Title :
Wireless Medical-Embedded Systems: A Review of Signal-Processing Techniques for Classification
Author :
Ghasemzadeh, Hassan ; Ostadabbas, S. ; Guenterberg, E. ; Pantelopoulos, A.
Author_Institution :
Comput. Sci. Dept., Univ. of California, Los Angeles, Los Angeles, CA, USA
Abstract :
Body-worn sensor systems will help to revolutionize the medical field by providing a source of continuously collected patient data. This data can be used to develop and track plans for improving health (more sleep and exercise), detect disease early, and provide an alert for dangerous events (e.g., falls and heart attacks). The amount of data collected by even a small set of sensors running all day is too much for any person to analyze. Signal processing and classification can be used to automatically extract useful information. This paper presents a general classification framework for wireless medical devices and reviews the available literature for signal processing and classification systems or components used in body-worn sensor systems. Examples focus on electrocardiography classification and signal processing for inertial sensors.
Keywords :
biomedical equipment; body sensor networks; diseases; electrocardiography; feature extraction; medical signal processing; signal classification; sleep; automatically extract useful information; body-worn sensor systems; continuously collected patient data; disease detection; electrocardiography classification; exercise; falls; heart attacks; inertial sensors; medical field; signal classification; signal processing techniques; sleep; wireless medical devices; wireless medical-embedded systems; Discrete wavelet transforms; Electrocardiography; Feature extraction; Medical services; Motion segmentation; Noise; Classification; embedded systems; healthcare; signal processing;
Journal_Title :
Sensors Journal, IEEE
DOI :
10.1109/JSEN.2012.2222572
