Introduction to polysomnography

Gupta, Ravi; Pandi-Perumal, Seithikurippu R.; BaHammam, Ahmed S.

doi:10.1201/b22464-3

Cited by 1 publication

(4 citation statements)

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“…Although the amount of data in this database is sufficient, we still think that additional experiments on another dataset could be useful. In addition, although models based on SpO2 only to detect OSA are common in the DL literature, sleep apnea is directly related to respiration, and AASM only approved a minimum of 3 channels for HSAT [ 4 ]. Therefore, the reliability of only SpO2 for OSA detection needs further investigation to be clinically approved.…”

Section: Discussionmentioning

confidence: 99%

“…It is best to start determining the optimal sequence length using domain knowledge. For example, if we are trying to predict OSA from the SpO2 time series, we know that the patterns in the PSG data are typically scored by sleep technologists using a two-minute time window [ 4 ]. In this case, you would want to choose a sequence length that is around this value.…”

Section: Methodsmentioning

confidence: 99%

“…Polysomnography (PSG) is considered the gold-standard diagnostic test for OSA, where the patient undergoes neuro-muscular-cardio-respiratory monitoring in the sleep laboratory [ 4 ]. PSG can be a full-night diagnostic study followed by a full-night therapeutic study or split-night sleep study, where the first half is diagnostic and the second half is therapeutic.…”

Section: Introductionmentioning

confidence: 99%

“…PSG is expressed as epochs of raw data, where each epoch is 30 s ( Figure 1 ). A record of 8 h translates to around 900 pages (epochs) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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Adoption of Transformer Neural Network to Improve the Diagnostic Performance of Oximetry for Obstructive Sleep Apnea

Almarshad,

Al-Ahmadi,

Islam

et al. 2023

Sensors

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Scoring polysomnography for obstructive sleep apnea diagnosis is a laborious, long, and costly process. Machine learning approaches, such as deep neural networks, can reduce scoring time and costs. However, most methods require prior filtering and preprocessing of the raw signal. Our work presents a novel method for diagnosing obstructive sleep apnea using a transformer neural network with learnable positional encoding, which outperforms existing state-of-the-art solutions. This approach has the potential to improve the diagnostic performance of oximetry for obstructive sleep apnea and reduce the time and costs associated with traditional polysomnography. Contrary to existing approaches, our approach performs annotations at one-second granularity. Allowing physicians to interpret the model’s outcome. In addition, we tested different positional encoding designs as the first layer of the model, and the best results were achieved using a learnable positional encoding based on an autoencoder with structural novelty. In addition, we tried different temporal resolutions with various granularity levels from 1 to 360 s. All experiments were carried out on an independent test set from the public OSASUD dataset and showed that our approach outperforms current state-of-the-art solutions with a satisfactory AUC of 0.89, accuracy of 0.80, and F1-score of 0.79.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…PSG is expressed as epochs of raw data, where each epoch is 30 s ( Figure 1 ). A record of 8 h translates to around 900 pages (epochs) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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