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Multi-Disease Deep Brain Stimulation

Parastarfeizabadi, Mahboubeh, Sillitoe, Roy V. and Kouzani, Abbas Z. 2020, Multi-Disease Deep Brain Stimulation, IEEE Access, vol. 8, pp. 216933-216947, doi: 10.1109/access.2020.3041942.

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Title Multi-Disease Deep Brain Stimulation
Author(s) Parastarfeizabadi, Mahboubeh
Sillitoe, Roy V.
Kouzani, Abbas Z.ORCID iD for Kouzani, Abbas Z. orcid.org/0000-0002-6292-1214
Journal name IEEE Access
Volume number 8
Start page 216933
End page 216947
Total pages 15
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2020
ISSN 2169-3536
Keyword(s) biomarkers
closed-loop
deep brain stimulation
fuzzy logic
multiple diseases
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Satellite broadcasting
Feature extraction
Batteries
Performance evaluation
Frequency control
Classification algorithms
HIGH-FREQUENCY OSCILLATIONS
LOCAL-FIELD POTENTIALS
SUBTHALAMIC NUCLEUS
PARKINSONS-DISEASE
SYSTEM
BETA
PATTERNS
EPILEPSY
DEVICE
Summary Current closed-loop deep brain stimulation (DBS) devices can generally tackle one disorder. This paper presents the design and evaluation of a multi-disease closed-loop DBS device that can sense multiple brain biomarkers, detect a disorder, and adaptively deliver electrical stimulation pulses based on the disease state. The device consists of: (i) a neural sensor, (ii) a controller involving a feature extractor, a disease classifier, and a control strategy, and (iii) neural stimulator. The neural sensor records and processes local field potentials and spikes from within the brain using two low-frequency and high-frequency channels. The feature extractor digitally processes the output of the neural sensor, and extracts five potential biomarkers: alpha, beta, slow gamma, high-frequency oscillations, and spikes. The disease classifier identifies the type of the neurological disorder through an analysis of the biomarkers' amplitude features. The control strategy considers the disease state and supplies the stimulation settings to the neural stimulator. Both the disease classifier and control strategy are based on fuzzy algorithms. The neural stimulator generates electrical stimulation pulses according to the control commands, and delivers them to the target area of the brain. The device can generate current stimulation pulses with specific amplitude, frequency, and duration. The fabricated device has the maximum radius of 15 mm. Its total weight including the circuit board, battery and battery holder is 5.1 g. The performance of the integrated device has been evaluated through six bench and in-vitro experiments. The experimental results are presented, analyzed, and discussed. Six bench and in-vitro experiments were conducted using sinusoidal, normal pre-recorded, and diseased neural signals representing normal, epilepsy, depression and PD conditions. The results obtained through these tests indicate the successful neural sensing, classification, control, and neural stimulating performance.
Language eng
DOI 10.1109/access.2020.3041942
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30146421

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.