Key result
Autoencoder-based deep learning boosts sensor signal classification accuracy by ~8% versus existing feature-based methods.
Why the study?
Physiological sensor data used for remote patient monitoring is often affected by measurement noise, and existing feature-based models may not capture full signal characteristics for cleaning.
A novel autoencoder-based semi-supervised deep learning framework improves the accuracy of physiological sensor data cleaning to over 90%, outperforming traditional feature-based methods.
May aid remote vital-signal monitoring via improved sensor cleaning; leaves open prospective clinical validation.
Objective. Physiological sensor data (e.g. photoplethysmograph) is important for remotely monitoring patients’ vital signals, but is often affected by measurement noise. Existing feature-based models for signal cleaning can be limited as they might not capture the full signal characteristics. Approach. In this work we present a deep learning framework for sensor signal cleaning based on dilated convolutions which capture the coarse- and fine-grained structure in order to classify whether a signal is noisy or clean. However, since obtaining annotated physiological data is costly and time-consuming we propose an autoencoder-based semi-supervised model which is able to learn a representation of the sensor signal characteristics, also adding an element of interpretability. Main results. Our proposed models are over 8% more accurate than existing feature-based approaches with half the false positive/negative rates. Finally, we show that with careful tuning (that can be improved further), the semi-supervised model outperforms supervised approaches suggesting that incorporating the large amounts of available unlabeled data can be advantageous for achieving high accuracy (over 90%) and minimizing the false positive/negative rates. Significance. Our approach enables us to reliably separate clean from noisy physiological sensor signal that can pave the development of reliable features and eventually support decisions regarding drug efficacy in clinical trials.
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Kriara et al. (2023) studied Physiological sensor data measurement noise. Autoencoder-based semi-supervised deep learning framework vs. Existing feature-based approaches and supervised approaches was evaluated on Signal classification accuracy (noisy vs clean) (over 8% more accurate). An autoencoder-based semi-supervised deep learning framework for sensor signal cleaning was over 8% more accurate than existing feature-based approaches, achieving over 90% accuracy.
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