Marcelo Risk

Angle estimation of human femora in a three-dimensional virtual environment

The estimation of human femur morphology and angulation provide useful information for assisted surgery, follow-up evaluation and prosthesis design, cerebral palsy management, congenital dislocation of the hip and fractures of the femur. Conventional methods that estimate femoral neck anteversion employ planar projections because accurate 3D estimations require complex reconstruction routines. In a recent work, we proposed a cylinder fitting method to estimate bifurcation angles in coronary arteries and we thought to test it in the estimation of femoral neck anteversion, valgus and shaft-neck angles. Femora from 10 patients were scanned using multisliced computed tomography. Virtual cylinders were fitted to 3 regions of the bone painted by the user to automatically estimate the femoral angles. Comparisons were made with a conventional manual method. Inter- and intra-reading measurements were evaluated for each method. We found femoral angles from both methods strongly correlated. Average anteversion, neck-shaft and valgus angles were 17.5°, 139.5°, 99.1°, respectively. The repeatability and reproducibility of the automated method showed a 5-fold reduction in inter- and intra-reading variability. Accordingly, the coefficients of variation for the manual method were below 25% whereas for the automated method were below 6%. The valgus angle assessment was globally the most accurate with differences below 1°. Maximum distances from true surface bone points and fitting cylinders attained 6 mm. The employment of virtual cylinders fitted to different regions of human femora consistently helped to assess true 3D angulations.

A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.

Transfer accuracy and precision scoring in planar bone cutting validated with ex vivo data

The use of interactive surgical scenarios for virtual preoperative planning of osteotomies has increased in the last 5 years. As it has been reported by several authors, this technology has been used in tumor resection osteotomies, knee osteotomies, and spine surgery with good results. A digital three‐dimensional preoperative plan makes possible to quantitatively evaluate the transfer process from the virtual plan to the anatomy of the patient. We introduce an exact definition of accuracy and precision of this transfer process for planar bone cutting. We present a method to compute these properties from ex vivo data. We also propose a clinical score to assess the goodness of a cut. A computer simulation is used to characterize the definitions and the data generated by the measurement method. The definitions and method are evaluated in 17 ex vivo planar cuts of tumor resection osteotomies. The results show that the proposed method and definitions are highly correlated with a previous definition of accuracy based in ISO 1101. The score is also evaluated by showing that it distinguishes among different transfer techniques based in its distribution location and shape. The introduced definitions produce acceptable results in cases where the ISO‐based definition produce counter intuitive results.

An algorithm for automatic surface labeling of planar surgical resections

Three dimensional (3D) preoperative planning and navigation in bone tumor resections have been used in the last five years with good results. The purpose of this study is to develop a method capable of detecting and labeling the nearly planar surface generated by the cutting saw in the surgical specimen taken off the patient during the resection procedure. This surface area labeling is fundamental to track the path that the cutting saw took during the surgery and compare it to the planned cutting plane. The algorithm presented here works by using a 3D reconstruction of the surgical specimen computed tomography (CT) scan, registered against the 3D reconstruction of the preoperative patient CT scan, and the cutting plane defined during surgical planning. The results show a high labeling accuracy (a matching mean of 98.5%) and a non significant accuracy variation for a range of distance and angle offsets.

Multivariate Bayesian classification of epilepsy EEG signals

The classification of epileptic seizure events in EEG signals is an important problem in biomedical engineering. In this paper we propose a Bayesian classification method for multivariate EEG signals. The method is based on a multilevel 2D wavelet decomposition that captures the distribution of energy across the different brain rhythms and regions, coupled with a generalised Gaussian statistical representation and a multivariate Bayesian classification scheme. The proposed approach is demonstrated on a challenging paediatric dataset containing both epileptic events and normal brain function signals, where it outperforms a state-of-the-art method both in terms of classification sensitivity and specificity.

A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence

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.

Electropore Formation in Mechanically Constrained Phospholipid Bilayers

Molecular dynamics simulations of lipid bilayers in aqueous systems reveal how an applied electric field stabilizes the reorganization of the water–membrane interface into water-filled, membrane-spanning, conductive pores with a symmetric, toroidal geometry. The pore formation process and the resulting symmetric structures are consistent with other mathematical approaches such as continuum models formulated to describe the electroporation process. Some experimental data suggest, however, that the shape of lipid electropores in living cell membranes may be asymmetric. We describe here the axially asymmetric pores that form when mechanical constraints are applied to selected phospholipid atoms. Electropore formation proceeds even with severe constraints in place, but pore shape and pore formation time are affected. Since lateral and transverse movement of phospholipids may be restricted in cell membranes by covalent attachments to or non-covalent associations with other components of the membrane or to membrane-proximate intracellular or extracellular biomolecular assemblies, these lipid-constrained molecular models point the way to more realistic representations of cell membranes in electric fields.

Predicción de crisis epilépticas utilizando el coeficiente de correlación producto-momento de Pearson a partir de un clasificador lineal de la distribución Gaussiana generalizada

To predict an epileptic event means the ability to determine in advance the time of the seizure with the highest possible accuracy. A correct prediction benchmark for epilepsy events in clinical applications is a typical problem in biomedical signal processing that helps to an appropriate diagnosis and treatment of this disease. In this work, we use Pearsons product-moment correlation coefficient from generalized Gaussian distribution parameters coupled with a linear-based classifier to predict between seizure and non-seizure events in epileptic EEG signals. The performance in 36 epileptic events from 9 patients showing good performance with 100% of effectiveness for sensitivity and specificity greater than 83% for seizures events in all brain rhythms. Pearsons test suggests that all brain rhythms are highly correlated in non-seizure events but no during the seizure events. This suggests that our model can be scaled with the Pearsons product-moment correlation coefficient for the detection of epileptic seizures.Resumen Predecir una crisis epileptica significa la capacidad de determinar de antemano el momento de una crisis con la mayor precision posible. Un pronostico correcto de un evento epileptico en aplicaciones clinicas es un problema tipico en procesamiento de senales biomedicas, lo cual ayuda a un diagnostico y tratamiento apropiado de esta enfermedad. En este trabajo, utilizamos el coeficiente de correlacion producto-momento de Pearson a partir de las clases estimadas con un clasificador lineal, usando los parametros de la distribucion Gaussiana generalizada. Esto con el fin de poder pronosticar eventos con crisis y eventos con no-crisis en senales epilepticas. El desempeno en 36 eventos epilepticos de 9 pacientes muestra un buen rendimiento, con un 100% de efectividad para sensibilidad y especificidad superior al 83% para eventos con crisis en todos los ritmos cerebrales. El test de Pearson indica que todos los ritmos cerebrales estan altamente correlacionados en los eventos con no-crisis, mas no durante los eventos con crisis. Esto indica que nuestro modelo puede escalarse con el coeficiente de correlacion producto-momento de Pearson para la deteccion de crisis en senales epilepticas.

Two-dimensional posture evaluation in Parkinson’s disease: effect of loads on the spinal angle during gait.

Parkinsons 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

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.