EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation

• URL: http://arxiv.org/abs/2412.17860v1
• Date: Fri, 20 Dec 2024 13:25:50 GMT
• Title: EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation
• Authors: Luca Benfenati, Sofia Belloni, Alessio Burrello, Panagiotis Kasnesis, Xiaying Wang, Luca Benini, Massimo Poncino, Enrico Macii, Daniele Jahier Pagliari,
• Abstract summary: We present Enhance, a method that enhances state-of-the-art models by integrating self-supervised learning with data augmentation. Inspired by a U-Net-like autoencoder architecture, we utilize unsupervised PPG signal reconstruction, taking advantage of large amounts of unlabeled data. We improve the best HR estimation by 12.2%, lowering from 4.03 Beats-Per-Minute (BPM) to 3.54 BPM error on PPG-DaLiA.
• Score: 17.617241860357407
• License:
• Abstract: Heart rate (HR) estimation from photoplethysmography (PPG) signals is a key feature of modern wearable devices for health and wellness monitoring. While deep learning models show promise, their performance relies on the availability of large datasets. We present EnhancePPG, a method that enhances state-of-the-art models by integrating self-supervised learning with data augmentation (DA). Our approach combines self-supervised pre-training with DA, allowing the model to learn more generalizable features, without needing more labelled data. Inspired by a U-Net-like autoencoder architecture, we utilize unsupervised PPG signal reconstruction, taking advantage of large amounts of unlabeled data during the pre-training phase combined with data augmentation, to improve state-of-the-art models' performance. Thanks to our approach and minimal modification to the state-of-the-art model, we improve the best HR estimation by 12.2%, lowering from 4.03 Beats-Per-Minute (BPM) to 3.54 BPM the error on PPG-DaLiA. Importantly, our EnhancePPG approach focuses exclusively on the training of the selected deep learning model, without significantly increasing its inference latency

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