Real-time DCT Learning-based Reconstruction of Neural Signals

Wearable and implantable body sensor network systems are one of the key technologies for continuous monitoring of patient's vital health status such as temperature and blood pressure, and brain activity. Such devices are critical for early detection of emergency conditions of people at risk and offer a wide range of medical facilities and services. Despite continuous advances in the field of wearable and implantable medical devices, it still faces major challenges such as energy-efficient and low-latency reconstruction of signals. This work presents a power-efficient real-time system for recovering neural signals. Such systems are of high interest for implantable medical devices, where reconstruction of neural signals needs to be done in real-time with low energy consumption. We combine a deep network and DCT-learning based compressive sensing framework to propose a novel and efficient compression-decompression system for neural signals. We compare our approach with state-of-the-art compressive sensing methods and show that it achieves superior reconstruction performance with significantly less computing time.


Published in:
2018 26Th European Signal Processing Conference (Eusipco), 1925-1929
Presented at:
European Signal Processing Conference (EUSIPCO), Rome, ITALY, Aug 03-07, 2018
Year:
Jan 01 2018
Publisher:
Los Alamitos, IEEE COMPUTER SOC
ISSN:
2076-1465
ISBN:
978-90-827970-1-5
Keywords:




 Record created 2019-01-25, last modified 2019-01-31


Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)