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Table 2 An overview of HR tracking techniques

From: Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review

Tracker Classifier Tracking method HR Stages Specialty Error1 Error2
TROIKA [65], 2015 and JOSS [66],2015 SDT Initialization, peak selection, peak verification, and Smoother algo. (JOSS) Search areas for F0 and F1 in PPG spectra 2.42 and 1.28 1.82 and 1.01
CC [51], 2015 SDT Heuristic Two spectral peaks in PPG and considers direction and direction of error 1.83 1.4
Emroz et al. [52], 2016 SDT Comparison of heart rate from multiple (4) sources Building of HR space, comparison of two sources for HRC at a time, and fine tuning 1.02
SpaMa [80], 2015 and SpaMa Plus [86], 2018 CNN SST Heuristic and trained CNN using 4 channel images converted from signals Downsampling of PPG and ACC. Three spectral peaks in PPG and ACC and iterative selection of spectral peak and end-to-end learning 1.93 and 3.56 2.07 and –
CARMA [85], 2015 SST Heart rate modeling using prior knowledge Downsampling of PPG and ACC. Three spectral peaks in PPG and ACC and iterative selection of spectral peak 2.26 3.63
HSUM [69], 2018 SST PPG and ACC modeling using HSUM Automatic detection of heart rate after motion modeling 0.74 0.83
Schack et al. [70], 2017 SPT Maximization of cost function Downsampling of PPG and ACC signals, cross correlation, and Gaussian filtering, cost function-based tracking 1.32
Torres et al. [87], 2016 SPT HR selection, verification, and BW adjustment Computationally less expensive hence suited for real-time application 1.36 1.05
Timko et al. [76], 2015 SPT Phase Vocoder & HR history tracking and smoothing A few parameters to tune hence is useful in real time 1.05 2.23
Galli et al. [61], 2018 SPT Prediction, estimation, and update Use of KF to calculate heart rates from DFT 2.45 2.21
Rocha et al. [96], 2020 DNN MLT Two layers of binary CNN and binary LSTM FPGA implementation of binary Cornet 3.75
Zhu et al. [71], 2019 NN MLT NN modeling, smoothing, and linear regression Polynomial approximation of false heart rate 1.03 0.79
Roy et al. [101], 2018 MLP-ANN MLT Trained NN using features generated by auto encoder from PCA Generate HR template using clean PPG 1.47 1.1
Biswas et al. [20], 2019 DNN MLT CNN extracted features used to LSTM Two layers of CNN and LSTM 1.96 1.47
Sun et al. [74], 2016 SVM MLT SVM trained on peak amplitude and peak-to-peak separation 1.78 1.57