The interest in high performance chip architectures for biomedical applications is on the rise. Heart diseases remain by far the main cause of death and a challenging problem for biomedical engineers to monitor and analyze. Electrocardiography (ECG) is an essential practice in heart medicine, which faces computational challenges, especially when 12 lead signals are to be analyzed in parallel, in real time, and under increasing sampling frequencies. Another challenge is the analysis of huge amounts of data that may grow to days of recordings. Nowadays, doctors use eyeball monitoring of the 12-lead ECG paper readout, which may seriously impair analysis accuracy. Our solution leverages the advance in multi-processor system-on-chip architectures, and is centered on the parallelization of the ECG computation kernel. It improves upon state-of-the-art mostly for its capability to perform real-time analysis of input data, leveraging the computation horsepower provided by many concurrent DSPs, more accurate diagnosis of cardiac diseases, and prompter reaction to abnormal heart alterations. The design methodology to go from the 12-lead ECG application specification to the final hardware/software architecture, modeling, and simulation is the focus of this paper. Our system model is based on industrial components. The architectural template we employ is scalable and flexible.

MPSoC ECG Biochip: A Multiprocessor System-on-Chip for Real-Time Human Heart Monitoring and Analysis

BERTOZZI, Davide;
2006

Abstract

The interest in high performance chip architectures for biomedical applications is on the rise. Heart diseases remain by far the main cause of death and a challenging problem for biomedical engineers to monitor and analyze. Electrocardiography (ECG) is an essential practice in heart medicine, which faces computational challenges, especially when 12 lead signals are to be analyzed in parallel, in real time, and under increasing sampling frequencies. Another challenge is the analysis of huge amounts of data that may grow to days of recordings. Nowadays, doctors use eyeball monitoring of the 12-lead ECG paper readout, which may seriously impair analysis accuracy. Our solution leverages the advance in multi-processor system-on-chip architectures, and is centered on the parallelization of the ECG computation kernel. It improves upon state-of-the-art mostly for its capability to perform real-time analysis of input data, leveraging the computation horsepower provided by many concurrent DSPs, more accurate diagnosis of cardiac diseases, and prompter reaction to abnormal heart alterations. The design methodology to go from the 12-lead ECG application specification to the final hardware/software architecture, modeling, and simulation is the focus of this paper. Our system model is based on industrial components. The architectural template we employ is scalable and flexible.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1192678
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