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Více informací o knize Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing

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Anotace knihy

Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based methods for cardiovascular data analysis. Topics in this book include machine learning methods such as supervised learning, unsupervised learning, semi-supervised learning, and meta-learning combined with different signal processing techniques such as multivariate data analysis, time-frequency analysis, multiscale analysis, and feature extraction techniques for the detection of cardiovascular diseases, heart valve disorders, hypertension, and activity monitoring using ECG, PPG, and PCG signals. This book also includes the applications of digital signal processing (time-frequency analysis, multiscale decomposition, feature extraction, non-linear analysis, and transform domain methods), machine learning and deep learning (convolutional neural network (CNN), recurrent neural network (RNN), transformer and attention-based models, etc.) techniques for the analysis of cardiac signals. The interpretable machine learning and deep learning models combined with signal processing for cardiovascular data analysis are also covered. Provides details regarding the application of various signal processing and machine learning-based methods for cardiovascular signal analysisCovers methodologies as well as experimental results and studiesHelps readers understand the use of different cardiac signals such as ECG, PCG, and PPG for the automated detection of heart ailments and other related biomedical applications

Parametry knihy

Zařazení knihy Knihy v angličtině Computing & information technology Computer science Artificial intelligence

• Plný název: Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing
• Autor: Rajesh Kumar Tripathy, Ram Bilas Pachori
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 184
• EAN: 9780443141416
• ISBN: 044314141X
• ID: 44342154
• Nakladatelství: Elsevier Science
• Hmotnost: 300 g
• Rozměry: 229 × 151 mm
• Datum vydání: 01. June 2024

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