Table 1.

Examples of studies using machine learning algorithms for sleep stage and respiratory scoring

Author, year	Population studied	Dataset source	Channels and sensors	Data preprocess	Classifier used	Performance measures	Other findings
Perslev [87], 2021	15,660 participants	16 clinical datasets	Single channel EEG and EOG sugnal	U Sleep software	Convoluted Neural Network(CNN)	U sleep performed as well as other algorithms, even though U sleep was not trained on similar datasets	It predicts sleep stages in a single forward pass
Sharma [88] 2021	80 subjects comprising of healthy controls as well as various sleep disorders	Cyclic Alternating Pattern (CAP) database	Dual channel EEG	Optimized wavelet filters	Bagged tree (EBT) classifier with tenfold cross validation	Accuracy 85.3% κ = 0.786	Accuracy improved in a balanced dataset created using over-sampling and under-sampling techniques
Sun, 2020 [6]	8682 PSG	N/A	ECG and respiratory signals	270 s time windows used	CNN	κ = 0.585 (sleep stages) κ = 0.76 (wake vs NREM vs REM)	Performance is better for younger ages
Jaoude 2020 [89]	6431 patients	MGH-PSG dataset; Ambulatory scalpEEG dataset	4 EEG channels	Bandpass filter, downsampling to 100 Hz, generated bipolar montage to make it “reference channel-free”	CNN followed by RNN	κ = 0.74 (MGH held out dataset) κ = 0.78 (scalpEEG dataset)	Performance was consistent across common EEG background abnormalities
Zhange et al., 2020 [90]	294 sleep studies (122 training data set, 20 validation dataset, 152 testing data set)	Prospectively collected	2 channel EEG, EOG, EMG, ECG, airflow	Filtered signal at 66 Hz, downsampled signal sampling frequency to 66 Hz	CNN	Accuracy 81.81% κ = 0.7276	Number of arousals affected model’s performance
Peter-Derex 2020 [91]	23 patients with insomnia, 24 patients with idiopathic hypersomnia, 24 patients with narcolepsy, 24 patients with OSA	Lyon sleep database	Single channel EEG	ASEEGA software	ASEEGA software	Agreement between software and consensual scorer was: insomnia 85.5% (κ = 0.80), narcolepsy 83.8% (κ = 0.78), idiopathic hypersomnia 86.1% (κ = 0.68), and obstructive sleep disorder 87.2% (κ = 0.82)
Sridhar, 2020 [92]	800 (561 subjects); 993 nights (993 subjects)	Sleep Heart Health Study; Multi-ethnic study of Atherosclerosis; Physionet Computing in Cardiology (CinC)	ECG	Normalize ECG signal, interbeat interval time series computed	CNN	Accuracy 77% κ = 0.66 (SHHS) Accuracy 72% κ = 0.55 (CinC)
Zhu, 2020 [93]	8 recordings (4 healthy, 4 sleep disorders); 20 recordings (healthy)	Sleep EDF, Sleep EDFX	Single channel EEG	Z score normalization of data	CNN + attention mechanism	Accuracy 93.7% F1 score 84.5%	Attention mechanism helped in learning inter and intra-epoch features
Xu, 2020 [94]	5793 participants (sleep disorders)	Sleep Heart Health Study	Multichannel EEG, EOG	Time frequency spectra	LSTM (Long short term memory)/RNN	Accuracy 87.4% κ = 0.8216	RNNtakes temporal information into account
Zhang et al. Sleep, 2019 [95]	5213 patients	Sleep Heart Health Study	Multichannel EEG, EOG, EMG	Raw signal, short term Fourier transform for spectogram	Recurrent and convolutional neural networks	κ = 0.82 Validation MrOS κ = 0.68 Validation SOF κ = 0.70
Yildrim, 2019 [96]	8 recordings; 61 recordings (healthy and insomnia)	Sleep EDF, Sleep EDFX	Single channel EEG, Single channel EOG	Raw PSG signal	CNN	Accuracy 98.06%
Phan, 2019 [97]	200 recordings	MGH sleep lab	Single channel EEG, EOG, EMG	Time frequency images	CNN + RNN	Accuracy 87% κ = 0.81	Trained network in end to end fashion
Zhang and Wu, 2018 [98]	25 recordings (sleep disorders) 16 recordings	MIT-BIH database, Sleep EDF	Single channel EEG	Phase encoder, unsupervised training	CNN	Accuracy 87% κ = 0.81
Stephansen, 2018 [57]	3000 recordings (healthy and sleep disorders)	10 databases	Multichannel EEG, EOG,	Filter + octave encoding	CNN + RNN	Accuracy = 87%	Automates type 1 narcolepsy diagnosis
Sors et al., 2018 [99]	5793 recordings (sleep disorders)	Sleep Heart Healthy Study	Single channel EEG	raw	CNN	Accuracy = 87% κ = 0.81
Biswal et al. 2018 [50]	1000 recordings 5804 recordings	Sleep Heart Health study; ISRUC-sleep	Multichannel EEG	Spectogram	Recurrent and convolutional neural networks	accuracy 87.5% κ = 0.805 Validation SHHS accuracy 77.7% κ = 0.732	Minimal reduction in accuracy noted when working on single channel
Patanaik et al. Sleep, 2018 [18]	1046 recordings (healthy adolescents) 284 recordings (healthy young adults) 210 recordings(sleep disorders) 77 recordings (Parkinson disease adults)	CNL lab, CSL lab, Singapore; UCSD sleep lab, UC database	Multichannel EEG, EOG,	Spectral images	Convolutional neural network	accuracy 89.2% κ = 0.86 Validation set 1- accuracy 81.4% κ = 0.740 Validation set 2 (PD)- accuracy 72.1% κ = 0.597	Faster compared to human experts (F sec compared to 30–60 min)
Olesen 2018 [7]	2310 recordings (healthy and sick)	CNL lab, Singapore	Multichannel EEG, EOG, EMG	Raw data	CNN	κ = 0.75	Most errors made in stage N1 and N3
Malafeev, 2018 [56]	54 recordings (healthy); 43 recordings (22 PSG and 21 MSLT, hypersomnia patients)	Warsaw (healthy); Wisconsin Sleep Cohort (hypersomnia)	Single channel EEG, EMG, EOG	Spectrogram	CNN	κ = 0.8 (except stage N1)	Performance in healthy subjects were better compared to those on hypersomnia patients
Cui 2018 [46]	116 recordings including healthy and sick population	University of Zurich	Multichannel EEG, EOG, EMG	Time series	CNN	Accuracy = 92.2%
Chambon, 2018 [100]	61 recordings from healthy adults	MGH sleep lab	Multichannel EEG Three chin EMG	Linear spatial filtering	CNN	Sensitivity = 52%	Utilized 1 min of data before and after each data segment which offered the strongest improvement
Vilamala, 2017 [101]	40 recordings from 20 healthy adults	Montreal archive	Single channel EEG	Multitaper spectral analysis	CNN	Acc = 84–88%
Supratak,2017 [55]	62 healthy recordings	MGH sleep lab, Montreal Archive	Single channel EEG	Raw data	CNN + RNN	Acc = 86.2% K = 0.8	Used multiple datasets
Tsinalis, 2016 [42]	40 recordings from 20 Healthy young adults	Montreal archive	Single channel EEG	Class balanced random sampling	CNN and 2D stack of frequency-specific activity in time	Accuracy = 71–76% Per stage Accuracy = 80–84%	Performance balanced across classes

CNN convolutional neural network, RNN recurrent neural network, LSTM long-short-term memory, EBT ensemble bagged tree, Κ kappa