Signal denoising methods based on deep learning have been extensively adopted on Electroencephalogram(EEG) devices. However, they are unable to deploy on edge-based portable or wearable (P/W) electronics due to the high computational complexity of the existed models. To overcome such issue, we propose an edge-based lightweight Kalman filter network (EKFNet) that does not require manual prior knowledge estimation.
Approach: Specifically, we construct a multi-scale feature fusion (MSFF) module to capture multi-scale feature information and implicitly compute the prior knowledge. Meanwhile, we design an adaptive gain estimation (AGE) module that incorporates long short-term memory (LSTM) and sequential channel attention module (CAM) to dynamically predict the Kalman gain. Furthermore, we present an optimization strategy utilizing operator fusion and constant folding to reduce the model's computational overhead and memory footprint.
Main results: Experimental results show that the EKFNet reduces the sum of the square of the distances by at least 12% and improves the cosine similarity by at least 2.2% over the state-of-the-art methods. Besides, the model optimization shortens the inference time by approximately 3.3×. The code of our EKFNet is available at https://github.com/cathnat/EKFNet.
Significance: By integrating Kalman filter with deep learning, the approach addresses the parameter-setting challenges in traditional algorithms while reducing computational overhead and memory consumption, which exhibits a good tradeoff between algorithm performance and computing power.
Keywords: Electroencephalography (EEG); Kalman filter; edge AI; signal denoising.
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