A non-contact Doppler radar system using a deep neural network achieved a 99.2% correct classification rate for predicting nocturnal body orientations in chronic heart failure patients.
Does non-contact Doppler radar using a deep neural network accurately predict nocturnal body orientations in chronic heart failure patients?
A non-contact Doppler radar system combined with a deep neural network can accurately predict nocturnal body orientations in patients with chronic heart failure, offering a potential alternative to cumbersome contact-based sleep monitoring.
Sleep is crucial in our daily life as it plays a key role in our physical and mental health. It is important to monitor the sleep body orientations and movements due to its relationships to particular diseases, e.g., obstructive sleep apnea, insomnia or periodic limb movement disorder. Analyzing sleep body orientations also helps in determining sleep quality and irregular sleeping patterns. However, the current non-invasive sleep body orientations monitoring technologies are not well suited for long-term continuous monitoring due to its restrictions in mobility and comfort. This paper proposes a system that applies a features extraction process, utilizing wavelet packet decomposition, to extract features that describe the non-contact Doppler radar signatures caused by the body orientations. A database consisting of 24 chronic heart failure patients is selected for the training, validation and test of the non-contact body orientations prediction. These patients are diagnosed with New York Heart Association heart failure classification Class II & III and underwent full polysomnography analysis for the diagnosis of sleep apnea, disordered sleep, or both. The patients' data are randomly concatenated and partitioned into the ratio of 50% for `Training', 15% for `Validation' and 35% for `Test. Across the `Test dataset with total sleep duration of 65 hours, the body orientations prediction accuracy achieved a correct classification rate of 99.2% for 5 classes of `Prone', `Upright', `Supine', `Right and `Left body orientations. The misclassification rate is 0.8%. A potential application would be non-contact continuous monitoring of nocturnal body orientations in the home.
Tran et al. (Wed,) conducted a other in Chronic heart failure (n=24). Non-contact Doppler radar with deep neural network was evaluated on Body orientations prediction accuracy (correct classification rate). A non-contact Doppler radar system using a deep neural network achieved a 99.2% correct classification rate for predicting nocturnal body orientations in chronic heart failure patients.