TY - GEN
T1 - RMED
T2 - 2011 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2011
AU - Alemzadeh, Homa
AU - Saleheen, Mushfiq U.
AU - Jin, Zhanpeng
AU - Kalbarczyk, Zbigniew
AU - Iyer, Ravishankar K.
PY - 2011
Y1 - 2011
N2 - In this paper we propose a multi-parameter reconfigurable architecture framework for patient-specific medical monitoring. This architecture is mainly composed of a set of heterogeneous processing engines and flexible communication interfaces, which enable the run-time configuration of the architecture for optimal diagnosis of different diseases. The flexibility of the proposed framework is evaluated by demonstrating two different medical applications for monitoring brain and heart status on an FPGA-based hardware prototype. The evaluated epileptic seizure detection application gains a high detection performance with overall accuracy of 98.52% and sensitivity of 99.47%. For the cardiac ICU monitoring application, the experimental results for detecting abnormality of blood pressure and heart rate in selected patients show a high true positive rate of 94.74%. By applying algorithmic enhancements in the detection scheme, we even achieve early detection of abnormalities in blood pressure in the range of few minutes before standard ICU monitor alarms with a true positive rate of 64%. With a balanced mixture of flexibility, patient-specificity, and detection accuracy at small hardware footprint, the proposed architecture can be an attractive framework for embedded monitoring of a wide variety of medical conditions.
AB - In this paper we propose a multi-parameter reconfigurable architecture framework for patient-specific medical monitoring. This architecture is mainly composed of a set of heterogeneous processing engines and flexible communication interfaces, which enable the run-time configuration of the architecture for optimal diagnosis of different diseases. The flexibility of the proposed framework is evaluated by demonstrating two different medical applications for monitoring brain and heart status on an FPGA-based hardware prototype. The evaluated epileptic seizure detection application gains a high detection performance with overall accuracy of 98.52% and sensitivity of 99.47%. For the cardiac ICU monitoring application, the experimental results for detecting abnormality of blood pressure and heart rate in selected patients show a high true positive rate of 94.74%. By applying algorithmic enhancements in the detection scheme, we even achieve early detection of abnormalities in blood pressure in the range of few minutes before standard ICU monitor alarms with a true positive rate of 64%. With a balanced mixture of flexibility, patient-specificity, and detection accuracy at small hardware footprint, the proposed architecture can be an attractive framework for embedded monitoring of a wide variety of medical conditions.
UR - http://www.scopus.com/inward/record.url?scp=79956115417&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79956115417&partnerID=8YFLogxK
U2 - 10.1109/LISSA.2011.5754169
DO - 10.1109/LISSA.2011.5754169
M3 - Conference contribution
AN - SCOPUS:79956115417
SN - 9781457704208
T3 - Proceedings of the 2011 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2011
SP - 112
EP - 115
BT - Proceedings of the 2011 IEEE/NIH Life Science Systems and Applications Workshop, LiSSA 2011
Y2 - 7 April 2011 through 8 April 2011
ER -