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...
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| Outros autores: | , |
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| Formato: | Electrónico eBook |
| Idioma: | inglés |
| Publicado: |
London :
Academic Press,
2024.
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| Subjects: | |
| Acceso en liña: | ScienceDirect |
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| 245 | 0 | 0 | |a Signal processing driven machine learning techniques for cardiovascular data processing / |c edited by Rajesh Kumar Tripathy and Ram Bilas Pachori. |
| 260 | |a London : |b Academic Press, |c 2024. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 505 | 0 | |a Front Cover -- Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing -- Copyright -- Contents -- List of contributors -- 1 Introduction to cardiovascular signals and automated systems -- 1.1 Heart conduction system and ECG signal -- 1.1.1 Features of ECG signals -- 1.1.2 Heart diseases and morphological changes in ECG signals -- 1.1.3 Automated disease diagnosis system using ECG -- 1.1.3.1 Recording of ECG signals -- 1.1.3.2 Preprocessing of ECG data -- 1.1.3.3 ECG feature extraction and selection -- 1.1.3.4 Machine learning and deep learning | |
| 505 | 8 | |a 1.2 Cardiac auscultation and PCG signal -- 1.2.1 Heart valve diseases and changes in PCG -- 1.2.2 Automated detection of HVDs using PCG -- 1.3 PPG signal and cardiorespiratory activity -- 1.3.1 Automated analysis of PPG signals -- 1.4 Future scope of cardiac data processing -- 1.5 Conclusion -- References -- 2 Third-order tensor-based cardiac disease detection from 12-lead ECG signals using deep convolutional neural network -- 2.1 Introduction -- 2.2 Dataset description -- 2.3 Proposed method -- 2.3.1 Preprocessing and beat segmentation | |
| 505 | 8 | |a 2.3.2 Multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) -- 2.3.3 Deep convolutional neural network (CNN) -- 2.4 Results and discussion -- 2.5 Conclusion and summary -- References -- 3 Ramanujan filter bank-domain deep CNN for detection of atrial fibrillation using 12-lead ECG -- 3.1 Introduction -- 3.2 12-lead ECG database -- 3.3 Proposed approach -- 3.3.1 Time-period representation of ECG -- 3.3.2 Development of TPR-domain deep CNN -- 3.4 Results and discussion -- 3.5 Conclusion -- References | |
| 505 | 8 | |a 4 Detection of atrial fibrillation using photoplethysmography signals: a systemic review -- 4.1 Introduction -- 4.2 Methods -- 4.2.1 Search strategy, inclusion and exclusion criteria -- 4.2.2 Data extraction -- 4.3 Results and discussion -- 4.3.1 Features -- 4.3.2 Cost-effectiveness and accessibility -- 4.3.3 Incorporation of machine and deep learning -- 4.3.4 Clinical implications -- 4.3.5 Limitations and research gaps -- 4.4 Conclusion -- References -- 5 Machine learning-based prediction of depression and anxiety using ECG signals -- 5.1 Introduction -- 5.2 Mental health problems | |
| 505 | 8 | |a 5.2.1 Anxiety disorder -- 5.2.2 Depression disorder -- 5.2.3 Factors affecting mental health -- 5.3 Exploratory data analysis and preprocessing -- 5.4 Feature extraction -- 5.5 Machine learning-based model for prediction and classification of ECG signals -- 5.5.1 Different machine learning models -- 5.5.1.1 Supervised learning -- 5.5.1.2 Unsupervised learning -- 5.5.1.3 Semisupervised learning -- 5.5.1.4 Transfer learning -- 5.5.1.5 Reinforcement learning -- 5.6 Conclusion and future scope -- References | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. In addition, 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. | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence |x Medical applications. | |
| 650 | 0 | |a Cardiovascular system |x Data processing. | |
| 650 | 2 | |a Machine Learning |0 (DNLM)D000069550 | |
| 650 | 6 | |a Apprentissage automatique. |0 (CaQQLa)201-0131435 | |
| 650 | 6 | |a Intelligence artificielle |x Applications en médecine. |0 (CaQQLa)201-0180593 | |
| 650 | 6 | |a Appareil cardiovasculaire |0 (CaQQLa)201-0006631 |x Informatique. |0 (CaQQLa)201-0380011 | |
| 650 | 7 | |a Artificial intelligence |x Medical applications |2 fast |0 (OCoLC)fst00817267 | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 655 | 0 | |a Electronic books. | |
| 700 | 1 | |a Tripathy, Rajesh Kumar. | |
| 700 | 1 | |a Pachori, Ram Bilas. | |
| 776 | 0 | 8 | |c Original |z 044314141X |z 9780443141416 |w (OCoLC)1405365193 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443141416 |