Carlos Arizmendi

Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks

The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is Magnetic Resonance Spectroscopy. In this task, radiology experts are likely to benefit from the support of computer-based systems built around robust classification processes. In this paper, a Discrete Wavelet Transform procedure was applied to the pre-processing of spectra corresponding to several brain tumour pathologies. This procedure does not alleviate the high dimensionality of the data by itself. For this reason, dimensionality reduction was subsequently implemented using Moving Window with Variance Analysis for feature selection or Principal Component Analysis for feature extraction. The combined method yielded very encouraging results in terms of diagnostic discriminatory binary classification using Bayesian Neural Networks. In most cases, the classification accuracy improved on previously reported results.

Support Vector Machine Classification Applied on Weaning Trials Patients

One of the most frequent reasons for instituting mechanical ventilation is to decrease patients work of breathing. Many attempts have been made to increase the effectiveness of the evaluation of the respiratory pattern with the analysis of the respiratory signals. This work proposes a method for the study of the differences in respiratory pattern variability in patients on weaning trials. The proposed method is based on a support vector machine using 35 features extracted from the respiratory flow signal. In this paper, a group of 146 patients with mechanical ventilation were studied: group S of 79 patients with successful weaning trials and group F of 67 patients that failed to maintain spontaneous breathing and were reconnected. Applying a feature selection procedure based on the use of the support vector machine with a leave-one-out cross-validation, it was obtained 86.67% of well classified patients on group S and 73.34% on group F, using only 8 of the 35 features. Therefore, support vector machine can be a classification method of the respiratory pattern variability useful in the study of patients on weaning trials

Diagnosis of brain tumours from magnetic resonance spectroscopy using wavelets and Neural Networks

The diagnosis of human brain tumours from noninvasive signal measurements is a sensitive task that requires specialized expertise. In this task, radiology experts are likely to benefit from the support of computer-based systems built around robust classification processes. In this brief paper, a method that combines data pre-processing using wavelets with classification using Artificial Neural Networks is shown to yield high diagnostic classification accuracy for a broad range of brain tumour pathologies.

Patients on Weaning Trials from Mechanical Ventilation Classified with Neural Networks and Feature Selection

One of the challenges in intensive care is the process of weaning from mechanical ventilation. We studied the differences in respiratory pattern variability between patients capable of maintaining spontaneous breathing during weaning trials and patients that fail to maintain spontaneous breathing. In this work, neural networks were applied to study these differences. 64 patients from mechanical ventilation are studied: Group S with 32 patients with Successful trials and Group F with 32 patients that Failed to maintain spontaneous breathing and were reconnected. A performance of 64.56% of well classified patients was obtained using a neural network trained with the whole set of 35 features. After the application of a feature selection procedure (backward selection) 84.56% was obtained using only 8 of the 35 features

Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian neural networks

Neuro-oncologists must ultimately rely on their acquired knowledge and accumulated experience to undertake the sensitive task of brain tumour diagnosis. This task strongly depends on indirect, non-invasive measurements, which are the source of valuable data in the form of signals and images. Expert radiologists should benefit from their use as part of an at least partially automated computer-based medical decision support system. This paper focuses on Magnetic Resonance Spectroscopy signal analysis and illustrates a method that combines Gaussian Decomposition, dimensionality reduction by Moving Window with Variance Analysis and classification using adaptively regularized Artificial Neural Networks. The method yields encouraging results in the task of binary classification of human brain tumours, even for tumour types that have seldom been analyzed from this viewpoint.

Brain tumour classification using Gaussian decomposition and neural networks

The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN). This combination of techniques is shown to yield high diagnostic classification accuracy in problems concerning diverse brain tumour pathologies, some of which have received little attention in the literature.

Binary classification of brain tumours using a Discrete Wavelet Transform and energy criteria

The accurate diagnosis of human brain tumours is a sensitive medical task, for which radiology experts often must rely on indirect signal measurements. There is thus a need for developing computer-based decision support tools to assist doctors in their diagnostic task. The experiments in this brief paper address such problem in the form of binary classification, for which the pre-processing of the Magnetic Resonance Spectroscopy (MRS) signal is a most relevant data analysis stage. A combination of the Discrete Wavelet Transform (DWT) for signal decomposition and an energy criterion for signal reconstruction is used to pre-process the MRS data prior to the feature selection and classification with Bayesian Neural Networks.

Nonlinear dynamic analysis of the cardiorespiratory system in patients undergoing the weaning process

In this work, the cardiorespiratory pattern of patients undergoing extubation process is studied. First, the respiratory and cardiac signals were resampled, next the Symbolic Dynamics (SD) technique was implemented, followed of a dimensionality reduction applying Forward Selection (FS) and Moving Window with Variance Analysis (MWVA) methods. Finally, the Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) classifiers were used. The study analyzed 153 patients undergoing weaning process, classified into 3 groups: Successful Group (SG: 94 patients), Failed Group (FG: 39 patients), and patients who had been successful during the extubation and had to be reintubated before 48 hours, Reintubated Group (RG: 21 patients). According to the results, the best classification present an accuracy higher than 88.98 ± 0.013% in all proposed combinations.

Cardiorespiratory Interaction Using Nonlinear Data Processing Techniques in Patients Undergoing Test Tube T

The estimate of the optimal time to remove the ventilator from a patient in intensive care remains critical in clinical practice. This study analyzes the breathing pattern from cardiorespiratory signals extubation patients undergoing performing resampling the signal, then the Symbolic Dynamics technique for data processing is implemented, together with the techniques of Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) for classifying 154 patients conglomerates in the Group Success and Failure Group classification, obtaining the best result obtained from 93.87 ± 0.01 % prediction, for SVM .

Analysis of the respiratory flow signal for the diagnosis of patients with chronic heart failure using artificial intelligence techniques

Patients with Chronic Heart Failure (CHF) often develop oscillatory breathing patterns. This work proposes the characterization of respiratory pattern by Wavelet Transform (WT) technique to identify Periodic Breathing pattern (PB) and Non-Periodic Breathing pattern (nPB) through the respiratory flow signal. A total of 62 subjects were analyzed: 27 CHF patients and 35 healthy subjects. Respiratory time series were extracted, and statistical methods were applied to obtain the most relevant information to classify patients. Support Vector Machine (SVM) were applied using forward selection technique to discriminate patients, considering four kernel functions. Differences between these parameters are assessed by investigating the following four classification issues: healthy subjects versus CHF patients, PB versus nPB patients, PB patients versus healthy subjects, and nPB patients versus healthy subjects. The results are presented in terms of average accuracy for each kernel function, and comparison groups.