Presentaciones a Congresos

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Two-dimensional posture evaluation in Parkinson’s disease: effect of loads on the spinal angle during gait
(2016) Celoria, Paula; Nanni, Federico; Pastore, Flavia Carina; Pulenta, Sebastián; Tajerian, Matías Nazareth; Pantazis, Lucio; Moscoso-Vásquez, Marcela; Cerquetti, Daniel; Merello, Marcelo; Risk, Marcelo
"Parkinson’s Disease patients present diminished coordination caused by neural degeneration. This leads to large motor difficulties during gait such as balance loss and pronounced forward inclination of the upper body. This work assessed the spinal sagittal plane angle alterations in two groups: six parkinsonian patients and six control healthy subjects. This parameter was analyzed during gait under three conditions: without external loads and with external loads applied either on the chest or on the lower back area. Results were statistically compared by means of t-test of paired samples in both groups. For patients, a significant effect was found when loads were applied on the chest. On the other hand, healthy subjects showed no significant differences in either case."
Time Domain Estimation of Arterial Parameters using the Windkessel Model and the Monte Carlo Method
(IOP Publishing Ltd, 2016) Gostuski, Vladimir; Pastore, Ignacio; Rodriguez Palacios, Gaspar; Vaca Diez, Gustavo; Moscoso-Vasquez, Marcela; Risk, Marcelo
"Numerous parameter estimation techniques exist for characterizing the arterial system using electrical circuit analogs. However, they are often limited by their requirements and usually high computational burdain. Therefore, a new method for estimating arterial parameters based on Monte Carlo simulation is proposed. A three element Windkessel model was used to represent the arterial system. The approach was to reduce the error between the calculated and physiological aortic pressure by randomly generating arterial parameter values, while keeping constant the arterial resistance. This last value was obtained for each subject using the arterial flow, and was a necessary consideration in order to obtain a unique set of values for the arterial compliance and peripheral resistance. The estimation technique was applied to in vivo data containing steady beats in mongrel dogs, and it reliably estimated Windkessel arterial parameters. Further, this method appears to be computationally efficient for on-line time-domain estimation of these parameters."
Is needle biopsy clinically useful in preoperative grading of central chondrosarcoma of the pelvis and long bones?
(2017) Roitman, Pablo D.; Farfalli, Germán L.; Ayerza, Miguel A.; Múscolo, D. Luis; Milano, Federico E.; Aponte-Tinao, Luis A.
"Central chondrosarcoma of bone is graded on a scale of 1 to 3 according to histological criteria. Clinically, these tumors can be divided into low-grade (Grade 1) and high-grade (Grade 2, Grade 3, and dedifferentiated) chondrosarcomas. Although en bloc resection has been the most widely used treatment, it has become generally accepted that in selected patients with low-grade chondrosarcomas of long bones, curettage is safe and effective. This approach requires an accurate preoperative estimation of grade to avoid under- or overtreatment, but prior reports have indicated that both imaging and biopsy do not always give an accurate prediction of grade."
What is the expected learning curve in computer-assisted navigation for bone tumor resection?
(2017) Farfalli, Germán L.; Albergo, José I.; Ritacco, Lucas E.; Ayerza, Miguel A.; Milano, Federico E.; Aponte-Tinao, Luis A.
"Background Computer navigation during surgery can help oncologic surgeons perform more accurate resections. However, some navigation studies suggest that this tool may result in unique intraoperative problems and increased surgical time. The degree to which these problems might diminish with experience–the learning curve–has not, to our knowledge, been evaluated for navigation-assisted tumor resections. Questions/purposes (1) What intraoperative technical problems were observed during the first 2 years using navigation? (2) What was the mean time for navigation procedures and the time improvement during the learning curve? (3) Have there been any differences in the accuracy of the registration technique that occurred over time? (4) Did navigation achieve the goal of achieving a wide bone margin? Methods All patients who underwent preoperative virtual planning for tumor bone resections and operated on with navigation assistance from 2010 to 2012 were prospectively collected. Two surgeons (GLF, LAA-T) performed the intraoperative navigation assistance. Both surgeons had more than 5 years of experience in orthopaedic oncology with more than 60 oncology cases per year per surgeon. This study includes from the very first patients performed with navigation. Although they did not take any formal training in orthopaedic oncology navigation, both surgeons were trained in navigation for knee prostheses. Between 2010 and 2012, we performed 124 bone tumor resections; of these, 78 (63%) cases were resected using intraoperative navigation assistance. During this period, our general indications for use of navigation included pelvic and sacral tumors and those tumors that were reconstructed with massive bone allografts to obtain precise matching of the host and allograft osteotomies. Seventy-eight patients treated with this technology were included in the study. Technical problems (crashes) and time for the navigation procedure were reported after surgery. Accuracy of the registration technique was defined and the surgical margins of the removed specimen were determined by an experienced bone pathologist after the surgical procedure as intralesional, marginal, or wide margins. To obtain these data, we performed a chart review and review of operative notes. Results In four patients (of 78 [5%]), the navigation was not completed as a result of technical problems; all occurred during the first 20 cases of the utilization of this technology. The mean time for navigation procedures during the operation was 31 minutes (range, 11–61 minutes), and the early navigations took more time (the regression analysis shielded R2 = 0.35 with p\0.001). The median registration error was 0.6 mm (range, 0.3–1.1 mm). Registration did not improve over time (the regression analysis slope estimate is 0.014, with R2 = 0.026 and p = 0.15). Histological examinations of all specimens showed a wide bone tumor margin in all patients. However, soft tissue margins were wide in 58 cases and marginal in 20. Conclusions We conclude that navigation may be useful in achieving negative bony margins, but we cannot state that it is more effective than other means for achieving this goal. Technical difficulty precluded the use of navigation in 5% of cases in this series. Navigation time decreased with more experience in the procedure but with the numbers available, we did not improve the registration error over time. Given these observations and the increased time and expense of using navigation, larger studies are needed to substantiate the value of this technology for routine use. Level of Evidence Level IV, therapeutic study."
Impact of participatory design for drug-drug interaction alerts: a comparison study between two interfaces
(2017) Luna, Daniel; Otero, Carlos; Risk, Marcelo; Stanziola, Enrique; González Bernaldo de Quirós, Fernán
"Decision support systems for alert drug-drug interactions have been shown as valid strategy to reduce medical error. Even so the use of these systems has not been as expected, probably due to the lack of a suitable design. This study compares two interfaces, one of them developed using participatory design techniques (based on user centered design processes). This work showed that the use of these techniques improves satisfaction, effectiveness and efficiency in an alert system for drug-drug interactions, a fact that was evident in specific situations such as the decrease of errors to meet the specified task, the time, the workload optimization and users overall satisfaction with the system."
User-centered design improves the usability of drug-drug interaction alerts: a validation study in the real scenario
(2017-08) Luna, Daniel; Rizzato Lede, Daniel; Rubin, Luciana; Otero, Carlos; Ortiz, Juan M.; García, Mónica G.; Rapisarda, Romina P.; Risk, Marcelo; González Bernaldo de Quirós, Fernán
"Decision support systems can alert physicians to the existence of drug interactions. The Hospital Italiano de Buenos Aires, Argentina, has an in-house electronic health record with computerized physician order entry and clinical decision support. It includes a drug-drug interaction alert system, initially developed under traditional engineering techniques. As we detected a high alert override rate, we rebuilt the knowledge database and redesigned the alert interface with User-Centered Design techniques. A laboratory crossover study using clinical vignettes showed that new alerts were more usable than traditional ones. This paper aimed to validate these results through a controlled and randomized experimental study with two branches (old vs. new design) in a real setting. We analyzed, quantitatively, every fired alert between April 2015 and September 2016. Finally, we performed user surveys and qualitative interviews to inquire about their satisfaction and perceptions. In real scenarios, user-centered design alerts were more usable, being more effective and satisfactory, but less efficient than traditional alerts. "Safe omission", as a new concept, emerged from our stratified analyses and interviews."
Comparative study of robust methods for motor imagery classification based on CSP and LDA
(2017-10) Villar, Ana Julia
" Common spatial patterns analysis and linear discriminant analysis are popular algorithms for spatial filtering and classifying in motor imagery. These algorithms are very sensitive to noise and artifacts which affect the classification accuracy. To deal with this issue, it is proposed to replace the usual estimators of covariance and scale used in the algorithms for robust versions. The performance of the methods are evaluated and compared on EGG data from BCI competition data sets; results show that robust methods outperformed classical techniques for subjects with poor classification accuracy. "
A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
(2017-10) Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Batatia, Hadj; Risk, Marcelo
"This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences between Seizure/Non-Seizure in patients suffering from epilepsy. Precisely, EEG signals are transformed into multivariate coefficients through multilevel 1D wavelet decomposition of different brain frequencies. The generalized Gaussian distribution (GGD) is shown to model precisely these coefficients. Patients are compared based on the analytical development of Kullback-Leibler divergence (KLD) of their corresponding GGD distributions. The method has been applied to a dataset of 18 epileptic signals of 9 patients. Results show a clear discrepancy between Seizure/Non-Seizure in epileptic signals, which helps in determining the onset of the seizure."
Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier
(2017-12) Quintero-Rincón, Antonio; Prendes, Jorge; D'Giano, Carlos; Muro, Valeria
"Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, in particular the early detection of epileptic seizures. In this paper we propose a k-nearest neighbors classification for epileptic EEG signals based on an t-location-scale statistical representation to detect spike-and-waves. The proposed approach is demonstrated on a real dataset containing both spike-and-wave events and normal brain function signals, where our performance is evaluated in terms of classification accuracy, sensitivity and specificity."
Epilepsy seizure onset detection applying 1-NN classifier based on statistical parameters
(2018) Zorgno, Ivanna; Blanc, María Cecilia; Oxenford, Simón; Gil Garbagnoli, Francisco; D'Giano, Carlos; Quintero-Rincón, Antonio
"Epilepsy is a disease caused by an excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an ongoing challenge in biomedical signal processing. In this paper, a new method is proposed for onset seizure detection in epileptic EEG signals based on parameters from the t-location-scale distribution coupled with the variance and the Pearson correlation coefficient. The 1-nearest neighbor classifier achieved a 91% sensitivity (True positive rate) and 95% specificity (True Negative Rate) with a delay of 4.5 seconds (on average) in the 45 signals analyzed, which suggests that the proposed methodology is potentially useful for seizure onset detection in epileptic EEG signals."
Autonomic modulation during a cognitive task using a wearable device
(2019) Bonomini, Maria Paula; Val-Calvo, Mikel; Díaz-Morcillo, Alejandro; Ferrández Vicente, José Manuel; Fernández-Jover, Eduardo
"Heart-brain interaction is by nature bidirectional, and then, it is sensible to expect the heart, via the autonomic nervous system (ANS), to induce changes in the brain. Respiration can originate differentiated ANS states reflected by HRV. In this work, we measured the changes in performance during a cognitive task due to four autonomic states originated by breath control: at normal breathing (NB), fast breathing (FB), slow breathing (SB) and control phases. ANS states were characterized by temporal (SDNN) and spectral (LF and HF power) HRV markers. Cognitive performance was measured by the response time (RT) and the success rate (SR). HRV parameters were acquired with the wristband Empatica E4. Classification was accomplished, firstly, to find the best ANS variables that discriminated the breathing phases (BPH) and secondly, to find whether ANS parameters were associated to changes in RT and SR. In order to compensate for possible bias of the test sets, 1000 classification iterations were run. The ANS parameters that better separated the four BPH were LF and HF power, with changes about 300% from controls and an average classification rate of 59.9%, a 34.9% more than random. LF and HF explained RT separation for every BPH pair, and so was HF for SR separation. The best RT classification was 63.88% at NB vs SB phases, while SR provided a 73.39% at SB vs NB phases. Results suggest that breath control could show a relation with the efficiency of certain cognitive tasks. For this goal the Empatica wristband together with the proposed methodology could help to clarify this hypothesis."
Automatic detection of reverse‑triggering related asynchronies during mechanical ventilation in ARDS patients using flow and pressure signals
(2019) Rodríguez, Pablo Oscar; Tiribelli, Norberto; Gogniat, Emiliano; Plotnikow, Gustavo A.; Fredes, Sebastián; Fernández Ceballos, Ignacio; Pratto, Romina A.; Madorno, Matías; Ilutovich, Santiago; San Román, Eduardo; Bonelli, Ignacio; Guaymas, María; Raimondi, Alejandro C.; Maskin, Luis Patricio; Setten, Mariano
"Asynchrony due to reverse-triggering (RT) may appear in ARDS patients. The objective of this study is to validate an algo-rithm developed to detect these alterations in patient–ventilator interaction. We developed an algorithm that uses flow and airway pressure signals to classify breaths as normal, RT with or without breath stacking (BS) and patient initiated double-triggering (DT). The diagnostic performance of the algorithm was validated using two datasets of breaths, that are classified as stated above. The first dataset classification was based on visual inspection of esophageal pressure (Pes) signal from 699 breaths recorded from 11 ARDS patients. The other classification was obtained by vote of a group of 7 experts (2 physicians and 5 respiratory therapists, who were trained in ICU), who evaluated 1881 breaths gathered from recordings from 99 sub-jects. Experts used airway pressure and flow signals for breaths classification. The RT with or without BS represented 19% and 37% of breaths in Pes dataset while their frequency in the expert’s dataset were 3% and 12%, respectively. The DT was very infrequent in both datasets. Algorithm classification accuracy was 0.92 (95% CI 0.89–0.94, P < 0.001) and 0.96 (95% CI 0.95–0.97, P < 0.001), in comparison with Pes and experts’ opinion. Kappa statistics were 0.86 and 0.84, respectively. The algorithm precision, sensitivity and specificity for individual asynchronies were excellent. The algorithm yields an excellent accuracy for detecting clinically relevant asynchronies related to RT."

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