Signal Processing for Applications in Healthcare Systems

Pau-Choo Chung, Chein-I Chang, Qi Tian, Chien-Cheng Lee
2008 EURASIP Journal on Advances in Signal Processing  
The cost of healthcare has been skyrocketing over the past decades. This is mainly due to the rapid growth of aging population. To provide more comfortable and effective healthcare services, a recent trend of healthcare has been directed towards deinstitutionalization, community care, and home care. On the other hand, the technologies for healthcare have run an impressive evolution in signal/image processing, computers, and network communications and have facilitated the development of
more » ... signal/image processing techniques in consumer electronics. Accordingly, the quality of community and home healthcare has been significantly improved and many portable devices have also been developed for a wide variety of applications where signal processing-based software plays a pivotal role in their success. The goal of this special issue is to provide most up-to-date and recent advances of signal/image processing techniques in system and network design of healthcare applications and to serve as a forum and venue for researchers in both academia and industries working in this fascinating and emerging area who share their experiences and findings with the readers. The timely need and demand for this special issue can be witnessed by tremendous responses to the announcement of call for papers, where 37 submissions were received, all of which have been gone through in-depth peer review. While many excellent papers were unfortunately left out, 16 papers selected by guest editors to be published in this special issue that cover a wide variety of healthcare applications ranging from medical signal/image processing to system design and development of hardware devices, each of which can be briefly summarized as follows. The paper entitled "Using intracardiac vectorcardiographic loop for surface ECG synthesis" by A. Kachenoura et al. describes a supervised machine learning approach to reconstruct the surface of ECG signals from EGM signals that are recorded by implanted devices. The proposed method was applied to reconstruct abnormal heart rhythm and exhibited promising results. The paper entitled "A minimax mutual information scheme for supervised feature extraction and its application to EEG-based brain-computer interfacing" by F. Oveisi and A. Erfanian proposes a two-dimensional mutual information-based feature extraction approach in the sense that an optimal feature set obtained from the data should have maximum joint data redundancy with target classes. The authors develop a so-called minimax mutual information feature extraction (Minimax MIFX) which maximizes the mutual information between a new feature set and target classes while minimizing the data redundancy. Its performance is then evaluated by EEG signal classification to show if the proposed approach performed better than other feature extraction methods in classification accuracy. The paper entitled "EEG-based subject-and sessionindependent drowsiness detection: an unsupervised approach" by Nikhil et al. develops an unsupervised subjectand session-independent approach for driver drowsiness detection. It demonstrates that the EEG power in the alpha band (as well as in the theta band) is correlated with changes in the driver's cognitive state with respect to drowsiness.
doi:10.1155/2008/869364 fatcat:x6mgkrysrzegje7j6kdsst6ham