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A fast and efficient algorithm for multi-channel transcranial magnetic stimulation (TMS) signal denoising

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Abstract

TMS signal denoising is crucial for 264-channel TMS high-performance magnetic field detection system application, which can be considered as a problem of obtaining an optimal solution to the desired clean signal. In order to efficiently suppress the noise, an improved generalized morphological filtering (IGMF) algorithm based on adaptive framing is proposed. Firstly, the framing points are calculated by the adaptive framing algorithm, and multiple signal segments are obtained by the framing points. Then, the IGMF algorithm is used to filter the signal segments. Finally, the filtered signal segments are merged into TMS signals. The performance of our algorithm is evaluated using the SNR, RMSE, and MAE. Experiments show that the results of the proposed algorithm on three evaluation indicators are superior to others. And the running time of the algorithm is only 2.88 ~ 37.87% of others. Therefore, the proposed algorithm can efficiently denoise TMS signals and has advantages in fast processing of multi-channel signals.

Graphical abstract

The improved generalized morphological filtering(IGMF) algorithm based on adaptive framing algorithm is used to process 264-channel signals, which achieves signal denoising through a series of operations. The flowchart and result of this algorithm are shown in Fig. 1.

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Funding

This work was supported by the Nation Natural Science Foundation of China (No.62071329) and the Natural Science Foundation Applying System of Tianjin (No.18JCYBJC90400 and NO. 18JCQNJC84000).

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Authors and Affiliations

  1. The School of Control Science and Engineering, Tiangong University, Tianjin, 300387, People’s Republic of China

    Jinzhen Liu, Kaiwen Tian & Hui Xiong

  2. Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin, 300387, People’s Republic of China

    Jinzhen Liu, Kaiwen Tian & Hui Xiong

  3. The School of Life Sciences, Tiangong University, Tianjin, 300387, People’s Republic of China

    Yu Zheng

Authors
  1. Jinzhen Liu
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  2. Kaiwen Tian
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  3. Hui Xiong
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  4. Yu Zheng
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Contributions

Jinzhen Liu: conceptualization. Kaiwen Tian: data curation, writing—original draft preparation. Hui Xiong: visualization and investigation. Yu Zheng: investigation.

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Correspondence to Hui Xiong.

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Liu, J., Tian, K., Xiong, H. et al. A fast and efficient algorithm for multi-channel transcranial magnetic stimulation (TMS) signal denoising. Med Biol Eng Comput 60, 2479–2492 (2022). https://doi.org/10.1007/s11517-022-02616-x

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  • DOI: https://doi.org/10.1007/s11517-022-02616-x

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