Obstructive sleep apnea (OSA) represents a major health problem. While polysomnography (PSG) remains the gold standard, its resource-intensive nature has encouraged the exploration of other alternative approaches. Most of them were based on heart rate variability (HRV) analysis, but only a few of them have presented a recurrence-based approach. The present paper addresses this gap by integrating convolutional neural networks (CNN) with HRV recurrence analysis. Employing three different and publicly available databases from the official PhysioNet repository (Apnea-ECG, MIT-BIH, and UCD-DB), the presented method was able to expose hidden patterns within the phase-space distance matrix of HRV, which is discernible at an appropriate level of abstraction via CNN.
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#apneaobstructivadelsueño #otorrinonaringologia #obstructivesleepapnea #otorhinolaryngology #aos #neuralnetworks #deeplearning #redesneuronales #aprendizajeprofundo
Link: https://www.mdpi.com/2076-3417…
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