Attention-based Learning for Sleep Apnea and Limb Movement Detection using Wi-Fi CSI Signals
This addresses sleep disorder detection for patients in uncontrolled environments, but it is incremental as it builds on prior Wi-Fi CSI work with attention mechanisms.
The paper tackled the problem of detecting sleep apnea and periodic limb movement disorder using Wi-Fi CSI signals, achieving a weighted F1-score of 84.33, which outperformed existing non-attention based methods.
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.