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. 2023 Sep 15;23(18):7924. doi: 10.3390/s23187924

Table 1.

Different approaches for apnea detection from SpO2 using DL, arranged by year of publication.

Ref Year DL Model Dataset Window Size (Time) #* Subjects Accuracy % (Best)
Almazaydeh et al. [23] 2012 NN * UCD database [30] - 7 93.3
Morillo et al. [22] 2013 PNN * Private dataset 30 s 115 84
Mostafa et al. [26] 2017 Deep Belief NN with an autoencoder UCD database [30] 1 min 8 and 25 85.26
Pathinarupothi et al. [29] 2017 LSTM *-RNN UCD database [30] 1 min 35 95.5
Cen et al. [32] 2018 CNN * UCD database [30] 1 s - 79.61
Mostafa et al. [33] 2020 CNN Private dataset and UCD database [30] 1, 3 and 5 min - 89.40
John et al. [12] 2021 1D CNN UCD database [30] 1 s 25 89.75
Vaquerizo-Villar et al. [25] 2021 CNN CHAT dataset [31] and 2 private datasets 20 min 3196 83.9
Piorecky et al. [27] 2021 CNN Private dataset 10 s 175 84
Bernardini et al. [28] 2021 LSTM OSASUD [34] 180 s 30 63.3
Li et al. [21] 2021 Artificial neural network (ANN) Private dataset - 148 97.8

* Abbreviations: #, number of; NN, neural network; PNN, Probabilistic neural network; UCD, St. Vincent’s University Hospital/University College Dublin Sleep Apnea; LSTM, long short-term memory; CNN, convolutional neural network.