Impulse Classification Network for Video Head Impulse Test

Baydadaev, Shokhrukh; Usmankhujaev, Saidrasul; Kwon, Jang-Woo; Kim, Kyu-Sung

doi:10.1109/embc44109.2020.9175969

Cited by 1 publication

(2 citation statements)

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“…In our proposed work, however, we chose optimal hyperparameters for the inception block to keep the training time and accuracy in the optimal trade-off. Instead, in the shortcut module, we applied deeper FCN, used in another study [ 14 ], with slight architectural changes. Implemented FCN consists of six 1D convolutional layers of the same size but with a different kernel length.…”

Section: Methodsmentioning

confidence: 99%

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Time Series Classification with InceptionFCN

Usmankhujaev

Ibrokhimov

Baydadaev

et al. 2021

Sensors

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Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive.

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Section: Methodsmentioning

confidence: 99%

“…Baydadaev et al [ 14 ] proposed to use FCN for the vestibulo–ocular reflex (VOR) custom dataset. VOR data are the fixed-length multivariate time-series data that give information about head and eye velocities.…”

Section: Related Workmentioning

confidence: 99%