AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning

A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification

“…For this sake, considerable efforts have been made to reduce the number of the required signals in a large volume of publications. [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] For example, the OSA severity was classified using ECG [7][8][9][10][11][12][13][14][15][16][17][18] or SpO 2 signals alone, [19][20][21] using a combination of ECG and SpO 2 signals, [22] or even using multiple types of signals, i.e., ECG, SpO 2 , chest, and abdominal respiratory movement signals. [23] Recently, wearable and portable tools at home for OSA classification have been developed as an easy-to-use alternative to PSG.…”

Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients

“…In another study, Mostafa et al [82] reached an accuracy, sensitivity, and specificity of 95.14%, 92.36%, and 97.08%, respectively, with a greedy based convolutional Neural Network. Bernardini et al [135] proposed a convolutional deep learning architecture to reduce the temporal resolution of raw waveform data and reached an accuracy, sensitivity, and specificity of 93.60%, 91.20%, and 95.10%. Finally, Sharma et al [136] decomposed the SpO 2 signals into various sub-bands (SBs) and extracted Shannon entropy features (Acc.…”

Artificial Intelligence in Healthcare and Biotechnology: A Review of the Saudi Experience

“…DL algorithms are heavily being researched and explored in electroencephalography [EEG] with the aim of predicting epileptic seizures and preventing them [14], in electrocardiography [ECG] with the aim of diagnosing and predicting a wide array of cardiac pathologies [15], and in gastroenterology with the aim of extrapolating the efficacy and safety of an expensive drug with little to no cost [16]. Moreover, an Italian study investigated the utility of a DL tool in the prediction of obstructive sleep apnea [OSA] events [17]. This tool was shown to have the potential to allow clinicians to predict the occurrence of OSA events easily and effectively in stroke patients for whom OSA carries great morbidity and mortality [17].…”

“…Moreover, an Italian study investigated the utility of a DL tool in the prediction of obstructive sleep apnea [OSA] events [17]. This tool was shown to have the potential to allow clinicians to predict the occurrence of OSA events easily and effectively in stroke patients for whom OSA carries great morbidity and mortality [17]. It utilizes convolutional neural networks.…”