Vital signs, such as body temperature, heart rate, respiratory rate, and blood pressure are standard indicators of an individual’s physiological functions in most medical settings.¹ Monitoring these vital parameters is crucial for early diagnosis, medical treatment, risk assessment, and patient recovery monitoring.^2,3 With the advancement of medical measurement technology, oxygen saturation has increasingly become recognized as an indispensable fifth vital sign.⁴ Oxygen saturation indicates the percentage of oxygenated hemoglobin (${\mathrm{HbO}}_2$) and hemoglobin (Hb) in the blood, in which the artery should be in the range of 95% to 100% in healthy individuals.⁵ Many pulmonary diseases cause abnormalities in oxygen saturation values, such as acute pneumonia, chronic obstructive pulmonary disease (COPD), and sleep apnea syndrome (SAS). Furthermore, the outbreak of coronavirus (COVID-19) has further underscored the critical importance of oxygen saturation measurement.

Many countries around the world face a shortage of medical personnel, leading to work overload or even burnout. This calls for political and scientific solutions to relieve the medical personnel. The measurement of vital signs in hospitals is still predominately carried out manually with traditional contact-based methods, taking over a substantial share of the medical personnel’s workload. The introduction of contactless methods for vital sign monitoring (e.g., with a camera) has great potential to relieve the medical personnel. This systematic review’s objective is to analyze the state of the art in the field of contactless optical patient diagnosis. This review distinguishes itself from already existing reviews by considering studies that do not only propose the contactless measurement of vital signs but also include an automatic diagnosis of the patient’s condition. This means that the included studies incorporate the physician’s reasoning and evaluation of vital signs into their algorithms, allowing an automated patient diagnosis. The literature screening of two independent reviewers resulted in a total of five eligible studies. The highest number of studies (three) introduce methods for the risk assessment of infectious diseases, one study introduces a method for the risk assessment of cardiovascular diseases, and one study introduces a method for the diagnosis of obstructive sleep apnea. Overall, high heterogeneity in relevant study parameters is reported among the included studies. The low number of included studies indicates a large research gap and emphasizes the demand for further research on this emerging topic.

Sleep apnea syndrome (SAS) is a sleep-related breathing disorder characterized by repetitive breathing interruptions during sleep, resulting in daytime drowsiness, concentration difficulties, and increased risk of cardiovascular diseases. SAS is diagnosed in specialized sleep laboratories via polysomnography (PSG). PSG involves a high number of contact-based sensors, and it may cause patient discomfort and bias in measurement results. Therefore, contactless alternatives to PSG are a promising way to overcome these issues. This work introduces a novel featurebased method for detecting and classifying apneic events in terms of event amplitude (apneas and hypopneas) and event source (obstructive and central) by using multispectral imaging in the near-infrared (NIR) and far-infrared (FIR) spectra. In the NIR spectrum, remote photoplethysmography signals at 780 and 940 nm are extracted, while in the FIR spectrum, a respiratory airflow signal is extracted. The method is based on the extraction of explainable and medically significant features and the fusion of multiple data modalities (multispectral images, demographic patient data, interspectral correlation analysis, and time-series analysis). The classification accuracy between normal breathing, hypopneas, and apneas is 99.5%, while the differentiation between obstructive and central apneas achieves an accuracy of 98.8%. The estimations of the apnea-hypopnea index (AHI), obstructive apnea index (oAI), and central apnea index (cAI) result in a Pearson correlation of 0.9981, 0.9989, and 0.9950 respectively. A correct SAS stage prediction for 19 symptomatic patients in our dataset is accomplished. The results show that this method may be used as a PSG substitute for apnea and hypopnea detection and classification.

Sleep apnea syndrome (SAS) is a common sleeprelated breathing disorder characterized by recurring cessations of airflow during sleep. The gold standard for diagnosing SAS is polysomnography (PSG) which requires the patient to spend one or several nights in a sleep clinic. A PSG involves a significant amount of contact-based sensors, which leads to discomfort and deviations in sleep behavior. In this work, a contactless, multispectral camera-based approach for the autonomous detection of events of nocturnal airflow reduction is presented. The detected events are further employed in estimators of sleep diagnostic metrics, such as the apnea-hypopnea index (AHI) and the SAS stage. The AHI estimation resulted in a Pearson correlation coefficient of r = 0.9993. The SAS stage estimator correctly predicted the SAS stage for all three recruited patients.

In this paper a novel approach to measure currents noninvasively in a multi-conductor cable with conductor level resolution is presented. The measurement is based on an evaluation of the magnetic field outside the cable. A sensor-device based on this approach can be installed without removing any part of the isolation, separating the conductors inside the cable or even turning of connected electrical consumers. It is therefore considered noninvasive. Based on this approach a sensor-device is developed and tested. A maximum measurement deviation of 4 percent is achieved.