On the development of an efficient, low-complexity and highly reproducible method for systolic peak detection

Prada, Erick Javier Argüello; Gallego, Carlos Alberto Bravo; García, Javier Ferney Castillo

doi:10.1016/j.bspc.2021.102606

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…Van Gent [36,37] presented a method based on an adaptive threshold followed by moving average and spline interpolation methods if the detected peaks show clipping. In [38], a derivative-based method was proposed equipped with a slope sum function and an adaptive threshold to mitigate false peak detection. The complexity of the adaptive threshold PPG peak detection methods is low.…”

Section: Adaptive Thresholdmentioning

confidence: 99%

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Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

Kazemi

Laitala

Azimi

et al. 2022

Sensors

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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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Section: Adaptive Thresholdmentioning

confidence: 99%

“…In [41], PPG peaks were automatically detected and corrected, exploiting a Poincare plot feature and envelope detection. In [38], a window-based approach was introduced for peak detection. The peak location was determined by sliding a certain window over PPG signal.…”

Section: Transform-based Techniquesmentioning

confidence: 99%