Fig. 1: Pipelines of “old-school” domain feature engineering and “new-school” self-supervised contrastive learning (SSCL) for healthcare wearables. | Communications Engineering

Fig. 1: Pipelines of “old-school” domain feature engineering and “new-school” self-supervised contrastive learning (SSCL) for healthcare wearables.

From: Transforming label-efficient decoding of healthcare wearables with self-supervised learning and “embedded” medical domain expertise

Fig. 1

Take the electrocardiogram (ECG) as an example, visualizations illustrate how different ECG patterns are clustered in each pipeline. a In the “old-school” pipeline, domain features such as heart rate variability (HRV) and P-wave amplitude (P Amp) are extracted from raw electrocardiogram (ECG) signals using biomedical signal processing tools. They can be used to distinguish abnormal samples against normal samples. b In the “new-school” pipeline, two augmented views (Aug 1 and Aug 2) are generated from each ECG input and passed through a shared encoder. The model learns to minimize the distance between the augmented views (positive pair) while maximizing it relative to other samples (negative pairs). ECG electrocardiogram, HRV heart rate variability, P Amp P-wave amplitude.

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