Publication: Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor
Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor
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Bauer, F. C., Muir, D. R., & Indiveri, G. (2019). Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor. IEEE Transactions on Biomedical Circuits and Systems, 13(6), 1575–1582. https://doi.org/10.1109/tbcas.2019.2953001
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Accurate detection of pathological conditions in human subjects can be achieved through off-line analysis of recorded biological signals such as electrocardiograms (ECGs). However, human diagnosis is time-consuming and expensive, as it requires the time of medical professionals. This is especially inefficient when indicative patterns in the biological signals are infrequent. Moreover, patients with suspected pathologies are often monitored for extended periods, requiring the storage and examination of large amounts of non-pathological
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Bauer, F. C., Muir, D. R., & Indiveri, G. (2019). Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor. IEEE Transactions on Biomedical Circuits and Systems, 13(6), 1575–1582. https://doi.org/10.1109/tbcas.2019.2953001
