ArticleSensors (Basel, Switzerland)2022
Robust PPG Peak Detection Using Dilated Convolutional Neural Networks.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A multitask Transformer-VAE framework for robust PPG denoising and atrial fibrillation screening.BMC medical imaging · 2026Article
- Detection of cortical arousals in sleep using multimodal wearable sensors and machine learning.Scientific reports · 2025Article
- Synthetic-data-driven LSTM framework for tracing cardiac pulsation in optical signals.Biomedical optics express · 2025Article
- Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones.Sensors (Basel, Switzerland) · 2025Article
- Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study.JMIR formative research · 2025Article
- Circadian rhythm of heart rate and heart rate variability in pregnancy.npj women's health · 2025Article
- Identifying daily-living features related to loneliness: A causal machine learning approach.PloS one · 2025Article
- Emotion Recognition Using PPG Signals of Smartwatch on Purpose of Threat Detection.Sensors (Basel, Switzerland) · 2024Article
- Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life.Scientific reports · 2024Article
- Rolling shutter speckle plethysmography for quantitative cardiovascular monitoring.Biomedical optics express · 2024Article
- A Transferable Lidar-Based Method to Conduct Contactless Assessments of Gait Parameters in Diverse Home-like Environments.Sensors (Basel, Switzerland) · 2024Article
- Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications.PloS one · 2024Article
- A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability.PloS one · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.
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Registered trials
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