Key result
Signal processing and classification techniques for body-worn sensor systems can automatically extract useful information from continuously collected patient data, such as ECG and inertial signals.
This review outlines signal processing and classification frameworks essential for managing the large volumes of data generated by body-worn medical sensors, particularly for ECG and inertial monitoring.
May enable automated wearable monitoring; leaves open prospective validation before clinical adoption.
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.
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Ghasemzadeh et al. (2012) reported a review. Signal processing and classification techniques for body-worn sensor systems was evaluated. Signal processing and classification techniques for body-worn sensor systems can automatically extract useful information from continuously collected patient data, such as ECG and inertial signals.
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