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Published by LAP LAMBERT Academic Publishing, 2012
ISBN 10: 365916173XISBN 13: 9783659161735
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
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Condition: New.
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Published by Editions Notre Savoir, 2023
ISBN 10: 6205972387ISBN 13: 9786205972380
Seller: GF Books, Inc., Hawthorne, CA, U.S.A.
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Condition: Fine. Book is in Used-LikeNew condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear.
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Published by LAP LAMBERT Academic Publishing Apr 2023, 2023
ISBN 10: 6206156567ISBN 13: 9786206156567
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The various classification algorithms can be used to classify extracted features from ECG signal. High performance of classification depends on how well the vectors of features can be separated in the feature space. Proposed architecture presents ECG based arrhythmia classification with more robust features and Regression based classifier. It proposes an effective automated classification of cardiac arrhythmia using MIT-BIH arrhythmia database and Local Clinical dataset. Proposed Method have trained the Incremental Support Vector Regression Classifier with 320 samples of different arrhythmias. Proposed Method has been tested and compared with the most common classifier such as Artificial Neural network, Support Vector Machine and Minimum Distance Classifier. From The confusion matrix it is clear that our proposed algorithm works well for multiple class recognition problem. Proposed Architecture uses both time and frequency domain features for classification purpose. Due to use of higher order statistic our classification problem becomes simpler than traditional morphological feature. Proposed algorithm delivered high performance even with smaller learning data. 104 pp. Englisch.
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Published by Edizioni Sapienza Mai 2023, 2023
ISBN 10: 6205972409ISBN 13: 9786205972403
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -I vari algoritmi di classificazione possono essere utilizzati per classificare le caratteristiche estratte dal segnale ECG. Le prestazioni elevate della classificazione dipendono dalla capacità di separare i vettori di caratteristiche nello spazio delle caratteristiche. L'architettura proposta presenta una classificazione delle aritmie basata sull'ECG con caratteristiche più robuste e un classificatore basato sulla regressione. Propone un'efficace classificazione automatizzata delle aritmie cardiache utilizzando il database delle aritmie MIT-BIH e il set di dati clinici locali. Il metodo proposto ha addestrato il classificatore incrementale di regressione vettoriale con 320 campioni di aritmie diverse. Il metodo proposto è stato testato e confrontato con i classificatori più comuni, come la rete neurale artificiale, la Support Vector Machine e il classificatore a distanza minima. Dalla matrice di confusione si evince che l'algoritmo proposto funziona bene per il riconoscimento di più classi. L'architettura proposta utilizza sia le caratteristiche del dominio del tempo che quelle del dominio della frequenza per la classificazione. Grazie all'uso di statistiche di ordine superiore, il problema della classificazione diventa più semplice rispetto alle tradizionali caratteristiche morfologiche. L'algoritmo proposto fornisce prestazioni elevate anche con dati di apprendimento più piccoli. 104 pp. Italienisch.
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Published by Edições Nosso Conhecimento Mai 2023, 2023
ISBN 10: 6205972417ISBN 13: 9786205972410
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Os vários algoritmos de classificação podem ser utilizados para classificar as características extraídas do sinal ECG. O elevado desempenho da classificação depende da forma como os vectores de características podem ser separados no espaço de características. A arquitectura proposta apresenta uma classificação da arritmia baseada no ECG com características mais robustas e um classificador baseado na regressão. Propõe uma classificação automática eficaz da arritmia cardíaca utilizando a base de dados de arritmia MIT-BIH e o conjunto de dados clínicos locais. O método proposto treinou o classificador de regressão de vectores de apoio incremental com 320 amostras de diferentes arritmias. O método proposto foi testado e comparado com o classificador mais comum, como a rede neural artificial, a máquina de vectores de apoio e o classificador de distância mínima. A partir da matriz de confusão, é evidente que o algoritmo proposto funciona bem para o problema de reconhecimento de várias classes. A arquitectura proposta utiliza características do domínio do tempo e da frequência para efeitos de classificação. Devido à utilização de estatísticas de ordem superior, o nosso problema de classificação torna-se mais simples do que a característica morfológica tradicional. O algoritmo proposto apresenta um desempenho elevado mesmo com dados de aprendizagem mais pequenos. 104 pp. Portugiesisch.
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Published by Verlag Unser Wissen Mai 2023, 2023
ISBN 10: 620597231XISBN 13: 9786205972311
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Die verschiedenen Klassifizierungsalgorithmen können verwendet werden, um extrahierte Merkmale aus dem EKG-Signal zu klassifizieren. Eine hohe Klassifikationsleistung hängt davon ab, wie gut die Merkmalsvektoren im Merkmalsraum getrennt werden können.Die vorgeschlagene Architektur bietet eine EKG-basierte Arrhythmie-Klassifizierung mit robusteren Merkmalen und einem regressionsbasierten Klassifikator. Es schlägt eine effektive automatisierte Klassifizierung von Herzrhythmusstörungen unter Verwendung der MIT-BIH-Arrhythmiedatenbank und des lokalen klinischen Datensatzes vor. Das vorgeschlagene Verfahren hat den Incremental Support Vector Regression Classifier mit 320 Proben verschiedener Arrhythmien trainiert. Die vorgeschlagene Methode wurde getestet und mit den gebräuchlichsten Klassifikatoren wie künstlichen neuronalen Netzwerken, Support Vector Machine und Minimum Distance Classifier verglichen. Aus der Konfusionsmatrix geht hervor, dass unser vorgeschlagener Algorithmus gut für das Problem der Erkennung mehrerer Klassen funktioniert. Die vorgeschlagene Architektur verwendet sowohl Zeit- als auch Frequenzbereichsmerkmale für Klassifizierungszwecke. Aufgrund der Verwendung von Statistiken höherer Ordnung wird unser Klassifizierungsproblem einfacher als herkömmliche morphologische Merkmale. Der vorgeschlagene Algorithmus lieferte selbst bei kleineren Lerndaten eine hohe Leistung. 108 pp. Deutsch.
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Published by Ediciones Nuestro Conocimiento Mai 2023, 2023
ISBN 10: 6205972328ISBN 13: 9786205972328
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Los distintos algoritmos de clasificación pueden utilizarse para clasificar las características extraídas de la señal de ECG. El alto rendimiento de la clasificación depende de lo bien que puedan separarse los vectores de características en el espacio de características. La arquitectura propuesta presenta una clasificación de arritmias basada en ECG con características más robustas y un clasificador basado en regresión. Propone una clasificación automatizada eficaz de las arritmias cardiacas utilizando la base de datos de arritmias MIT-BIH y el conjunto de datos clínicos locales. El método propuesto ha entrenado el clasificador de regresión de vectores de soporte incremental con 320 muestras de diferentes arritmias. El método propuesto se ha probado y comparado con los clasificadores más comunes, como la red neuronal artificial, la máquina de vectores de soporte y el clasificador de distancia mínima. De la matriz de confusión se desprende claramente que nuestro algoritmo propuesto funciona bien para problemas de reconocimiento de clases múltiples. La arquitectura propuesta utiliza características de dominio de tiempo y frecuencia para la clasificación. Debido al uso de estadísticas de orden superior, nuestro problema de clasificación es más simple que las características morfológicas tradicionales. El algoritmo propuesto ofrece un alto rendimiento incluso con datos de aprendizaje más pequeños. 104 pp. Spanisch.
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Published by LAP LAMBERT Academic Publishing Aug 2023, 2023
ISBN 10: 6206739139ISBN 13: 9786206739135
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explains how a deep generative adversarial network built on a large dataset may detect arrhythmias more accurately than physicians. Furthermore, feature extraction has traditionally been seen as an essential component of electrocardiogram arrhythmia classification The purpose of this research is to examine ECG arrhythmia classification using a deep dense generative adversarial network. The GAN architecture shown in this book can be taught to produce ECG signals that are comparable to real-world ECG signals. The results indicate that using a sequence-based strategy for all ECG-beat types substantially improves area under curve on our test set. Traditional architecture does not naturally address this structure, and therefore suffers from decreased performance when such a structure is informative. This book compares the proposed technique to kernel principle component analysis with incremental support vector regression, discrete wavelet transforms with incremental support vector regression and general sparse neural network. From obtained results, it is concluded that the proposed GAN technique is superior to these three methods with an overall accuracy of 97.44 percent. 164 pp. Englisch.
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Published by Edições Nosso Conhecimento Dez 2023, 2023
ISBN 10: 6206959848ISBN 13: 9786206959847
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 148 pp. Portugiesisch.
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Published by Edizioni Sapienza Dez 2023, 2023
ISBN 10: 620695983XISBN 13: 9786206959830
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 144 pp. Italienisch.
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Published by Verlag Unser Wissen Dez 2023, 2023
ISBN 10: 6206959805ISBN 13: 9786206959809
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 148 pp. Deutsch.
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Published by Ediciones Nuestro Conocimiento Dez 2023, 2023
ISBN 10: 6206959813ISBN 13: 9786206959816
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 148 pp. Spanisch.
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Published by Editions Notre Savoir Omniscriptum, 2023
ISBN 10: 6206959821ISBN 13: 9786206959823
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Ce livre explique comment un réseau accusatoire génératif profond construit sur un grand ensemble de données peut détecter les arythmies avec plus de précision que les médecins. En outre, l'extraction de caractéristiques a toujours été considérée comme un élément essentiel de la classification des arythmies par électrocardiogramme. L'objectif de cette recherche est d'examiner la classification des arythmies par ECG à l'aide d'un réseau accusatoire génératif dense et profond. L'architecture GAN présentée dans ce livre peut être enseignée pour produire des signaux ECG comparables aux signaux ECG du monde réel. Les résultats indiquent que l'utilisation d'une stratégie basée sur les séquences pour tous les types de battements ECG améliore considérablement l'aire sous la courbe sur notre ensemble de tests. L'architecture traditionnelle ne prend pas naturellement en compte cette structure et souffre donc d'une baisse de performance lorsqu'une telle structure est informative. Cet ouvrage compare la technique proposée à l'analyse des composantes de principe du noyau avec régression incrémentielle du vecteur de support, aux transformées en ondelettes discrètes avec régression incrémentielle du vecteur de support et au réseau neuronal clairsemé général. Les résultats obtenus permettent de conclure que la technique GAN proposée est supérieure à ces trois méthodes, avec une précision globale de 97,44 %. 148 pp. Französisch.
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