Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligencePilot Tone (PT) respiratory signals are susceptible to interference like patient bulk motion or radio frequency interference. The signal curve is prone to bad when remains strong interference after suppression processing in the learning phase, resulting in low triggering accuracy and efficiency. With real-time quality assessment in the learning phase, a good-quality signal can be selected to learn better processing parameters. To ensure robustness and inference time, a tiny CNN is utilized to classify the signal into good and bad quality. Experimental results demonstrate that the proposed method has a strong ability to assess the quality of PT respiratory signals.
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