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Complementary Detection for Hardware Efficient On-site Monitoring of Parkinsonian Progress

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. Green open access

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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 > 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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