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A Computationally-Efficient, Online-Learning Algorithm for Detecting High-Voltage Spindles in the Parkinsonian Rats

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Abstract

Abnormally-synchronized, high-voltage spindles (HVSs) are associated with motor deficits in 6-hydroxydopamine-lesioned parkinsonian rats. The non-stationary, spike-and-wave HVSs (5-13 Hz) represent the cardinal parkinsonian state in the local field potentials (LFPs). Although deep brain stimulation (DBS) is an effective treatment for the Parkinson’s disease, continuous stimulation results in cognitive and neuropsychiatric side effects. Therefore, an adaptive stimulator able to stimulate the brain only upon the occurrence of HVSs is demanded. This paper proposes an algorithm not only able to detect the HVSs with low latency but also friendly for hardware realization of an adaptive stimulator. The algorithm is based on autoregressive modeling at interval, whose parameters are learnt online by an adaptive Kalman filter. In the LFPs containing 1131 HVS episodes from different brain regions of four parkinsonian rats, the algorithm detects all HVSs with 100% sensitivity. The algorithm also achieves higher precision (96%) and lower latency (61 ms), while requiring less computation time than the continuous wavelet transform method. As the latency is much shorter than the mean duration of an HVS episode (4.3 s), the proposed algorithm is suitable for realization of a smart neuromodulator for mitigating HVSs effectively by closed-loop DBS.

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Abbreviations

AKF:

Adaptive Kalman filter

AR:

Autoregressive model

cDBS:

Closed-loop deep brain stimulation

CWT:

Continuous wavelet transform

DBS:

Deep brain stimulation

FN:

False negative

FP:

False positive

FPGA:

Field-programmable gate array

HHT:

Hilbert-Huang transform

HVS:

High-voltage spindle

LFP:

Local field potential

ML:

Machine-learning

PACF:

Partial autocorrelation function

PD:

Parkinson’s disease

PSD:

Power spectral density

SNR:

Signal-to-noise ratio

TP:

True positive

TR:

Detection threshold

6-OHDA:

6-Hydroxydopamine

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Acknowledgments

The authors gratefully acknowledge the support from the National Health Research Institute [Grant No. NHRI-EX105-10430NI] and the Ministry of Science and Technology [Grant No. MOST 106-2622-8-007-014 -TA] in Taiwan.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Tsing Hua University, No.101, Sec.2 Kuang-Fu Road, Hsinchu, 30013, Taiwan, R.O.C.

    Ramesh Perumal & Hsin Chen

  2. IBISC, EA 4526, Universite Evry, Universite Paris-Saclay, Saint-Aubin, France

    Vincent Vigneron

  3. DSPCom, FAC/UNICAMP, Limeira, SP, Brazil

    Vincent Vigneron

  4. Institute of Molecular Medicine, National Tsing Hua University, No.101, Sec.2 Kuang-Fu Road, Hsinchu, 30013, Taiwan, R.O.C.

    Chi-Fen Chuang, Yen-Chung Chang & Shih-Rung Yeh

  5. Biopro Scientific, Unit 312, Center of Innovative Incubator, No.101, Sec.2, Kuangfu Rd., East Dist., Hsinchu, 30013, Taiwan, R.O.C.

    Hsin Chen

Authors
  1. Ramesh Perumal
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Correspondence to Hsin Chen.

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Associate Editor Xiaoxiang Zheng oversaw the review of this article.

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In the following, ‘a.u.’ represents arbitrary unit. Vectors and matrices are designated with bold lowercase (\(\varvec{x}\)) and uppercase (\(\varvec{X}\)) letters, respectively. The elements of a matrix \(\varvec{M} = \left\{ {m_{ij} } \right\}\) are indexed by the row index \(i\) and column index\(j\). T denotes the transpose operator. Unbolded lowercase letters refer to values or individual elements in a vector whose position in the vector is indicated by the subscript.

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Perumal, R., Vigneron, V., Chuang, CF. et al. A Computationally-Efficient, Online-Learning Algorithm for Detecting High-Voltage Spindles in the Parkinsonian Rats. Ann Biomed Eng 48, 2809–2820 (2020). https://doi.org/10.1007/s10439-020-02680-0

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