Imperial College London

DrFelipeOrihuela-Espina

Faculty of MedicineDepartment of Surgery & Cancer

Honorary Lecturer
 
 
 
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Publications

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106 results found

Zaqueros-Martinez J, Rodríguez-Gómez G, Tlelo-Cuautle E, Orihuela-Espina Fet 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 [1]. 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

Conference paper

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

Journal article

Zaqueros-Martinez J, Rodriguez-Gomez G, Tlelo-Cuautle E, Orihuela-Espina Fet 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.

Book chapter

Garcia-Mendoza J-L, Villasenor-Pineda L, Orihuela-Espina F, Bustio-Martinez Let 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

Journal article

Del Angel Arrieta F, Rojas Cisneros M, Rivas JJ, Castrejon LR, Sucar LE, Andreu-Perez J, Orihuela-Espina Fet 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.

Conference paper

Borrego G, Morán AL, Meza V, Orihuela-Espina F, Sucar LEet 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.

Journal article

Sharma C, Singh H, Orihuela-Espina F, Darzi A, Sodergren MHet 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.

Journal article

Wu K-C, Sunwoo J, Sheriff F, Farzam P, Farzam PY, Orihuela-Espina F, LaRose SL, Monk AD, Aziz-Sultan MA, Patel N, Vaitkevicius H, Franceschini MAet 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.

Journal article

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.

Journal article

Rivas JJ, Lara MDC, Castrejon L, Hernandez-Franco J, Orihuela-Espina F, Palafox L, Williams A, Berthouze N, Sucar Eet al., 2021, Multi-label and multimodal classifier for affective states recognition in virtual rehabilitation, IEEE Transactions on Affective Computing, Pages: 1-1, 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.

Journal article

Hernandez-Franco J, Orihuela-Espina F, Palafox L, Palencia C, Camberos-Angulo C, de los Remedios Quijada-Cruz M, Rene Marrufo-Melendez O, Cuervo-Soto B, Enrique Sucar Let 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

Conference paper

Joel Rivas J, Orihuela-Espina F, Enrique Sucar L, Bianchi-Berthouze Net 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

Conference paper

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.

Conference paper

Orihuela-Espina F, Rojas-Cisneros M, Montero-Hernández SA, García-Salinas JS, Cuervo-Soto B, Herrera-Vega Jet 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.

Book chapter

Ávila-Sansores S-M, Rodríguez-Gómez G, Tachtsidis I, Orihuela-Espina Fet 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.

Journal article

Casillas-Figueroa R, Morán AL, Meza-Kubo V, Ramírez-Fernández C, Acosta-Quiroz C, Orihuela-Espina F, Montero-Hernandez Set 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.

Journal article

Rivas JJ, Orihuela-Espina F, Palafox L, Berthouze N, Lara MDC, Hernandez-Franco J, Sucar Eet 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.

Journal article

Zavriyev AI, Kaya K, Farzam P, Farzam PY, Sunwoo J, Orihuela-Espina F, Jassar AS, Cameron DE, Sundt TM, Melnitchouk S, Carp SA, Franceschini MA, Qu JZet 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.

Working paper

Sunwoo J, Nair V, Steele T, Lawrence N, Zavriyev A, Peruch A, Starkweather Z, Wu KC, Orihuela-Espina F, Inder T, Franceschini MA, El-Dib Met 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.

Conference paper

Joel Rivas J, Orihuela-Espina F, Enrique Sucar L, Williams A, Bianchi-Berthouze Net 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.

Conference paper

Modi HN, Singh H, Fiorentino F, Orihuela-Espina F, Athanasiou T, Yang G-Z, Darzi A, Leff DRet 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

Journal article

Wu K, Farzam P, Sheriff F, Farzam PY, Monk AD, Aziz-Sultan MA, Patel N, Orihuela-Espina F, Vaitkevicius H, Franceschini MAet 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

Conference paper

Modi H, Singh H, Fiorentino F, Orihuela-Espina F, Athanasiou T, Yang G-Z, Darzi A, Leff Det 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

Journal article

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.

Conference paper

Alejandro Hernandez-Contreras D, Peregrina-Barreto H, De Jesus Rangel-Magdaleno J, Orihuela-Espina Fet 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.

Journal article

Lami M, Singh H, Dilley JH, Ashraf H, Edmondon M, Orihuela-Espina F, Hoare J, Darzi A, Sodergren MHet 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.

Journal article

Montero-Hernandez S, Orihuela-Espina F, Enrique Sucar L, Pinti P, Hamilton A, Burgess P, Tachtsidis Iet al., 2018, Estimating functional connectivity symmetry between oxy- and deoxy-haemoglobin: implications for fNIRS connectivity analysis, Algorithms, Vol: 11, ISSN: 1999-4893

Functional Near InfraRed Spectroscopy (fNIRS) connectivity analysis is often performed using the measured oxy-haemoglobin (HbO2) signal, while the deoxy-haemoglobin (HHb) is largely ignored. The in-common information of the connectivity networks of both HbO2 and HHb is not regularly reported, or worse, assumed to be similar. Here we describe a methodology that allows the estimation of the symmetry between the functional connectivity (FC) networks of HbO2 and HHb and propose a differential symmetry index (DSI) indicative of the in-common physiological information. Our hypothesis is that the symmetry between FC networks associated with HbO2 and HHb is above what should be expected from random networks. FC analysis was done in fNIRS data collected from six freely-moving healthy volunteers over 16 locations on the prefrontal cortex during a real-world task in an out-of-the-lab environment. In addition, systemic data including breathing rate (BR) and heart rate (HR) were also synchronously collected and used within the FC analysis. FC networks for HbO2 and HHb were established independently using a Bayesian networks analysis. The DSI between both haemoglobin (Hb) networks with and without systemic influence was calculated. The relationship between the symmetry of HbO2 and HHb networks, including the segregational and integrational characteristics of the networks (modularity and global efficiency respectively) were further described. Consideration of systemic information increases the path lengths of the connectivity networks by 3%. Sparse networks exhibited higher asymmetry than dense networks. Importantly, our experimental connectivity networks symmetry between HbO2 and HHb departs from random (t-test: t(509) = 26.39, p < 0.0001). The DSI distribution suggests a threshold of 0.2 to decide whether both HbO2 and HHb FC networks ought to be studied. For sparse FC networks, analysis of both haemoglobin species is strongly recommended. Our DSI can provide a quantifiable g

Journal article

Herrera-Vega J, Orihuela-Espina F, Ibarguengoytia PH, Garcia UA, Rosado D-EV, Morales EF, Enrique Sucar Let al., 2018, A local multiscale probabilistic graphical model for data validation and reconstruction, and its application in industry, Engineering Applications of Artificial Intelligence, Vol: 70, Pages: 1-15, ISSN: 0952-1976

The detection and subsequent reconstruction of incongruent data in time series by means of observation of statistically related information is a recurrent issue in data validation. Unlike outliers, incongruent observations are not necessarily confined to the extremes of the data distribution. Instead, these rogue observations are unlikely values in the light of statistically related information. This paper proposes a multiresolution Bayesian network model for the detection of rogue values and posterior reconstruction of the erroneous sample for non-stationary time-series. Our method builds local Bayesian Network models that best fit to segments of data in order to achieve a finer discretization and hence improve data reconstruction. Our local multiscale approach is compared against its single-scale global predecessor (assumed as our gold standard) in the predictive power and of this, both error detection capabilities and error reconstruction capabilities are assessed. This parameterization and verification of the model are evaluated over three synthetic data source topologies. The virtues of the algorithm are then further tested in real data from the steel industry where the aforementioned problem characteristics are met but for which the ground truth is unknown. The proposed local multiscale approach was found to dealt better with increasing complexities in data topologies.

Journal article

Orihuela-Espina F, Sucar LE, 2018, Adaptation and customization in virtual rehabilitation, Virtual and Augmented Reality: Concepts, Methodologies, Tools, and Applications, Pages: 826-849, ISBN: 9781522554691

Background. Adaptation and customization are two related but distinct concepts that are central to virtual rehabilitation if this motor therapy modality is to succeed in alleviating the demand for expert supervision. These two elements of the therapy are required to exploit the flexibility of virtual environments to enhance motor training and boost therapy outcome. Aim. The chapter provides a non-systematic overview of the state of the art regarding the evolving manipulation of virtual rehabilitation environments to optimize therapy outcome manifested through customization and adaptation mechanisms. Methods. Both concepts will be defined, aspects guiding their implementation reviewed, and available literature suggesting different solutions discussed. We present "Gesture Therapy", a platform realizing our contributions to the field and we present results of the adaptation techniques integrated into it. Less explored additional dimensions such as liability and privacy issues affecting their implementation are briefly discussed. Results. Solutions to implement decision-making on how to manipulate the environment are varied. They range from predefined system configurations to sophisticated artificial intelligence (AI) models. Challenge maintenance and feedback personalization is the most common driving force for their incorporation to virtual rehabilitation platforms. Conclusions. Customization and adaptation are the main mechanisms responsible for the full exploitation of the potential of virtual rehabilitation environments, and the potential benefits are worth pursuing. Despite encouraging evidence of the many solutions proposed thus far in literature, none has yet proven to substantially alter the therapy outcome. In consequence, research is still on going to equip virtual rehabilitation solutions with efficacious tailoring elements.

Book chapter

Soto-Perez de Celis E, Abraham Baez-Bagattela J, Lira-Huerta E, Herrera de la Luz A, Parra-Cabrera S, Orihuela-Espina F, Perez de Celis-Herrero MDLCet al., 2018, Sensor-based mobile system for the promotion and real-time monitoring of physical activity, Salud Pública de Mexico, Vol: 60, Pages: 119-120, ISSN: 0036-3634

Journal article

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