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

• Published in: Annals of biomedical engineering 2020-12, Vol.48 (12), p.2809-2820
• Main Authors: Perumal, Ramesh, Vigneron, Vincent, Chuang, Chi-Fen, Chang, Yen-Chung, Yeh, Shih-Rung, Chen, Hsin
• Format: Article
• Language: English
• Subjects: 6-Hydroxydopamine; Adaptive algorithms; Adaptive filters; Algorithms; Autoregressive models; Basal ganglia; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Brain; Central nervous system diseases; Classical Mechanics; Cognitive ability; Continuous wavelet transform; Deep brain stimulation; Engineering Sciences; High voltages; Kalman filters; Latency; Machine learning; Mental disorders; Movement disorders; Neurodegenerative diseases; Neuromodulation; Original Article; Parkinson's disease; Side effects; Signal and Image processing; Spindles; Stimulation; Stimulators; Voltage; Wavelet transforms
• ISSN: 0090-6964; 1573-9686
• DOI: 10.1007/s10439-020-02680-0

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.