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.
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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.
| Type: | Article |
|---|---|
| Title: | Complementary Detection for Hardware Efficient On-site Monitoring of Parkinsonian Progress |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JETCAS.2018.2830971 |
| Publisher version: | http://dx.doi.org/10.1109/JETCAS.2018.2830971 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Biomedical signal processor, classifier, deep brain stimulation (DBS), event detection, feature extraction, Parkinson’s disease (PD). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10049966 |
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