Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (XVI, 835 p. 398 illus., 267 illus. in color).

Edition: 1st ed. 2022

ISBN: 978-981-1915-20-8

Series Statement: Lecture Notes in Electrical Engineering 888

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-19-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-21-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-22-2

Language: English

Subjects: Computer Science

DOI: 10.1007/978-981-19-1520-8

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XVI, 835 p. 398 illus., 267 illus. in color).

Edition: 1st ed. 2022

ISBN: 978-981-1915-20-8

Series Statement: Lecture Notes in Electrical Engineering 888

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-19-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-21-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-22-2

Language: English

Subjects: Computer Science

DOI: 10.1007/978-981-19-1520-8

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XIV, 901 p. 473 illus., 366 illus. in color)

Edition: 1st ed. 2023

ISBN: 9789819900855

Series Statement: Lecture Notes in Electrical Engineering 997

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9900-84-8

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9900-86-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9900-87-9

Language: English

DOI: 10.1007/978-981-99-0085-5

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (186 pages)

Edition: 1st ed.

ISBN: 0-443-14140-1

Note: 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 -- 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 -- 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 -- 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 -- 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 -- 6 A robust peak detection algorithm for localization and classification of heart sounds in PCG signals -- 6.1 Introduction -- 6.2 Literature review -- 6.3 Proposed methodology -- 6.3.1 Discrete wavelet transform -- 6.3.2 Peak detection using K-means clustering -- 6.4 Experimental results -- 6.4.1 Dataset description -- 6.4.2 Peak localization -- 6.4.3 Classification results -- 6.5 Results and discussion -- 6.6 Conclusion -- References -- 7 Verifying the effectiveness of a Taylor-Fourier filter bank-based PPG signal denoising approach using machine learning -- 7.1 Introduction -- 7.2 PPG signal database -- 7.3 Method -- 7.3.1 O-spline FIR filter implementation -- 7.3.2 Frequency estimation -- 7.3.3 Machine learning-based PPG quality measurement -- 7.4 Results and discussion -- 7.4.1 High-frequency noise filtering -- 7.4.2 Low-frequency noise filtering -- 7.4.3 Discussion -- 7.5 Conclusion -- References. , 8 Automated detection of hypertension from PPG signals using continuous wavelet transform and transfer learning -- 8.1 Introduction -- 8.2 Methodology -- 8.2.1 Database -- 8.2.2 Preprocessing of raw PPG records -- 8.2.3 Continuous wavelet transform -- 8.2.4 PPG-to-scalogram conversion -- 8.2.5 Transfer learning approach -- 8.2.5.1 DenseNet201 architecture -- 8.2.5.2 VGG19 architecture -- 8.3 Results -- 8.4 Discussion -- 8.5 Conclusion -- References -- 9 Automated estimation of blood pressure using PPG recordings: an updated review -- 9.1 Introduction -- 9.2 Technical approaches and challenges -- 9.2.1 Pulse transit time -- 9.2.2 Reflective PTT -- 9.2.3 PPG waveform analysis -- 9.2.4 PPG with minicuff and amplitude analysis -- 9.3 Technical issues associated with accurate BP estimation using PPG signals -- 9.3.1 Artificial intelligence in PPG-based automatic estimation of BP -- 9.3.2 Skin attachment and contact pressure in reliable sensor placement -- 9.3.3 Multisite PPG measurement -- 9.3.4 Multiwavelength PPG signals -- 9.4 Conclusion -- References -- 10 Time-frequency-domain deep representation learning for detection of heart valve diseases using PCG recordings for IoT-bas... -- 10.1 Introduction -- 10.2 Heart sound signal databases -- 10.3 Proposed method -- 10.3.1 Filtering and amplitude normalization -- 10.3.2 Synchrosqueezed short-time Fourier transform -- 10.3.3 TFD-based DRL model -- 10.4 Results and discussion -- 10.5 Conclusion -- References -- Index -- Back Cover.

Additional Edition: ISBN 0-443-14141-X

Language: English

UID:

Format: 1 Online-Ressource (XVI, 835 p. 398 illus., 267 illus. in color)

Edition: 1st ed. 2022

ISBN: 9789811915208

Series Statement: Lecture Notes in Electrical Engineering 888

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-19-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-21-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-22-2

Language: English

Subjects: Computer Science

DOI: 10.1007/978-981-19-1520-8

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XX, 767 p. 301 illus., 224 illus. in color)

ISBN: 9789811309236

Series Statement: Advances in Intelligent Systems and Computing 748

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-130-922-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-130-924-3

Language: English

DOI: 10.1007/978-981-13-0923-6

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XVI, 835 p. 398 illus., 267 illus. in color).

Edition: 1st ed. 2022

ISBN: 978-981-1915-20-8

Series Statement: Lecture Notes in Electrical Engineering 888

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-19-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-21-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1915-22-2

Language: English

Subjects: Computer Science

DOI: 10.1007/978-981-19-1520-8

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: xvi, 835 Seiten : , Illustrationen, Diagramme (teilweise farbig).

ISBN: 978-981-19-1519-2

Series Statement: Lecture notes in electrical engineering Volume 888

Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-981-19-1520-8

Language: English

Subjects: Computer Science

UID:

Format: 1 Online-Ressource (28 PDFs (420 pages))

ISBN: 9781799821229

Content: "This book explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of challenging healthcare engineering solutions"--

Note: Description based on title screen (IGI Global, viewed 03/19/2020)

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 179982120X

Additional Edition: ISBN 9781799821205

Language: English

DOI: 10.4018/978-1-7998-2120-5

UID:

Format: 1 online resource (757 pages)

ISBN: 981-13-0923-X

Series Statement: Advances in Intelligent Systems and Computing, 748

Content: The book covers the most recent developments in machine learning, signal analysis, and their applications. It covers the topics of machine intelligence such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and electromyogram (EMG) as well as other signals such as speech signals, communication signals, vibration signals, image, and video. Further, it analyzes normal and abnormal categories of real-world signals, for example normal and epileptic EEG signals using numerous classification techniques. The book is envisioned for researchers and graduate students in Computer Science and Engineering, Electrical Engineering, Applied Mathematics, and Biomedical Signal Processing.

Note: Chapter 1: Detecting R-peaks in Electrocardiogram signal using Hilbert envelope -- Chapter 2: Lung Nodule Identification and Classification from Distorted CT Images for Diagnosis and Detection of Lung Cancer -- Chapter 3: Baseline wander and power-line interference removal from ECG signals using Fourier decomposition method -- Chapter 4: Baseline wander and power-line interference removal from ECG signals using Fourier decomposition method -- Chapter 5: An Empirical Analysis of Instance-based Transfer Learning Approach on Protease Substrate Cleavage Sites Prediction -- Chapter 6: Comparison analysis: single and multichannel EMD based filtering with application to BCI -- Chapter 7: A 2-norm Squared Fuzzy-based Least Squares Twin Parametric-margin Support Vector Machine -- Chapter 8: Redesign of a Railway Coach for Safe and Independent Travel of Elderly.

Additional Edition: ISBN 981-13-0922-1

Language: English

DOI: 10.1007/978-981-13-0923-6