Attention-based Learning for Sleep Apnea and Limb Movement Detection using Wi-Fi CSI Signals
Wi-Fi channel state information (CSI) has become a promising solution for non-invasive breathing and body motion monitoring during sleep. Sleep disorders of apnea and periodic limb movement disorder (PLMD) are often unconscious and fatal. The existing researches detect abnormal sleep disorders in im...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
26.03.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Wi-Fi channel state information (CSI) has become a promising solution for
non-invasive breathing and body motion monitoring during sleep. Sleep disorders
of apnea and periodic limb movement disorder (PLMD) are often unconscious and
fatal. The existing researches detect abnormal sleep disorders in impractically
controlled environments. Moreover, it leads to compelling challenges to
classify complex macro- and micro-scales of sleep movements as well as
entangled similar waveforms of cases of apnea and PLMD. In this paper, we
propose the attention-based learning for sleep apnea and limb movement
detection (ALESAL) system that can jointly detect sleep apnea and PLMD under
different sleep postures across a variety of patients. ALESAL contains
antenna-pair and time attention mechanisms for mitigating the impact of modest
antenna pairs and emphasizing the duration of interest, respectively.
Performance results show that our proposed ALESAL system can achieve a weighted
F1-score of 84.33, outperforming the other existing non-attention based methods
of support vector machine and deep multilayer perceptron. |
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DOI: | 10.48550/arxiv.2304.06474 |