Study Objectives: To validate a contact-free system designed to achieve maximal comfort during long-term sleep monitoring, together with high monitoring accuracy. Methods: We used a contact-free monitoring system (EarlySense, Ltd., Israel), comprising an under-the-mattress piezoelectric sensor and a smartphone application, to collect vital signs and analyze sleep. Heart rate (HR), respiratory rate (RR), body movement, and calculated sleep-related parameters from the EarlySense (ES) sensor were compared to data simultaneously generated by the gold standard, polysomnography (PSG). Subjects in the sleep laboratory underwent overnight technician-attended full PSG, whereas subjects at home were recorded for 1 to 3 nights with portable partial PSG devices. Data were compared epoch by epoch. Results: A total of 63 subjects (85 nights) were recorded under a variety of sleep conditions. Compared to PSG, the contact-free system showed similar values for average total sleep time (TST), % wake, % rapid eye movement, and % non-rapid eye movement sleep, with 96.1% and 93.3% accuracy of continuous measurement of HR and RR, respectively. We found a linear correlation between TST measured by the sensor and TST determined by PSG, with a coefficient of 0.98 (R = 0.87). Epoch-by-epoch comparison with PSG in the sleep laboratory setting revealed that the system showed sleep detection sensitivity, specificity, and accuracy of 92.5%, 80.4%, and 90.5%, respectively. Conclusions: TST estimates with the contact-free sleep monitoring system were closely correlated with the gold-standard reference. This system shows good sleep staging capability with improved performance over accelerometer-based apps, and collects additional physiological information on heart rate and respiratory rate.

BACKGROUND:Continuous vital sign monitoring has the potential to detect early clinical deterioration. While commonly employed in the intensive care unit (ICU), accurate and noninvasive monitoring technology suitable for floor patients has yet to be used reliably.OBJECTIVE:To establish the accuracy of the Earlysense continuous monitoring system in predicting clinical deterioration.DESIGN:Noninterventional prospective study with retrospective data analysis.SETTING:Two medical wards in 2 academic medical centers.PATIENTS:Patients admitted to a medical ward with a diagnosis of an acute respiratory condition.INTERVENTION:Enrolled patients were monitored for heart rate (HR) and respiration rate (RR) by the Earlysense monitor with the alerts turned off.MEASUREMENTS:Retrospective analysis of vital sign data was performed on a derivation cohort to identify optimal cutoffs for threshold and 24‐hour trend alerts. This was internally validated through correlation with clinical events recognized through chart review.RESULTS:Of 113 patients included in the study, 9 suffered major clinical deterioration. Alerts were found to be infrequent (2.7 and 0.2 alerts per patient‐day for threshold and trend alert, respectively). For the threshold alerts, sensitivity and specificity in predicting deterioration was found to be 82% and 67%, respectively, for HR and 64% and 81%, respectively, for RR. For trend alerts, sensitivity and specificity were 78% and 90% for HR, and 100% and 64% for RR, respectively.CONCLUSIONS:The Earlysense monitor was able to continuously measure RR and HR, providing low alert frequency. The current study provides data supporting the ability of this system to accurately predict patient deterioration. Journal of Hospital Medicine 2012; © 2012 Society of Hospital Medicine

The high correlation between the EverOn motion score and the calculated Norton scale indicates the potential of this technology to serve as a risk assessment tool for the development of PUs.

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We found no difference between values of wakefulness, sleep, NREM, REM sleep, and RDI calculated from manually scored PSG recordings with those derived through analyses of HRV.

Changes in body position alter the relative angle between ECG electrodes and the mean electric axis of the heart. These changes influence the time interval during which the projection of the electric dipole, on any ECG lead, is positive (R-wave). In this study, measurements of R-wave duration (RWD) were used to identify changes in body position, and two of its uncorrelated features were used to classify each heartbeat into four basic groups relating to four body positions (supine, prone, left-side, right-side). Data were acquired from healthy volunteers during controlled condition experiments that included well-defined sequences of body positions and simultaneous recordings of ECG leads I, II and III. Results showed over 90% correct identifications of body position changes when using any of the three leads. Lead II had the best performance for the classification of body position and correctly classified 80% of heartbeats. Classification did not improve for a combination of two leads. The technique can be used to reveal additional important clinical information and can be easily implemented, in a variety of applications where ECG is recorded, such as sleep studies, Holter recordings and ischaemia detection.

In heart rate monitoring, the Electrocardiogram