Eduardo José S. da Luz, William Robson Schwartz, Guillermo Cámara-Chávez, and David Menotti. 2016. “
ECG-based heartbeat classification for arrhythmia detection: A survey.” Computer Methods and Programs in Biomedicine, 127, Pp. 144-164.
Publisher's VersionAbstractAn electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing the electrical signal of each heartbeat, i.e., the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. In addition, we describe some of the databases used for evaluation of methods indicated by a well-known standard developed by the Association for the Advancement of Medical Instrumentation (AAMI) and described in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges, and also we propose an evaluation process workflow to guide authors in future works.
G. Garcia, G. Moreira, E. Luz, and D. Menotti. 2016. “
Improving automatic cardiac arrhythmia classification: Joining temporal-VCG, complex networks and SVM classifier.” In 2016 International Joint Conference on Neural Networks (IJCNN), Pp. 3896-3900.
Publisher's VersionAbstractThe classification of heartbeats using electrocardiogram (ECG) aiming arrhythmia detection is a well researched subject and still there are room for improvements concerning the recommended databases. In this sense, aiming to classify heartbeats for arrhythmia detection, we extend a previous ours proposal that uses vectorcardiogram, a bi-dimensional representation of two ECG leads, by incorporating the time component producing a three-dimensional representation, the temporal vectorcardiogram. Along with the new representation, also we apply complex networks to extract features from the temporal VCG. The new proposed features feed then a Support Vector Machines (SVM) classifier. The temporal VCG have increased in the global accuracy, and have better results classifying the N and S classes, when it is compared with the best result to our previous work with VCG. We conclude that new techniques to extract 3D features from the Temporal VCG could be an interesting research direction.
D. Xavier, G. Moreira, E. Luz, and M. J. F. Souza. 2016. “
A new irregular spatial cluster detection through multi-objective particle swarm optimization.” In 2016 IEEE Congress on Evolutionary Computation (CEC), Pp. 403-407.
Publisher's VersionAbstractMethods aiming detection and inference of spatial clusters are of great relevance. Both for its applicability to public health problems, as well as for the effective scientific interest in the development of these methods. The main techniques are based on spatial scan statistics and many applications link this statistic in efficient optimization methods. Recently, regularity functions have been proposed to control the excessive freedom of the shape of spatial clusters whenever the spatial scan statistic is used. We present a novel scan statistic algorithm employing a function based on the geographical dispersion to penalize the presence of a huge gap between the areas in candidate clusters, the dispersion function. The proposed multi-objective scan maximize the spatial scan and minimize the dispersion function, using particle swarm optimization. We show that, compared to the non-connectivity regularity function, the multi-objective scan with dispersion function is faster and suited for the detection of moderately irregularly shaped clusters. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. We show that, the multi-objective approach with the dispersion function is a promising algorithm for the detection of irregularly shaped clusters.
G. Moreira, E. Luz, and D. Menotti. 2016. “
Optimizing acceptance frontier using PSO and GA for multiple signature iris recognition.” In 2016 IEEE Congress on Evolutionary Computation (CEC), Pp. 4592-4598.
Publisher's VersionAbstractIn the last three decades, the eye iris has been investigated as the most unique phenotype feature in the biometric literature. In the iris recognition literature, works achieving outstanding accuracies in very well behavior environments, i.e., when the subject's eye is in a well lightened environment and at a fixed distance for image acquisition, are known since a decade. Nonetheless, in noncooperative environments, where the image acquisition aiming iris location/segmentation is not straightforward, the iris recognition problem has many open issues and has been well researched in recent works. A promising strategy in this context employs a classification approach using multiple signature extraction. Representations are extracted from overlapping and different parts of the iris region. Aiming a robust and noisy invariant classification, an increasing set of acceptance thresholds (frontier) are required for dealing with multiple signatures for iris recognition in the iris matching process. Usually this frontier is estimated using brute force algorithms and a specific step resolution playing an important trade-off between runtime and accuracy of recognition in terms of false acceptance and false rejection rates, measured using half total error rate (HTER). In this sense, this work aims to use Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for finding such frontier. Moreover, we also employ a robust feature extraction technique proposed by us in a previous work. The experiments showed that the use of these evolutionary algorithms provides similar, if not better, effectiveness in very little runtime in a complex database well-known in the literature. Furthermore, it is shown that the frontier obtained by PSO is more stable than the one obtained by GA.