Biometric Applications of One-Dimensional Physiological Signals – Electrocardiograms

In the last decade, affordable computing power has become available on platforms intended for low-power, mobile applications. For example, Gumstix Overo products integrate WiFi/Bluetooth connectivity, microSD storage, and 600 MHz Texas Instruments OMAP (Open Multimedia Application Platform) 35xx processors with up to 256 MB of flash memory/SDRAM, offering laptop-like resources and performance in a form factor of a stick of gum (Chaoui, et al., 2001). This speaks to the potential for wearable, wireless medical devices based on such products to process signals on-board; functionality that previously required expensive, bulky, benchtop/bedside equipment. These computational capabilities also show promise for smart devices that implement context-based intelligence and ondevice expert systems for clinical decision making. This move toward highly-capable and intelligent mobile medical devices drives the need for verification tools, data integrity checkers, and role-based security mechanisms that can also be implemented at the embedded level, since the potential usage scenarios and associated protection needs are numerous in comparison with legacy medical device applications in controlled hospital and home care settings. For example, many implementations of personal and body area networks have been developed to facilitate ambulatory monitoring of health status, where physiological parameters such as heart rate, heart activity, blood oxygen saturation, and respiration rate can be gathered through the use of mobile, wearable electrocardiographs and pulse oximeters (Jovanov, et al., 2009; Galeottei, et al., 2008; Chuo, et al., 2010). These data need to be authenticated and checked for integrity before they are stored in electronic patient records, which implies the need for “owner-aware” devices that verify user identity as part of the data acquisition process (Warren, et al., 2005; Warren & Jovanov, 2006). Many biometric authentication protocols are computationally intensive and can well-utilize the emerging computational capabilities of low-power mobile devices. A broad range of biomedical data, from physiological signals/images to behavioral traits, has been explored for its biometric authentication and identification potential (Biel, et al., 2001; Chan, et al., 2008; Doi & Yamanaka, 2004; Duc, et al., 1997; Elsherief, et al., 2006; Faundez-Zanuy, 2005; Irvine, et al., 2001; Israel, et al., 2005; Shen, et al., 2002; Sullivan, et al., 2007; Yao & Wan, 2010; G. H. Zhang, et al., 2009). Image-based mechanisms that assess