In 17 chapters, this book presents the principle of many advanced biosignal processing techniques. After an important chapter introducing the main biosignal properties as well as the most recent acquisition techniques, it highlights five specific parts which build the body of this book. Each part concerns one of the most intensively used biosignals in the clinical routine, namely the Electrocardiogram (ECG), the Elektroencephalogram (EEG), the Electromyogram (EMG) and the Evoked Potential (EP). In addition, each part gathers a certain number of chapters related to analysis, detection, classification, source separation and feature extraction. These aspects are explored by means of various advanced signal processing approaches, namely wavelets, Empirical Modal Decomposition, Neural networks, Markov models, Metaheuristics as well as hybrid approaches including wavelet networks, and neuro-fuzzy networks. The last part concerns the Multimodal Biosignal processing. Concise overview of most all standard method Presents latest developments in Biosignal processing INDICE: Introduction.- Biosignal properties and acquisition.- ECG analysis: wavelet based approaches.- ECG analysis: Empirical Mode Decomposition (EMD) based approach.- Extraction of ECG characteristics using source separation techniques.- Statistical models based ECG classification.- ECG compression: a review.- EEG analysis and feature extraction.- Neural networks and hybrid approaches for EEG classification.- Source separation for multichannel EEG.- EMG analysis: time-frequency based techniques.- Empirical Mode Decomposition for EMG feature extraction.- EMG pattern recognition.- EP analysis.- EP classification.- Optimal approaches for EP denoising.- Integration of EEG in functional MRI.-Multimodal compression of biomedical data.- Conclusion.
- ISBN: 978-3-540-89505-3
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 378
- Fecha Publicación: 01/02/2009
- Nº Volúmenes: 1
- Idioma: Inglés