Mohammed, A; Demosthenous, A; (2018) Complementary Detection for Hardware Efficient On-site Monitoring of Parkinsonian Progress. IEEE Journal on Emerging and Selected Topics in Circuits and Systems , 8 (3) pp. 603-615. 10.1109/JETCAS.2018.2830971.

Preview

Text JETCAS-2018_final.pdf - Accepted Version Download (1MB) | Preview

Abstract

The progress of Parkinson & #x2019;s disease (PD) in patients is conventionally monitored through follow-up visits. These may be insufficient for clinicians to obtain a good understanding of the occurrence and severity of symptoms in order to adjust therapy to the patients & #x2019; needs. Portable platforms for PD diagnostics can provide in-depth information, thus reducing the frequency of face-to-face visits. This paper describes the first known on-site PD detection and monitoring processor. This is achieved by employing complementary detection which uses a combination of weak k-NN classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers. Various implementations of the classifier are investigated for trade-offs in terms of area, power and detection performance. Detection performances are validated on an FPGA platform. Achieved accuracy measures were: Matthews correlation coefficient of 0.6162, mean F1-score of 91.38 & #x0025;, and mean classification accuracy of 91.91 & #x0025;. By mapping the implemented designs on a 45 nm CMOS process, the optimal configuration achieved a dynamic power per channel of 2.26 & #x03BC;W and an area per channel of 0.24 mm2.

Download activity - last month

Export as CSVXMLJSON

Download activity - last 12 months

Export as XMLCSVJSON

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item