Rajesh Kumar Tripathy – författare

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.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.- Provides details regarding the application of various signal processing and machine learning-based methods for cardiovascular signal analysis- Covers methodologies as well as experimental results and studies- Helps 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

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.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.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

Non-Stationary and Nonlinear Data Processing for Automated Computer-Aided Medical Diagnosis demonstrates the applications of machine learning and deep learning combined with signal processing techniques for human-machine interface applications using EMG signals. The book includes the analysis and classification of various heart diseases based on bio-signals like electrocardiogram (ECG), photoplethysmography (PPG), and phonocardiogram (PCG) signals. Various machine learning approaches, including advanced algorithms like multivariate signal processing, time-frequency analysis, and nonlinear signal processing are covered for CAD of neural, muscular, and cardiovascular diseases. The methods for CAD of various brain disorders are also included.Presented techniques utilize advanced non-stationary and nonlinear signal processing, along with machine learning and deep learning-based classification processes. CAD methods for diagnosing various neurological diseases are based on bio-signals such as electroencephalogram (EEG) and magnetoencephalogram (MEG), as well as medical images like magnetic resonance imaging (MRI) and computerized tomography (CT). Finally, the book addresses various types of medical signals and images, integrating nonlinear and non-stationary signal processing, machine learning, and deep learning within the CAD framework for diagnosing various diseases.Focuses on various signal analysis techniquesAddresses a wide range of applications, including the analysis and classification of signals related to neural, muscular, and cardiovascular diseasesCovers CAD methods for diagnosing various brain disorders using bio-signals like EEG and medical images like MRI and CT scansExplores advanced algorithms and methodologies, such as multivariate signal processing, time-frequency analysis, and nonlinear signal processing

Non-Stationary and Nonlinear Data Processing for Automated Computer-Aided Medical Diagnosis demonstrates the applications of machine learning and deep learning combined with signal processing techniques for human-machine interface applications using EMG signals. The book includes the analysis and classification of various heart diseases based on bio-signals like electrocardiogram (ECG), photoplethysmography (PPG), and phonocardiogram (PCG) signals. Various machine learning approaches, including advanced algorithms like multivariate signal processing, time-frequency analysis, and nonlinear signal processing are covered for CAD of neural, muscular, and cardiovascular diseases. The methods for CAD of various brain disorders are also included.Presented techniques utilize advanced non-stationary and nonlinear signal processing, along with machine learning and deep learning-based classification processes. CAD methods for diagnosing various neurological diseases are based on bio-signals such as electroencephalogram (EEG) and magnetoencephalogram (MEG), as well as medical images like magnetic resonance imaging (MRI) and computerized tomography (CT). Finally, the book addresses various types of medical signals and images, integrating nonlinear and non-stationary signal processing, machine learning, and deep learning within the CAD framework for diagnosing various diseases. - Focuses on various signal analysis techniques- Addresses a wide range of applications, including the analysis and classification of signals related to neural, muscular, and cardiovascular diseases- Covers CAD methods for diagnosing various brain disorders using bio-signals like EEG and medical images like MRI and CT scans- Explores advanced algorithms and methodologies, such as multivariate signal processing, time-frequency analysis, and nonlinear signal processing

The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book:Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signalsPresents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interfaceHighlights the latest machine learning and deep learning methods for neural signal processingDiscusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysisShowcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniquesIt is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.

The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book:Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signalsPresents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interfaceHighlights the latest machine learning and deep learning methods for neural signal processingDiscusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysisShowcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniquesIt is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.

The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book:

Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signals Presents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interface Highlights the latest machine learning and deep learning methods for neural signal processing Discusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysis Showcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniques

It is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.

The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book:

Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signals Presents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interface Highlights the latest machine learning and deep learning methods for neural signal processing Discusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysis Showcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniques

It is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.

The book "Advanced wearable sensing in clinical practice towards healthcare 5.0" provides an overview of the state of the art that caters various professionals from clinical, scientific, and engineering backgrounds, as well as the novices that are interested in wearable sensing technology. It presents a comprehensive overview of modern wearable technology and unmet challenges. The book covers research hotspots in wearable technology, including advanced materials, low-cost wearable sensing systems, bio-signal processing, multimodal data analysis, artificial intelligence, and internet-of-medical-things. The readers from various research backgrounds (e.g., biomedical engineering, artificial intelligence, material sciences, etc.) can get an in-depth understanding of current trends and future directions. It summarizes different technologies regarding the clinical needs, theoretical basis, latest technical innovations, challenges in implementation, and limitations. These details will help improve existing designs and algorithms to cater different application scenarios. The book also provides the details of the implementation of advanced wearable sensors in different clinical settings including population screening, clinical diagnosis, to remote healthcare monitoring. The targeted diseases include cardiovascular, neurological, respiratory, metabolic, and other chronic diseases. By analyzing relevant standards and regulations, the challenges for next-generation wearable sensors are discussed in the context of healthcare ecosystem from a perspective of healthcare 5.0. In summary, the book focus on technical innovations but also delves into clinical and regulatory aspects. It caters a broad spectrum of readers. Clinical researchers, healthcare professionals, and students can obtain a better grasp of current trends of wearable technology. Biomedical engineers can understand the clinical needs, the state of the art, and the challenges in implementation, to develop better wearable sensors and algorithms for signal processing and data analysis. Public health researchers and policymakers can update the regulations and clinical guidelines to enable the integration of advanced wearable sensing technologies into current healthcare ecosystems.