Biomedical Signal Processing for Healthcare Applications
Varun Bajaj, G. R. Sinha, Chinmay Chakraborty, Varun Bajaj, G. R. Sinha, Chinmay Chakraborty
- 336 páginas
- English
- ePUB (apto para móviles)
- Disponible en iOS y Android
Biomedical Signal Processing for Healthcare Applications
Varun Bajaj, G. R. Sinha, Chinmay Chakraborty, Varun Bajaj, G. R. Sinha, Chinmay Chakraborty
Información del libro
This book examines the use of biomedical signal processing—EEG, EMG, and ECG—in analyzing and diagnosing various medical conditions, particularly diseases related to the heart and brain. In combination with machine learning tools and other optimization methods, the analysis of biomedical signals greatly benefits the healthcare sector by improving patient outcomes through early, reliable detection. The discussion of these modalities promotes better understanding, analysis, and application of biomedical signal processing for specific diseases.
The major highlights of Biomedical Signal Processing for Healthcare Applications include biomedical signals, acquisition of signals, pre-processing and analysis, post-processing and classification of the signals, and application of analysis and classification for the diagnosis of brain- and heart-related diseases. Emphasis is given to brain and heart signals because incomplete interpretations are made by physicians of these aspects in several situations, and these partial interpretations lead to major complications.
FEATURES
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- Examines modeling and acquisition of biomedical signals of different disorders
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- Discusses CAD-based analysis of diagnosis useful for healthcare
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- Includes all important modalities of biomedical signals, such as EEG, EMG, MEG, ECG, and PCG
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- Includes case studies and research directions, including novel approaches used in advanced healthcare systems
This book can be used by a wide range of users, including students, research scholars, faculty, and practitioners in the field of biomedical engineering and medical image analysis and diagnosis.
Preguntas frecuentes
Información
1 Automatic Sleep EEG Classification with Ensemble Learning Using Graph Modularity
Contents
1.1 Introduction
- Lying down posture
- Raised threshold to sensory simulation
- Low level of motor output
- Unparalleled behavior dreaming
- Electroencephalography (EEG): Electroencephalogram (EEG) is a test used to evaluate the electrical activity in the brain. Brain cells communicate with each other through electrical impulses. EEG can be used to detect potential problems associated with this activity.
- Electrooculography (EOG): It is a technique for measuring the corneo-retinal standing potential that exists between the front and the back of the human eye. The resulting signal is called the electrooculogram. Primary applications are in ophthalmological diagnosis and in recording eye movements.
- Electromyography (EMG): It is an electrodiagnostic medicine technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG is performed using an instrument called an electromyograph to produce a record called electromyogram.
- SpO2: Oxygen saturation (SpO2) is a measurement of how much oxygen your blood is carrying as a percentage of the maximum it could carry. For a healthy individual, the normal SpO2 should be between 96% and 99%. High altitudes and other factors may affect what is considered normal for a given individual.
- Electrocardiography (ECG): It records the electrical signal from our heart to check for different heart conditions. Electrodes are placed on our chest to record our heart’s electrical signals, which cause our heart to beat. The signals are shown as waves on an attached computer monitor or printer.
- Breathing functions: Breathing provides oxygen to the body parts and eliminates carbon dioxide resulting from cell metabolism. Major physiologic switches in breathing take place during the sleeping period linked to alterations in respiratory drive and musculature.