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. Author manuscript; available in PMC: 2021 Jun 7.
Published in final edited form as: Sci Transl Med. 2020 Dec 2;12(572):eaay7680. doi: 10.1126/scitranslmed.aay7680

Fig. 6. Performances of CL-DBS at monthly visits in all the enrolled subjects using the fully embedded onboard PC + S algorithm.

Fig. 6.

(A) Performances over the monthly clinical/research visit, such as accuracy and sensitivity of the CL-DBS classifier on top, and energy usage compared to energy usage (TEED) of continuous stimulation (with CL-DBS settings delivered as open-loop paradigm) at the bottom. (B) Average performances across months with CL-DBS during clinical/research visit. (C) Average performances of CL-DBS, including energy usage compared to energy usage (TEED) of continuous stimulation (with CL-DBS settings delivered as open-loop paradigm). (D) Characterization of the delays in the CL-DBS implementation (intervals MO-SO and MO-MSAR). (E) Energy saving with closed-loop stimulation compared to continuous stimulation (with CL-DBS settings delivered as open-loop paradigm) during clinical/research visit [as in (A) (bottom), (B), and (C)] and at short-term daily life usage. (F) TRS versus TEED scatter-plot, showing a subject-based decrease in the overall CL-DBS TEED compared to OL-DBS while maintaining a comparable TRS. TEEDs are normalized to the duration of the task to avoid bias due to each task length.