Miguel A. Becerra

Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection

This paper presents a dimensionality reduction study based on fuzzy rough sets with the aim of increasing the discriminant capability of the representation of normal ECG beats and those that contain ischemic events. A novel procedure is proposed to obtain the fuzzy equivalence classes based on entropy and neighborhood techniques and a modification of the Quick Reduct Algorithm is used to select the relevant features from a large feature space by a dependency function. The tests were carried out on a feature space made up by 840 wavelet features extracted from 900 ECG normal beats and 900 ECG beats with evidence of ischemia. Results of around 99% classification accuracy are obtained. This methodology provides a reduced feature space with low complexity and high representation capability. Additionally, the discriminant strength of entropy in terms of representing ischemic disorders from time-frequency information in ECG signals is highlighted.

Effect of the electrograms density in detecting and ablating the tip of the rotor during chronic atrial fibrillation: an in silico study

AIMS Identification in situ of arrhythmogenic mechanisms could improve the rate of ablation success in atrial fibrillation (AF). Our research group reported that rotors could be located through dynamic approximate entropy (DApEn) maps. However, it is unknown how much the spatial resolution of catheter electrodes could affect substrates localization. The present work looked for assessing the electrograms (EGMs) spatial resolution needed to locate the rotor tip using DApEn maps. METHODS AND RESULTS A stable rotor in a two-dimensional computational model of human atrial tissue was simulated using the Courtemanche electrophysiological model and implementing chronic AF features. The spatial resolution is 0.4 mm (150 × 150 EGM). Six different lower resolution arrays were obtained from the initial mesh. For each array, DApEn maps were constructed using the inverse distance weighting (IDW) algorithm. Three simple ablation patterns were applied. The full DApEn map detected the rotor tip and was able to follow the small meander of the tip through the shape of the area containing the tip. Inverse distance weighting was able to reconstruct DApEn maps after applying different spatial resolutions. These results show that spatial resolutions from 0.4 to 4 mm accurately detect the rotor tip position. An ablation line terminates the rotor only if it crosses the tip and ends at a tissue boundary. CONCLUSION A previous work has shown that DApEn maps successfully detected simulated rotor tips using a high spatial resolution. In this work, it was evinced that DApEn maps could be applied using a spatial resolution similar to that available in commercial catheters, by adding an interpolation stage. This is the first step to translate this tool into medical practice with a view to the detection of ablation targets.

Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition

The hearts mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs.

Low Data Fusion Framework Oriented to Information Quality for BCI Systems

The evaluation of the data/information fusion systems does not have standard quality criteria making the reuse and optimization of these systems a complex task. In this work, we propose a complete low data fusion (DF) framework based on the Joint Director of Laboratories (JDL) model, which considers contextual information alongside information quality (IQ) and performance evaluation system to optimize the DF process according to the user requirements. A set of IQ criteria was proposed by level. The model was tested with a brain-computer interface (BCI) system multi-environment to prove its functionality. The first level makes the selection and preprocessing of electroencephalographic signals. In level one feature extraction is carried out using discrete wavelet transform (DWT), nonlinear and linear statistical measures, and Fuzzy Rough Set – FRS algorithm for selecting the relevant features; finally, in the same level a classification process was conducted using support vector machine – SVM. A Fuzzy Inference system is used for controlling different processes based on the results given by an IQ evaluation system, which applies quality measures that can be weighted by the users of the system according to their requirements. Besides, the system is optimized based on the results given by the cuckoo search algorithm, which uses the IQ traceability for maximizing the IQ criteria according to user requirements. The test was carried out with different type and levels of noise applied to the signals. The results showed the capability and functionality of the model.

Analysis of Motor Imaginary BCI Within Multi-environment Scenarios Using a Mixture of Classifiers

Brain-computer interface (BCI) is a system that provides communication between human beings and machines through an analysis of human brain neural activity. Several studies on BCI systems have been carried out in controlled environments, however, a functional BCI should be able to achieve an adequate performance in real environments. This paper presents a comparative study on alternative classification options to analyze motor imaginary BCI within multi-environment real scenarios based on mixtures of classifiers. The proposed methodology is as follows: The imaginary movement detection is carried out by means of feature extraction and classification, in the first stage; feature set is obtained from wavelet transform, empirical mode decomposition, entropy, variance and rates between minimum and maximum, in the second stage, where several classifier combinations are applied. The system is validated using a database, which was constructed using the Emotiv Epoc+ with 14 channels of electroencephalography (EEG) signals. These were acquired from three subject in 3 different environments with the presence and absence of disturbances. According to the different effects of the disturbances analyzed in the three environments, the performance of the mixture of classifiers presented better results when compared to the individual classifiers, making it possible to provide guidelines for choosing the appropriate classification algorithm to incorporate into a BCI system.

Developments on Solutions of the Normalized-Cut-Clustering Problem Without Eigenvectors

Normalized-cut clustering (NCC) is a benchmark graph-based approach for unsupervised data analysis. Since its traditional formulation is a quadratic form subject to orthogonality conditions, it is often solved within an eigenvector-based framework. Nonetheless, in some cases the calculation of eigenvectors is prohibitive or unfeasible due to the involved computational cost – for instance, when dealing with high dimensional data. In this work, we present an overview of recent developments on approaches to solve the NCC problem with no requiring the calculation of eigenvectors. Particularly, heuristic-search and quadratic-formulation-based approaches are studied. Such approaches are elegantly deduced and explained, as well as simple ways to implement them are provided.

Reconstruction of Multi Spatial Resolution Feature Maps on a 2D Model of Atrial Fibrillation: Simulation Study

Catheter ablation is a technique used as treatment for atrial fibrillation, this procedure is guided using 3D electro anatomic mapping systems. Ablation is one of the treatments for AF whose effectiveness depend on the location of the rotor tip and this depend of the quality of mapping obtained from a re-duced set of real signals. This paper presents a comparison study between three approaches for reconstruction of features maps of a 2D model of simulated atrial fibrillation. The model was char-acterized using the mean, Shannon entropy and approximate entropy of the electrograms (EGM). The model is made up of 22500 EGM and reductions of 75%, 93.5% and 97.3% in the spatial resolution of the model was conducted. Thereupon a reconstruction of the feature maps was realized using inverse distance weighted (IDW), inverse distance weighted-median filter (IDW-MF) and backpropagation artificial neural networks (BPANN), the performance of the techniques was analyzed using the root mean square error (RMSE) and the peak signal to noise ratio (PSNR). IDW shows a general RSME of 4.2% and a PSNR of 27.5dB, IDW-MF exhibited a RSME of 17.5% and a PSNR of 21.8 dB, finally BPANN shows a RSME of 9% and PSNR of 22.8 dB. IDW shows the best performance at any degree of reduction while IDW-MF represent the best approach for Shannon entropy and mean maps reconstruction and IDW has the best perfor-mance for approximate entropy maps reconstruction.

Chloroquine effect on rotor termination under paroxysmal and chronic atrial fibrillation. 2D simulation study

Atrial fibrillation (AF) is the most common prevalent cardiac arrhythmia. If the paroxysmal AF (pAF) is not treated it could become chronic AF (cAF). Blockade of inward rectifying potassium (IK1) and acetylcholine-activated potassium (IKACh) currents by the antimalarial drug chloroquine could play a role as antiarrhythmic drug in human AF. We simulated the effects of chloroquine on human atrial tissue to study its effect on rotor termination, under pAF and cAF conditions. For this, we modified a human cell model to obtain pAF and cAF models and we developed a model of chloroquine effects on IK1 and IKACh· Rotors were generated in a 2D model of atrial tissue and different chloroquine concentrations were applied. Our results show that chloroquine blocks both currents, which results in action potential duration (APD) lengthening. Chloroquine has a greater effect on pAF conditions and high chloroquine concentration is needed to achieve similar effects in cAF. In pAF, was necessary 0.3 μM or more, in order to terminate the rotor, however, in cAF, was necessary a higher chloroquine concentration (0.5 μM). Our results suggest that the antimalarial drug chloroquine could be a potent antiarrhythmic agent in the treatment of pAF at concentrations from 0.3 μM and in the treatment of cAF at higher concentrations, from 0.5 μM.

Case-Based Reasoning Systems for Medical Applications with Improved Adaptation and Recovery Stages.

Case-Based Reasoning Systems (CBR) are in constant evolution, as a result, this article proposes improving the retrieve and adaption stages through a different approach. A series of experiments were made, divided in three sections: a proper pre-processing technique, a cascade classification, and a probability estimation procedure. Every stage offers an improvement, a better data representation, a more efficient classification, and a more precise probability estimation provided by a Support Vector Machine (SVM) estimator regarding more common approaches. Concluding, more complex techniques for classification and probability estimation are possible, improving CBR systems performance due to lower classification error in general cases.

Electroencephalographic Signals and Emotional States for Tactile Pleasantness Classification

Haptic textures are alterations of any surface that are perceived and identified using the sense of touch, and such perception affects individuals. Therefore, it has high interest in different applications such as multimedia, medicine, marketing, systems based on human-computer interface among others. Some studies have been carried out using electroencephalographic signals; nevertheless, this can be considered few. Therefore this is an open research field. In this study, an analysis of tactile stimuli and emotion effects was performed from EEG signals to identify pleasantness and unpleasantness sensations using classifier systems. The EEG signals were acquired using Emotiv Epoc+ of 14 channels following a protocol for presenting ten different tactile stimuli two times. Besides, three surveys (Becks depression, emotion test, and tactile stimuli pleasant level) were applied to three volunteers for establishing their emotional state, depression, anxiety and the pleasantness level to characterize each subject. Then, the results of the surveys were computed and the signals preprocessed. Besides, the registers were labeled as pleasant and unpleasant. Feature extraction was applied from Short Time Fourier Transform and discrete wavelet transform calculated to each sub-bands (\(\delta \), \(\theta \), \(\alpha \), \(\beta \), and \(\gamma \)) of EEG signals. Then, Rough Set algorithm was applied to identify the most relevant features. Also, this technique was employed to establish relations among stimuli and emotional states. Finally, five classifiers based on the support vector machine were tested using 10-fold cross-validation achieving results upper to 99% of accuracy. Also, dependences among emotions and pleasant and unpleasant tactile stimuli were identified.