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ApSense: Data-Driven Algorithm in PPG-Based Sleep Apnea Sensing
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DOI：10.1109/JIOT.2024.3433500.png)
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Abstract
En 中文
Detecting obstructive sleep apnea (OSA) is essential for diagnosing and managing sleep health. Traditionally, this involves clinical settings with hardly accessible processes. We propose that the automated detection of OSA events is achievable using features extracted from fingertip photoplethysmography (PPG) signals combined with modern deep learning (DL) techniques. Utilizing two benchmark data sets with extensive PPG recordings, we introduce ApSense, a DL model designed for the OSA event onset recognition from PPG features. ApSense presents a custom neural architecture and domain-specific feature extraction from PPG waveforms. We benchmark it against the state-of-the-art (SOTA) algorithms, including RRWaveNet, PPGNetSA, AIOSA, DRIVEN, and LeNet-5. In our evaluations, ApSense demonstrated improved sensitivity, specificity, and area under the receiver operating characteristic (AUROC) on the test data sets. Furthermore, an ablation study highlighted strategic customizations of ApSense, enhancing its performance and adaptability to different data sets. ApSense demonstrates high reliability, as its outstanding results were confirmed even in high-variance data sets. By detecting OSA events, ApSense enables the estimation of the predicted apnea-hypopnea index (pAHI), which can be used for prescreening individuals for sleep apnea in a low-cost setup. ApSense shows the potential for the PPG-based OSA detection and clinical applications for prescreening in the future.
Keywords:
Sleep apnea
Feature extraction
Internet of Things
Benchmark testing
Sensors
Wrist
Heart beat
Deep learning (DL)
obstructive sleep apnea (OSA)
photoplethysmography (PPG)
pulse wave
wearable devices
Journal
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8.9
Papers:
1.4W
Citations:
7.8W
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Cited Papers
Towards automatic home-based sleep apnea estimation using deep learning.png)
NPJ DIGITAL MEDICINE
OAAI
NPJ DIGITAL MEDICINEIF15.1
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