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T.H. Linh

Bio: T.H. Linh is an academic researcher from Warsaw University of Technology. The author has contributed to research in topics: Neuro-fuzzy & Artificial neural network. The author has an hindex of 7, co-authored 10 publications receiving 800 citations.

Papers
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Journal ArticleDOI
TL;DR: The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution and show that the method may find practical application in the recognition and classification of different type heart beats.
Abstract: Presents the application of the fuzzy neural network for electrocardiographic (ECG) beat recognition and classification. The new classification algorithm of the ECG beats, applying the fuzzy hybrid neural network and the features drawn from the higher order statistics has been proposed in the paper. The cumulants of the second, third, and fourth orders have been used for the feature selection. The hybrid fuzzy neural network applied in the solution consists of the fuzzy self-organizing subnetwork connected in cascade with the multilayer perceptron, working as the final classifier. The c-means and Gustafson-Kessel algorithms for the self-organization of the neural network have been applied. The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution. The investigations show that the method may find practical application in the recognition and classification of different type heart beats.

519 citations

Journal ArticleDOI
TL;DR: A neuro-fuzzy approach to the recognition and classification of heart rhythms on the basis of ECG waveforms using a fuzzy neural network based on the Hermite characterization of the QRS complexes.
Abstract: This paper presents a neuro-fuzzy approach to the recognition and classification of heart rhythms on the basis of ECG waveforms. The important part in recognition fulfills the Hermite characterization of the QRS complexes. The Hermite coefficients serve as the features of the process. These features are applied to a fuzzy neural network for recognition. The results of numerical experiments have confirmed very good performance of such a solution.

175 citations

Proceedings ArticleDOI
07 Aug 2002
TL;DR: The paper presents the neuro-fuzzy approach to the recognition and classification of heart rhythms on the basis of ECG waveforms that fulfills the Hermite characterization of the QRS complexes.
Abstract: The paper presents the neuro-fuzzy approach to the recognition and classification of heart rhythms on the basis of ECG waveforms. The important part in recognition fulfills the Hermite characterization of the QRS complexes. The Hermite coefficients serve as the features of the process. These features are applied to the fuzzy neural network for the recognition. The results of numerical experiments have confirmed the very good performance of such a solution.

72 citations

Journal ArticleDOI
TL;DR: The neuro-fuzzy network applying Takagi-Sugeno-Kang (TSK) fuzzy reasoning for the calibration of the semiconductor sensor array is developed and tested on the example of estimation of the concentration of gas components in the gaseous mixture.
Abstract: The neuro-fuzzy network applying Takagi-Sugeno-Kang (TSK) fuzzy reasoning for the calibration of the semiconductor sensor array is developed in this paper. The structure, as well as the learning algorithm of the neuro-fuzzy network, is presented and tested on the example of estimation of the concentration of gas components in the gaseous mixture (so-called artificial nose problem). The results of numerical experiments are presented and discussed.

25 citations

Journal ArticleDOI
TL;DR: The neuro-fuzzy Takagi-Sugeno-Kang (TSK) network for the recognition and classification of flavor fulfills the self-organizing process used for the creation of the inference rules and has the optimal size.
Abstract: This paper presents the neuro-fuzzy Takagi-Sugeno-Kang (TSK) network for the recognition and classification of flavor. The important role in this process fulfills the self-organizing process used for the creation of the inference rules. The self-organizing neurons perform the role of clustering data into fuzzy groups with different membership values (the preprocessing stage). Applying the automatic control of clusters, we have the optimal size of the TSK network. The developed measuring system has been applied for the recognition of flavor of different brands of beer. The fuzzy neural network is used for processing signals obtained from the semiconductor sensor array. The results of numerical experiments have confirmed the excellent performance of such solutions.

16 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats and results are an improvement on previously reported results for automated heartbeat classification systems.
Abstract: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50 000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.

1,449 citations

Journal ArticleDOI
TL;DR: This work surveys 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.

635 citations

Book
01 Jan 1994

607 citations

Journal ArticleDOI
TL;DR: Five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed and dimensionality reduced features were fed to the Support Vector Machine, neural network and probabilistic neural network (PNN) classifiers for automated diagnosis.

586 citations