Paper Info
Title
Hybrid CNN-LSTM Network for Real-Time Apnea-Hypopnea Event Detection Based on IR-UWB Radar
Abstract
Polysomnography (PSG) is the gold-standard for sleep apnea and hypopnea syndrome (SAHS) diagnosis. Because the PSG system is not suitable for long-term continuous use owing to the high cost and discomfort caused by attached multi-channel sensors, alternative methods using a non-contact sensor have been investigated. However, the existing methods have limitations in that the radar-person distance is fixed, and the detected apnea hypopnea (AH) event cannot be provided in real-time. In this paper, therefore, we propose a novel approach for real-time AH event detection with impulse-radio ultra-wideband (IR-UWB) radar using a deep learning model. 36 PSG recordings and simultaneously measured IR-UWB radar data were used in the experiments. After the clutter was removed, IR-UWB radar images were segmented by sliding a 20-s window at 1-s shift, and categorized into two classes: AH and N. A hybrid model combining the convolutional neural networks and long short-term memory networks was trained with the data, which consisted of class-balanced segments. Time sequenced classified outputs were then fed to an event detector to identify valid AH events. Therefore, the proposed method showed a Cohen's kappa coefficient of 0.728, sensitivity of 0.781, specificity of 0.956, and an accuracy of 0.930. According to the apnea-hypopnea index (AHI) estimation analysis, the Pearson's correlation coefficient between the estimated AHI and reference AHI was 0.97. In addition, the average accuracy and kappa of SAHS diagnosis was 0.98 and 0.96, respectively, for AHI cutoffs of >= 5, 15, and 30 events/h. The proposed method achieved the state-of-the-art performance for classifying SAHS severity without any hand-engineered feature regardless of the user's location. Our approach can be utilized for a cost-effective and reliable SAHS monitoring system in a home environment.
Year
DOI
Venue
2022
10.1109/ACCESS.2021.3081747
IEEE ACCESS
Keywords
DocType
Volume
Radar, Sleep apnea, Sensors, Radar detection, Monitoring, Real-time systems, Indexes, Convolutional neural network, impulse-radio ultra-wideband, long short-term memory network, non-contact monitoring, sleep apnea and hypopnea syndrome
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Hyun Bin Kwon102.03
Dongyeon Son200.68
Dongseok Lee321.74
Heenam Yoon401.01
Mi Hyun Lee501.35
Yu Jin Lee600.68
Sang Ho Choi702.03
Kwang Suk Park800.34