PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档。目前包含多参数的心肺、神经和其他生物医学信号，尤以心电图（ECG）为主。信号来自健康受试者和各种疾病的患者。涉及的疾病包括心脏猝死、充血性心力衰竭、癫痫、步态不稳、睡眠呼吸暂停和衰老等。

PhysioBank

A PSO-adaptive KNN based gene selection method is proposed to select useful genes.A heuristic for selecting the optimal values of K efficiently is also proposed.The proposed technique is applied on SRBCT, ALL_AML and MLL microarray datasets.The usefulness of the identified genes is reconfirmed using SVM classifier.The method finds 6, 3 and 4 genes for SRBCT, ALL_AML, and MLL with high accuracy. These days, microarray gene expression data are playing an essential role in cancer classifications. However, due to the availability of small number of effective samples compared to the large number of genes in microarray data, many computational methods have failed to identify a small subset of important genes. Therefore, it is a challenging task to identify small number of disease-specific significant genes related for precise diagnosis of cancer sub classes. In this paper, particle swarm optimization (PSO) method along with adaptive K-nearest neighborhood (KNN) based gene selection technique are proposed to distinguish a small subset of useful genes that are sufficient for the desired classification purpose. A proper value of K would help to form the appropriate numbers of neighborhood to be explored and hence to classify the dataset accurately. Thus, a heuristic for selecting the optimal values of K efficiently, guided by the classification accuracy is also proposed. This proposed technique of finding minimum possible meaningful set of genes is applied on three benchmark microarray datasets, namely the small round blue cell tumor (SRBCT) data, the acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) data and the mixed-lineage leukemia (MLL) data. Results demonstrate the usefulness of the proposed method in terms of classification accuracy on blind test samples, number of informative genes and computing time. Further, the usefulness and universal characteristics of the identified genes are reconfirmed by using different classifiers, such as support vector machine (SVM).

Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique

In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of different statistical features were extracted from the obtained frequency bands to denote the distribution of wavelet coefficients. This study shows that RF classifier achieves superior performances compared to other decision tree methods using 10-fold cross-validation for the ECG datasets and the obtained results suggest that further significant improvements in terms of classification accuracy can be accomplished by the proposed classification system. Accurate ECG signal classification is the major requirement for detection of all arrhythmia types. Performances of the proposed system have been evaluated on two different databases, namely MIT-BIH database and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. For MIT-BIH database, RF classifier yielded an overall accuracy 99.33 % against 98.44 and 98.67 % for the C4.5 and CART classifiers, respectively. For St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, RF classifier yielded an overall accuracy 99.95 % against 99.80 % for both C4.5 and CART classifiers, respectively. The combined model with multiscale principal component analysis (MSPCA) de-noising, discrete wavelet transform (DWT) and RF classifier also achieves better performance with the area under the receiver operating characteristic (ROC) curve (AUC) and F-measure equal to 0.999 and 0.993 for MIT-BIH database and 1 and 0.999 for and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, respectively. Obtained results demonstrate that the proposed system has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).

Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier

A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias

https://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/1698_2014-Derrac-INS.pdf.pdf

Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects

Highlights? Hybrid intelligent system for arrhythmia classification. ? Combination of fuzzy KNN with neural networks with Mamdani fuzzy system. ? ECG signal transformation for improving classification results. In this paper we describe a hybrid intelligent system for classification of cardiac arrhythmias. The hybrid approach was tested with the ECG records of the MIT-BIH Arrhythmia Database. The samples considered for classification contained arrhythmias of the following types: LBBB, RBBB, PVC and Fusion Paced and Normal, as well as the normal heartbeats. The signals of the arrhythmias were segmented and transformed for improving the classification results. Three methods of classification were used: Fuzzy K-Nearest Neighbors, Multi Layer Perceptron with Gradient Descent and momentum Backpropagation, and Multi Layer Perceptron with Scaled Conjugate Gradient Backpropagation. Finally, a Mamdani type fuzzy inference system was used to combine the outputs of the individual classifiers, and a very high classification rate of 98% was achieved.

Hybrid intelligent system for cardiac arrhythmia classification with Fuzzy K-Nearest Neighbors and neural networks combined with a fuzzy system

This paper describes an approach using computational intelligence methods to form a hybrid model as a classification method for 2-lead cardiac arrhythmias. The hybridization of methods can increase the performance in a system and take advantage of the benefits offered by such techniques in solving complex problems. The interpretation of electrocardiograms is a useful task for physicians, but when it comes to reviewing more than 24 h of information, it becomes a laborious task for them. For this reason, the design a computational model that helps in such a task is very useful for the timely medical diagnosis. The hybrid model is build using artificial neural networks and fuzzy logic. Training and testing of the hybrid model was with the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) arrhythmia database. The heartbeats are preprocessed to improve results of classification. Ten different classes of normal and arrhythmia signals for building the hybrid model are considered. We used two electrode signals or leads included in the MIT-BIH arrhythmia database, MLII and V1, V2, or V3 as second electrode signal. The hybrid model is composed by two basic module units, as described below. A basic module unit to perform the classification for each signal lead is used. Each basic module unit is composed of three different classifiers based on the following models: fuzzy KNN algorithm, multilayer perceptron with gradient descent and momentum (MLP-GDM), and multilayer perceptron with scaled conjugate gradient backpropagation (MLP-SCG). The outputs from the classifiers are combined using a fuzzy system for integration of results. We designed two fuzzy systems, Mamdani type-1 fuzzy system (type-1 FIS) and an interval type-2 fuzzy system (IT2FIS). The reason is to perform a comparison between type-1 FIS and IT2FIS in the hybrid model. We have obtained best results in the classification rate using IT2FIS instead of type-1 FIS in the basic units. Finally, a type-1 FIS is used to determine the global classification for the 2 basic units in hybrid model. We obtained a good classification rate in each basic module unit, 92.90% and 92.70% of classification rate for basic modules unit 1 and unit 2 respectively. Finally, we obtained a 93.80% when used type-1 FIS and 94.20% of classification rate used IT2FIS combining both basic module units. In the results presented, we improve the global classification in proposed hybrid model combining neural networks and fuzzy logic used both signal lead included in MIT-BIH arrhythmia database. The proposed hybrid model maybe extended to use multi-lead arrhythmia classification using other databases that contain 12 leads to be able to make a complete medical diagnosis.

Hybrid model based on neural networks, type-1 and type-2 fuzzy systems for 2-lead cardiac arrhythmia classification

In this paper we describe a hybrid architecture for classification of cardiac arrhythmias taking as a source the ECG records MIT-BIH Arrhythmia database. The Samples were taken from the LBBB, RBBB, PVC and Fusion Paced and Normal arrhythmias, as well as the normal beats. These were segmented and normalized and three methods of classification were used: Fuzzy KNN, Multi Layer Perceptron with Gradient Descent with Momentum Backpropagation, and Multi Layer Perceptron with Scaled Conjugate Gradient Backpropagation. Finally, we used a Mamdani fuzzy inference system to combine the outputs of each classifier, and we achieved a very high classification rate of 98%.

Hybrid system for cardiac arrhythmia classification with fuzzy k-nearest neighbors and Multi Layer Perceptrons combined by a fuzzy inference system

In this paper we describe a hybrid architecture for classification of cardiac arrhythmias taking as a source the ECG records MIT-BIH Arrhythmia database. The Samples were taken from the LBBB, RBBB, PVC and Fusion Paced and Normal arrhythmias, as well as the normal heartbeats. These were segmented and transformation and 3 methods of classification were used: Fuzzy KNN, Multi Layer Perceptron with Gradient Descent and momentum Backpropagation and Multi Layer Perceptron with Scaled Conjugate Gradient Backpropagation. Finally, we used a Mamdani type fuzzy inference system to combine the outputs of each classifier, and we achieved a very high classification rate of 98%.

Hybrid System for Cardiac Arrhythmia Classification with Fuzzy K-Nearest Neighbors and Neural Networks Combined by a Fuzzy Inference System

In this paper we present a new variant of the Fuzzy K-Nearest Neighbor algorithm. We propose to use Interval Type-2 Fuzzy Logic to improve the performance of the Fuzzy K-Nearest Neighbor algorithm (Fuzzy KNN algorithm). We have used different measures to calculate the distance between the neighbors and the vector to be classified, such as Euclidean, Hamming, cosine similarity and city block distances. These distances represent the inputs for the Interval Type-2 Fuzzy Inference System. Simulation results show the potential of the proposed approach.

Eduardo Ramírez

Papers

Hybrid intelligent system for cardiac arrhythmia classification with Fuzzy K-Nearest Neighbors and neural networks combined with a fuzzy system

Hybrid model based on neural networks, type-1 and type-2 fuzzy systems for 2-lead cardiac arrhythmia classification

Hybrid system for cardiac arrhythmia classification with fuzzy k-nearest neighbors and Multi Layer Perceptrons combined by a fuzzy inference system

Hybrid System for Cardiac Arrhythmia Classification with Fuzzy K-Nearest Neighbors and Neural Networks Combined by a Fuzzy Inference System

A new variant of Fuzzy K-Nearest Neighbor using Interval Type-2 Fuzzy Logic