Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage.

This paper proposes a novel mathematical model that quantitatively defines the relationships between the arterial oxygen saturation (SaO2), the arterial partial pressure of oxygen (PaO2) and the arterial partial pressure of carbon dioxide (PaCO2). A novel non-contact algorithm that utilizes the mathematical model, multilayer perceptron (MLP) neural network and microwave Doppler radar to translate the human periodic chest displacements caused by respiratory efforts into peripheral capillary oxygen saturation (SpO2) measurements is also proposed. A database consisting of 20 obstructive sleep apnea (OSA) and chronic heart failure (CHF) patients, with total sleep duration of 155 hours, 1 minute and 30 seconds, is selected for the “Training”, “Validation” and independent “Test” of the SpO2 predictions. For independent “Test”dataset with total sleep duration of 54 hours, 15 minutes and 31.5 seconds, the SpO2 predictions correlation coefficient achieved 0.93 with sum squared error (SSE) of 1.3 (% of oxygen saturation) and the 95limits of agreement of ±2.5 (% of oxygen saturation). These results demonstrate and proven the feasibility of non-contact SpO2 predictions using microwave Doppler radar in the complexity of sleep environment. A potential application could be non-contact sleep monitoring of oxygen saturation in the home.

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.

Long-term continuous patient monitoring is required in many health systems for monitoring and analytical diagnosing purposes. It has been recognized that these types of monitoring systems have shortcomings related to patient comfort and/or functionality. Non-contact monitoring systems have been developed to address some of these shortcomings. One of such systems is non-contact physiological vital signs assessments for Chronic Heart Failure (CHF) patients. This paper presents a novel pulmonary ventilation model that defines the relationship between the intrapulmonary pressure and the chest displacement. A novel intrapulmonary pressure and tidal volume estimation algorithm is also proposed. A database consisting of twenty CHF patients with New York Heart Association (NYHA) Heart Failure Classification Class II & III; whose underwent full Polysomnography (PSG) analysis for diagnosis of sleep apnea, disordered sleep, or both, was selected for the verification of the proposed model and algorithm. The proposed algorithm analyzes the non-contact sensor data and estimate the patient's intrapulmonary pressure and tidal volume. The output of the algorithm is compared with the gold-standard PSG recordings. Across all twenty CHF patients' recordings with mean recorded sleep duration of 7.76 hours, the tidal volume estimation median accuracy achieved 83.13% with a median error of 57.32 milliliters. A potential application would be non-contact continuous monitoring of intrapulmonary pressure and tidal volume during sleep in the home.

Long term continuous patient monitoring is required in many health systems for monitoring and analytical diagnosing purposes. Most of monitoring systems had shortcomings related to their functionality or patient comfortably. Non-contact continuous monitoring systems have been developed to address some of these shortcomings. One of such systems is non-contact physiological vital signs assessments for chronic heart failure (CHF) patients. This paper presents a novel automated estimation algorithm for the non-contact physiological vital signs assessments for CHF patients based on a patented novel non-contact biomotion sensor. A database consists of twenty CHF patients with New York Heart Association (NYHA) heart failure Classification Class II & III, whose underwent full Polysomnography (PSG) analysis for the diagnosis of sleep apnea, disordered sleep, or both, were selected for the study. The patients mean age is 68.89 years, with mean body weight of 86.87 kg, mean BMI of 28.83 (obesity) and mean recorded sleep duration of 7.78 hours. The propose algorithm analyze the non-contact biomotion signals and estimate the patients' respiratory and heart rates. The outputs of the algorithm are compared with gold-standard PSG recordings. Across all twenty patients' recordings, the respiratory rate estimation median accuracy achieved 92.4689% with median error of ±1.2398 breaths per minute. The heart rate estimation median accuracy achieved 88.0654% with median error of ±7.9338 beats per minute. Due to the good performance of the propose novel automated estimation algorithm, the patented novel non-contact biomotion sensor can be an excellent tool for long term continuous sleep monitoring for CHF patients in the home environment in an ultra-convenient fashion.

Long-term continuous patient monitoring is required in many health systems for monitoring and analytical diagnosing purposes. Most of monitoring systems have shortcomings related to their functionality and/or patient comfortably. Non-contact monitoring systems have been developed to address some of these shortcomings. One of such systems is non-contact physiological vital signs assessments for chronic heart failure (CHF) patients. This paper presents novel real-time demodulation technique and estimations algorithms for the non-contact physiological vital signs assessments for CHF patients based on a patented novel non-contact bio-motion sensor. A database consists of twenty CHF patients with New York Heart Association (NYHA) Heart Failure Classification Class II & III, whose underwent full Polysomnography (PSG) analysis for the diagnosis of sleep apnea, disordered sleep, or both, were selected for the study. The propose algorithms analyze the non-contact bio-motion signals and estimate the patient's respiratory and heart rates. The outputs of the algorithms are compared with gold-standard PSG recordings. Across all twenty CHF patients’ recordings, the respiratory rate estimation median accuracy achieved 91.52% with median error of ±1.31 breaths per minute. The heart rate estimation median accuracy achieved 91.29% with median error of ±6.16 beats per minute. A potential application would be home continuous sleep and circadian rhythm monitoring.