Name
Abstract | ||
|---|---|---|
Body Area Sensor Networks (BASN) are expected to provide a way to improve medical care while reducing its costs. Reducing their energy consumption is a critical step before building reliable and durable systems. Acquisition and classification of Electrocardiogram (ECG) signals is a central task in medical BASNs. This paper introduces a method to perform the classification between three abnormal types of heart beats at ultra-low power using Sparse Neural Associative Memories (SNAM). Based on recent analog implementation of a SNAM node using the ST CMOS 65 nm design kit, the proposed SNAM uses only 864 fJ per classification. Compared to a digital ultra-low power multi-core architecture, this SNAM consumes several orders of magnitude less energy while achieving classification accuracy of 93.5 %. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/NEWCAS.2016.7604764 | 2016 14th IEEE International New Circuits and Systems Conference (NEWCAS) |
Keywords | Field | DocType |
body area sensor networks,medical care,energy consumption,ECG signal acquisition,ECG signal classification,electrocardiogram,medical BASN,abnormal heart beat,sparse neural associative memories,ST CMOS design kit,digital ultralow power multicore architecture | Content-addressable memory,Computer science,CMOS,Electronic engineering,Wireless sensor network,Energy consumption,Low-power electronics | Conference |
ISSN | ISBN | Citations |
2472-467X | 978-1-4673-8901-3 | 1 |
PageRank | References | Authors |
0.40 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul Chollet | 1 | 1 | 1.75 |
| Kevin Colombier | 2 | 1 | 0.40 |
| Cyril Lahuec | 3 | 29 | 9.17 |
| Matthieu Arzel | 4 | 69 | 15.10 |
| Fabrice Seguin | 5 | 36 | 16.02 |