This noise is precisely one of the main challenges of this task. Until now, the methods that incorporate a previous noise reduction step do not evaluate the performance of this step. This paper evaluates the noise reduction using a new representation obtained with autoencoders. In addition, it was incoporated more information to the input of the autoencoder proposed in the state-of-the-art to improve the representation over which the noise is reduced. Also, three methods were proposed to select the instances considered as real. As a result, it was obtained the highest values of the area under the ROC curves using the improved input combined with state-of-the-art anomaly detection methods. Moreover, the three proposed selection methods significantly improve the existing method in the literature.
Zaqueros-Martinez J, Rodriguez-Gomez G, Tlelo-Cuautle E, et al., 2022, Synchronization of Chaotic Electroencephalography (EEG) Signals, Studies in Big Data, Pages: 83-108
Synchronization of chaotic signals often considers a master-slave paradigm where a slave chaotic system is required to follow the master also chaotic. Most times in literature both systems are known, but synchronization to some unknown master has a potentially large range of applications, for example, EEG based authentication. We aim to test the feasibility of fuzzy control to systematically synchronize a chaotic EEG record. In this chapter, we study the suitability of two chaotic systems and the companion fuzzy control strategies under complete and projective synchronization to synchronize to EEG records. We used two public EEG datasets related to the genetic predisposition to alcoholism and with detecting emotions respectively. We present a comparative study among fuzzy control strategies for synchronization of chaotic systems to EEG records on selected datasets. As expected, we observed success and failures alike on the synchronization highlighting the difficulty in achieving this kind of synchronization, but we interpret this as advantageous for purposes of the suggested domain application. With successful synchronizations, we confirm that synchronization is feasible. With unsuccessful synchronizations, we illustrate that synchronization of chaotic systems does not follow a simple one-size-fits-all recipe and we attempt to gain insight for future research. The same chaotic system may succeed or fail depending on its companion type of synchronization and controller design.
Del Angel Arrieta F, Rojas Cisneros M, Rivas JJ, et al., 2021, Characterization of a raspberry Pi as the core for a low-cost multimodal EEG-fNIRS platform., 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Publisher: IEEE, Pages: 1288-1291, ISSN: 1557-170X
Poor understanding of brain recovery after injury, sparsity of evaluations and limited availability of healthcare services hinders the success of neurorehabilitation programs in rural communities. The availability of neuroimaging ca-pacities in remote communities can alleviate this scenario supporting neurorehabilitation programs in remote settings. This research aims at building a multimodal EEG-fNIRS neuroimaging platform deployable to rural communities to support neurorehabilitation efforts. A Raspberry Pi 4 is chosen as the CPU for the platform responsible for presenting the neurorehabilitation stimuli, acquiring, processing and storing concurrent neuroimaging records as well as the proper synchronization between the neuroimaging streams. We present here two experiments to assess the feasibility and characterization of the Raspberry Pi as the core for a multimodal EEG-fNIRS neuroimaging platform; one over controlled conditions using a combination of synthetic and real data, and another from a full test during resting state. CPU usage, RAM usage and operation temperature were measured during the tests with mean operational records below 40% for CPU cores, 13.6% for memory and 58.85 ° C for temperatures. Package loss was inexistent on synthetic data and negligible on experimental data. Current consumption can be satisfied with a 1000 mAh 5V battery. The Raspberry Pi 4 was able to cope with the required workload in conditions of operation similar to those needed to support a neurorehabilitation evaluation.
Borrego G, Morán AL, Meza V, et al., 2021, Key factors that influence the UX of a dual-player game for the cognitive stimulation and motor rehabilitation of older adults, Universal Access in the Information Society, Vol: 20, Pages: 767-783, ISSN: 1615-5289
In this work, the results of usability and user experience (UX) evaluation of a serious video game for the cognitive stimulation and motor rehabilitation of the upper limb of the elderly are presented. The serious game includes features that allow (1) performing cooperative therapy exercises between two patients, (2) remote session configuration therapy, and (3) monitoring/analyzing the sessions’ results by the therapist. An evaluation of the game with 16 older adults provides evidence about how the tool is perceived by participants, who embraced it as stimulating, useful, usable and even fun, and which impacts in therapy compliance and acceptability by the elderly. In addition, through an in depth analysis of the participants’ performance and observed emotions, as well as their self-report, we determined which engagement attributes are present in the game. Finally, we also found evidence that suggests that the participants’ skill level and the game difficulty level rather than just a good performance on the game are key factors that influence their enjoyment and frustration.
Sharma C, Singh H, Orihuela-Espina F, et al., 2021, Visual gaze patterns reveal surgeons' ability to identify risk of bile duct injury during laparoscopic cholecystectomy, HPB, Vol: 23, Pages: 715-722, ISSN: 1365-182X
BACKGROUND: Bile duct injury is a serious surgical complication of laparoscopic cholecystectomy. The aim of this study was to identify distinct visual gaze patterns associated with the prompt detection of bile duct injury risk during laparoscopic cholecystectomy. METHODS: Twenty-nine participants viewed a laparoscopic cholecystectomy that led to a serious bile duct injury ('BDI video') and an uneventful procedure ('control video') and reported when an error was perceived that could result in bile duct injury. Outcome parameters include fixation sequences on anatomical structures and eye tracking metrics. Surgeons were stratified into two groups based on performance and compared. RESULTS: The 'early detector' group displayed reduced common bile duct dwell time in the first half of the BDI video, as well as increased cystic duct dwell time and Calot's triangle glances count during Calot's triangle dissection in the control video. Machine learning based classification of fixation sequences demonstrated clear separability between early and late detector groups. CONCLUSION: There are discernible differences in gaze patterns associated with early recognition of impending bile duct injury. The results could be transitioned into real time and used as an intraoperative early warning system and in an educational setting to improve surgical safety and performance.
Wu K-C, Sunwoo J, Sheriff F, et al., 2021, Validation of diffuse correlation spectroscopy measures of critical closing pressure against transcranial Doppler ultrasound in stroke patients, Journal of Biomedical Optics, Vol: 26, Pages: 036008-1-036008-14, ISSN: 1083-3668
SIGNIFICANCE: Intracranial pressure (ICP), variability in perfusion, and resulting ischemia are leading causes of secondary brain injury in patients treated in the neurointensive care unit. Continuous, accurate monitoring of cerebral blood flow (CBF) and ICP guide intervention and ultimately reduce morbidity and mortality. Currently, only invasive tools are used to monitor patients at high risk for intracranial hypertension. AIM: Diffuse correlation spectroscopy (DCS), a noninvasive near-infrared optical technique, is emerging as a possible method for continuous monitoring of CBF and critical closing pressure (CrCP or zero-flow pressure), a parameter directly related to ICP. APPROACH: We optimized DCS hardware and algorithms for the quantification of CrCP. Toward its clinical translation, we validated the DCS estimates of cerebral blood flow index (CBFi) and CrCP in ischemic stroke patients with respect to simultaneously acquired transcranial Doppler ultrasound (TCD) cerebral blood flow velocity (CBFV) and CrCP. RESULTS: We found CrCP derived from DCS and TCD were highly linearly correlated (ipsilateral R2 = 0.77, p = 9 × 10 - 7; contralateral R2 = 0.83, p = 7 × 10 - 8). We found weaker correlations between CBFi and CBFV (ipsilateral R2 = 0.25, p = 0.03; contralateral R2 = 0.48, p = 1 × 10 - 3) probably due to the different vasculature measured. CONCLUSION: Our results suggest DCS is a valid alternative to TCD for continuous monitoring of CrCP.
Andrea B-M, Felipe O-E, Alberto R-GC, 2021, Ordering of functions according to multiple fuzzy criteria: application to denoising electroencephalography, Soft Computing, Vol: 25, Pages: 8573-8593, ISSN: 1432-7643
We introduce a new relation of order over functions according to multiple fuzzy criteria. Proof of the complied properties for relations of partial orders is given. Convergent and divergent validity of the new membership functions is established. Tolerance to noise of the relation of order is evaluated by corrupting synthetic prototypes and observing changes in the retrieved ordering. The effect of weighting strategies is evaluated in terms of Jaccard and XOR indices. The performance of the ordering algorithm is quantified in terms of richness of the resulting Hasse diagram. Applicability is demonstrated in the context of de-noising electroencephalographic (EEG) signals exemplified over two datasets and evaluated by classification wrapping.
Hernandez-Franco J, Orihuela-Espina F, Palafox L, et al., 2021, Remote central effects of botulinum toxin type A as adjuvant to intense occupational therapy in the early stage of stroke: A Type II fMRI randomised controlled trial, TOXINS Conference on Basic Science and Clinical Aspects of Botulinum and other Neurotoxins, Publisher: Elsevier, Pages: S33-S33, ISSN: 0041-0101
Introduction: Improvements in motor function following interventions incorporating botulinum toxin type A (BTX-A) remain controversial, with existing studies yielding contrasting results.1-3 The mechanisms underlying BTX-A remote central effects are still under investigation. It is hypothesized that the toxin administration strategy may play a role in producing such differing outcomes. We tested a strategy based on modulating muscle synergies.Aim: The aim of the study was to investigate the clinical and remote central effects of an occupational therapy intervention combined with adjunctive BTX-A compared to the same occupational therapy without the adjuvant application of the toxin.Methods: A two-group, parallel, pre-post, randomized controlled trial was performed. The clinical effects of occupational therapy when performed following BTX-A injections to disinhibit finger flexors (n=5) was compared to those of an equal dose of occupational therapy alone (n=6). Motor dexterity and function were assessed using the Fugl-Meyer Scale, Motor Index, Arm Activity Measure, 9-Hole Peg Test, and Box and Block Test, and differences were analysed using ANCOVA. Brain activity was examined using functional magnetic resonance imaging (fMRI), and between-group differences were analysed using contrast statistical parametric mapping.Results: Both groups started in statistically similar conditions. Both treatments provided significant clinical improvements compared to baseline. The total differences in change score on the Fugl-Meyer Scale and Motor Index were larger, though not significantly, in the toxin-treated group than in the control group (Figure). When the toxin is administered, activity in the brain is more localised and appears more in the right hemisphere in subjects in the toxin-treated group and more in the left in those in the control group.Conclusions: Functional improvements were observed in the toxin-treated group, but the effect size compared to the control group was to
Rojas-Cisneros M, Montero-Hernandez SA, Orihuela-Espina F, 2021, Analysis in the broader, medium, and narrow autism phenotypes using fNIRS
Characterizing the brain activity of autistic phenotypes (AP) could inform an endophenotypic understanding of ASD. We present possibly the first study comparing brain responses in AP using fNIRS. Syllabic stress may unveil a potential biomarker.
Joel Rivas J, Orihuela-Espina F, Enrique Sucar L, et al., 2021, Dealing with a Missing Sensor in a Multilabel and Multimodal Automatic Affective States Recognition System, 9th International Conference on Affective Computing and Intelligent Interaction (ACII), Publisher: IEEE, ISSN: 2156-8103
Orihuela-Espina F, Rojas-Cisneros M, Montero-Hernández SA, et al., 2021, Physics augmented classification of fNIRS signals, Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms, and Applications, Pages: 375-405, ISBN: 9780128201251
Background. Predictive classification favors performance over semantics. In traditional predictive classification pipelines, feature engineering is often oblivious to the underlying phenomena. Hypothesis. In applied domains, such as functional near infrared spectroscopy (fNIRS), the exploitation of physical knowledge may improve the discriminative quality of our observation set. Aim. Give exemplary evidence that intervening the physical observation process can augment classification. Methods. We manipulate the observation process in four ways independently. First, sampling and quantization are designed to enhance class-related contrast. Second, we show how selection of optical filters affects the cross-talk, in turn, affecting classification. Third, we regularize the inverse problem to maximize sensitivity to any gradient that would later support the classification. And fourth, we introduce a catalyst covariate during experiment design to exacerbate response differences. Results. For each of the proposed manipulations, we show that the performance of the classification exercise is altered in some way or another. Conclusions. Exploitation knowledge of physics even before acquisition can support classification, alleviating otherwise blind feature engineering. This can also enhance interpretability of the classification model.
Ávila-Sansores S-M, Rodríguez-Gómez G, Tachtsidis I, et al., 2020, Interpolated functional manifold for functional near-infrared spectroscopy analysis at group level, Neurophotonics, Vol: 7, Pages: 045009-045009, ISSN: 2329-423X
Significance: Solutions for group-level analysis of connectivity from fNIRS observations exist,but groupwise explorative analysis with classical solutions is often cumbersome. Manifoldbased solutions excel at data exploration, but there are infinite surfaces crossing the observationscloud of points.Aim: We aim to provide a systematic choice of surface for a manifold-based analysis of connectivity at group level with small surface interpolation error.Approach: This research introduces interpolated functional manifold (IFM). IFM builds a manifold from reconstructed changes in concentrations of oxygenated ΔcHbO2 and reduced ΔcHbRhemoglobin species by means of radial basis functions (RBF). We evaluate the root mean squareerror (RMSE) associated to four families of RBF. We validated our model against psychophysiological interactions (PPI) analysis using the Jaccard index (JI). We demonstrate the usability inan experimental dataset of surgical neuroergonomics.Results: Lowest interpolation RMSE was 1.26e − 4 1.32e − 8 for ΔcHbO2 [A.U.] and4.30e − 7 2.50e − 13 [A.U.] for ΔcHbR. Agreement with classical group analysis was JI ¼0.89 0.01 for ΔcHbO2. Agreement with PPI analysis was JI ¼ 0.83 0.07 for ΔcHbO2 andJI ¼ 0.77 0.06 for ΔcHbR. IFM successfully decoded group differences [ANOVA: ΔcHbO2:Fð2;117Þ ¼ 3.07; p < 0.05; ΔcHbR: Fð2;117Þ ¼ 3.35; p < 0.05].Conclusions: IFM provides a pragmatic solution to the problem of choosing the manifold associated to a cloud of points, facilitating the use of manifold-based solutions for the group analysisof fNIRS datasets.
Casillas-Figueroa R, Morán AL, Meza-Kubo V, et al., 2020, ReminiScentia: shaping olfactory interaction in a personal space for multisensory stimulation therapy, Personal and Ubiquitous Computing, ISSN: 1617-4909
Recently, multimodal interfaces are incorporating smell as an additional means of interaction. Devices called olfactory displays have been designed to improve applications with various objectives, such as notifying or alerting through scents, increasing immersion in virtual or augmented reality applications, or learning and enhancement of mental functions. Based on the potential of olfactory memory to evoke memories and emotions to benefit health and well-being, we propose ReminiScentia as an olfactory display to generate and deliver scents. This work presents an evaluation of the effectiveness of ReminiScentia in evoking brain responses similar to those generated by manually delivered scents. To achieve this, we monitored the hemodynamic responses during manual and ReminiScentia olfactory stimulation over the prefrontal cortex (PFC) by using a functional near-infrared spectroscopy (fNIRS) device in 33 healthy subjects. Among the results, it was found that when ReminiScentia was used to deliver the olfactory stimuli, there is no statistically significant difference in the magnitude of concentration changes of OxyHb in the PFC between manual deliver and ReminiScentia (Wilcoxon: p > 0.05). The effectiveness of the use of ReminiScentia has allowed us not only its application for the evocation of memories in a multisensory therapy but also to propose an olfactory interaction design space where olfactory stimuli are used to provide feedback or instructions in multisensory stimulation activities that could support the training of higher mental functions such as memory and learning in patients with cognitive disabilities.
Rivas JJ, Orihuela-Espina F, Palafox L, et al., 2020, Unobtrusive inference of affective states in virtual rehabilitation from upper limb motions: a feasibility study, IEEE Transactions on Affective Computing, Vol: 11, Pages: 470-481, ISSN: 1949-3045
Virtual rehabilitation environments may afford greater patient personalization if they could harness the patient's affective state. Four states: anxiety, pain, engagement and tiredness (either physical or psychological), were hypothesized to be inferable from observable metrics of hand location and gripping strength -relevant for rehabilitation-. Contributions are; (a) multiresolution classifier built from Semi-Naïve Bayesian classifiers, and (b) establishing predictive relations for the considered states from the motor proxies capitalizing on the proposed classifier with recognition levels sufficient for exploitation. 3D hand locations and gripping strength streams were recorded from 5 post-stroke patients whilst undergoing motor rehabilitation therapy administered through virtual rehabilitation along 10 sessions over 4 weeks. Features from the streams characterized the motor dynamics, while spontaneous manifestations of the states were labelled from concomitant videos by experts for supervised classification. The new classifier was compared against baseline support vector machine (SVM) and random forest (RF) with all three exhibiting comparable performances. Inference of the aforementioned states departing from chosen motor surrogates appears feasible, expediting increased personalization of virtual motor neurorehabilitation therapies.
Zavriyev AI, Kaya K, Farzam P, et al., 2020, Diffuse correlation spectroscopy used to monitor cerebral blood flow during adult hypothermic circulatory arrests, Publisher: Cold Spring Harbor Laboratory
Real-time noninvasive monitoring of cerebral blood flow during surgery could improve the morbidity and mortality rates associated with hypothermic circulatory arrests (HCA) in adult cardiac patients. In this study, we used a combined frequency domain near-infrared spectroscopy (FDNIRS) and diffuse correlation spectroscopy (DCS) system to measure cerebral oxygen saturation (SO2) and an index of blood flow (CBFi) in 12 adults going under cardiac surgery with HCA. Our measurements revealed that a negligible amount of blood is delivered to the brain during HCA with retrograde cerebral perfusion (RCP), indistinguishable from HCA-only cases (CBFi drops of 91% ± 3% and 96% ± 2%, respectively) and that CBFi drops for both are significantly higher than drops during HCA with antegrade cerebral perfusion (ACP) (p = 0.003). We conclude that FDNIRS-DCS can be a powerful tool to optimize cerebral perfusion, and that RCP needs to be further examined to confirm its efficacy, or lack thereof.
Sunwoo J, Nair V, Steele T, et al., 2020, Assessment of cerebral autoregulation in extremely low gestational age newborns using diffuse correlation spectroscopy
We use diffuse correlation spectroscopy to safely quantify cerebral blood flow response to spontaneous fluctuations in autonomic and respiratory activities to help characterize the elevated risk of intraventricular hemorrhage in extremely premature newborns.
Joel Rivas J, Orihuela-Espina F, Enrique Sucar L, et al., 2019, Automatic recognition of multiple affective states in virtual rehabilitation by exploiting the dependency relationships, 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Publisher: IEEE, Pages: 655-661, ISSN: 2156-8103
The automatic recognition of multiple affective states can be enhanced if the underpinning computational models explicitly consider the interactions between the states. This work proposes a computational model that incorporates the dependencies between four states (tiredness, anxiety, pain, and engagement)known to appear in virtual rehabilitation sessions of post-stroke patients, to improve the automatic recognition of the patients' states. A dataset of five stroke patients which includes their fingers' pressure (PRE), hand movements (MOV)and facial expressions (FAE)during ten sessions of virtual rehabilitation was used. Our computational proposal uses the Semi-Naive Bayesian classifier (SNBC)as base classifier in a multiresolution approach to create a multimodal model with the three sensors (PRE, MOV, and FAE)with late fusion using SNBC (FSNB classifier). There is a FSNB classifier for each state, and they are linked in a circular classifier chain (CCC)to exploit the dependency relationships between the states. Results of CCC are over 90% of ROC AUC for the four states. Relationships of mutual exclusion between engagement and all the other states and some co-occurrences between pain and anxiety for the five patients were detected. Virtual rehabilitation platforms that incorporate the automatic recognition of multiple patient's states could leverage intelligent and empathic interactions to promote adherence to rehabilitation exercises.
Modi HN, Singh H, Fiorentino F, et al., 2019, Association of residents' neural signatures with stress resilience during surgery, JAMA Surgery, Vol: 154, ISSN: 2168-6254
Importance: Intraoperative stressors may compound cognitive load, prompting performance decline and threatening patient safety. However, not all surgeons cope equally well with stress, and the disparity between performance stability and decline under high cognitive demand may be characterized by differences in activation within brain areas associated with attention and concentration such as the prefrontal cortex (PFC). Objective: To compare PFC activation between surgeons demonstrating stable performance under temporal stress with those exhibiting stress-related performance decline. Design, Setting, and Participants: Cohort study conducted from July 2015 to September 2016 at the Imperial College Healthcare National Health Service Trust, England. One hundred two surgical residents (postgraduate year 1 and greater) were invited to participate, of which 33 agreed to partake. Exposures: Participants performed a laparoscopic suturing task under 2 conditions: self-paced (SP; without time-per-knot restrictions), and time pressure (TP; 2-minute per knot time restriction). Main Outcomes and Measures: A composite deterioration score was computed based on between-condition differences in task performance metrics (task progression score [arbitrary units], error score [millimeters], leak volume [milliliters], and knot tensile strength [newtons]). Based on the composite score, quartiles were computed reflecting performance stability (quartile 1 [Q1]) and decline (quartile 4 [Q4]). Changes in PFC oxygenated hemoglobin concentration (HbO2) measured at 24 different locations using functional near-infrared spectroscopy were compared between Q1 and Q4. Secondary outcomes included subjective workload (Surgical Task Load Index) and heart rate. Results: Of the 33 participants, the median age was 33 years, the range was 29 to 56 years, and 27 were men (82%). The Q1 residents demonstrated task-induced increases in HbO2 across the bilateral ventrolateral PFC (VLPFC) and right dorsolateral P
Wu K, Farzam P, Sheriff F, et al., 2019, Monitoring cerebral blood flow and critical closing pressure in stroke patients, 29th International Symposium on Cerebral Blood Flow, Metabolism and Function / 14th International Conference on Quantification of Brain Function with PET (BRAIN and BRAIN Pet), Publisher: SAGE Publications, Pages: 258-258, ISSN: 0271-678X
Modi H, Singh H, Fiorentino F, et al., 2019, Neural signatures of resident resilience, JAMA Surgery, ISSN: 2168-6254
Importance: Intraoperative stressors may compound cognitive load, prompting performance decline and threatening patient safety. However, not all surgeons cope equally well with stress, and the disparity between performance stability and decline under high cognitive demand may be characterized by differences in activation within brain areas associated with attention and concentration such as the prefrontal cortex (PFC).Objective: To compare PFC activation between surgeons demonstrating stable performance under temporal stress with those exhibiting stress-related performance decline. The a priori hypothesis being that under temporal demand sustained prefrontal “activation(s)” reflect performance stability, whereas performance decline is manifest as “deactivation(s)”.Design: Cohort study conducted from July 2015 to September 2016. Setting: Single center (Imperial College Healthcare NHS Trust, United Kingdom). Participants: 102 surgical residents (PGY1 and above) were invited to participate, of which 33 agreed to partake (median age [range]: 33 [29-56] years, 27 [82%] males).Exposure: Subjects performed a laparoscopic suturing task under two conditions: ‘self-paced’ (SP; without time per knot restrictions), and ‘time pressure’ (TP; two-minute per knot time restriction). Main Outcomes and Measures: A composite deterioration score was computed based on between-condition differences in task performance metrics [(task progression score (au), error score (mm), leak volume (ml) and knot tensile strength (N)]. Based on the composite score, quartiles were computed reflecting performance stability (Q1) and decline (Q4). Changes in PFC oxygenated haemoglobin concentration (HbO2) measured at 24 different locations using functional near-infrared spectroscopy were compared between Q1 and Q4. Secondary outcomes included subjective workload (Surgical Task Load Index) and heart rate. Results: Q1 residents demonstrated task-induced incr
Joel Rivas J, Orihuela-Espina F, Enrique Sucar L, 2019, Recognition of affective states in virtual rehabilitation using late fusion with semi-naive Bayesian classifier, 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Publisher: Association for Computing Machinery, Pages: 308-313, ISSN: 2153-1633
Virtual rehabilitation platforms may tailor the rehabilitation tasks to the patients' needs if they could recognize the patient's affective state. Affective states recognition systems can enhance their performance if they receive data coming from different sensors of human behaviour. In this work, we propose a late Fusion using Semi-Naive Bayesian classifier (FSNB) as a multimodal affective states recognition system to infer four states: tiredness, anxiety, pain, and motivation, from observable metrics of fingers pressure, hand movements, and facial expressions of post-stroke patients. Data streams were recorded from 5 post-stroke patients while they attended virtual rehabilitation therapies along 10 sessions over 4 weeks, manifesting the aforementioned states spontaneously. Recognition rates of the FSNB classifier were over 90% (with a standard deviation of around ± 0.06) of AUC for the four states. These results represent contributions for enhancing the development of affective states recognition systems in virtual rehabilitation.
Alejandro Hernandez-Contreras D, Peregrina-Barreto H, De Jesus Rangel-Magdaleno J, et al., 2019, Statistical approximation of plantar temperature distribution on diabetic subjects based on beta mixture model, IEEE Access, Vol: 7, Pages: 28383-28391, ISSN: 2169-3536
A change in plantar temperature distribution can be an indicator of tissue damage, inflammation, or peripheral vascular abnormalities associated with diabetic foot. Despite the efforts to detect these abnormalities through infrared thermography, there are still several problems to be addressed, especially to detect abnormalities on each foot separately. In this paper, a characterization of the plantar temperature distribution based on a probabilistic approach is proposed. The objective is to detect temperature variations on each foot eluding contralateral comparison. A beta mixture model with four components approximates the plantar temperature distributions of diabetic and non-diabetic subjects. Each component represents an area of the plantar region: toes; metatarsal heads; arch; and heel. The approximation was applied to 60 temperature distributions of non-diabetic subjects and 220 of diabetic subjects. The results suggest that it is possible to characterize distribution in terms of the mean of its beta components.
Lami M, Singh H, Dilley JH, et al., 2018, Gaze patterns hold key to unlocking successful search strategies and increasing polyp detection rate in colonoscopy, Endoscopy, Vol: 50, Pages: 701-707, ISSN: 1438-8812
BACKGROUND: The adenoma detection rate (ADR) is an important quality indicator in colonoscopy. The aim of this study was to evaluate the changes in visual gaze patterns (VGPs) with increasing polyp detection rate (PDR), a surrogate marker of ADR. METHODS: 18 endoscopists participated in the study. VGPs were measured using eye-tracking technology during the withdrawal phase of colonoscopy. VGPs were characterized using two analyses - screen and anatomy. Eye-tracking parameters were used to characterize performance, which was further substantiated using hidden Markov model (HMM) analysis. RESULTS: Subjects with higher PDRs spent more time viewing the outer ring of the 3 × 3 grid for both analyses (screen-based: r = 0.56, P = 0.02; anatomy: r = 0.62, P < 0.01). Fixation distribution to the "bottom U" of the screen in screen-based analysis was positively correlated with PDR (r = 0.62, P = 0.01). HMM demarcated the VGPs into three PDR groups. CONCLUSION: This study defined distinct VGPs that are associated with expert behavior. These data may allow introduction of visual gaze training within structured training programs, and have implications for adoption in higher-level assessment.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.