Andreas Brauers

Sleep Monitoring Through a Textile Recording System

In this paper, we present a home device for the continuous monitoring of sleep and investigate its reliability regarding sleep evaluation. The system has been particularly designed for healthy people and for preventive purposes. It is not obtrusive and therefore can be used every night without impeding sleep in itself and without interfering with the normal way of life. The signal used for sleep evaluation is the HRV derived from the ECG recorded by means of a sheet and a pillow. Patients in a sleep lab and healthy subjects at home were monitored during sleep with the textile system, while also standard ECG and respiration were recorded. For the textile ECG sensor, coverage of the signal on a beat-to-beat basis ranged from 47,9 - 95,8% of the overall night for the healthy subjects, with a mean coverage of 81,8%. In the group of sleep laboratory patients, the mean coverage was lower - 64,4% - although even in this group the coverage of a single night ranged up to 98.4%. After frequency analysis, the spectral parameters used for sleep staging and derived at the same time from standard and textile ECG signals were compared. The trends along the night are very similar, indicating the possibility of using textile HRV for sleep evaluation.

Applying machine learning to detect individual heart beats in ballistocardiograms

Ballistocardiography is a technique in which the mechanical activity of the heart is recorded. We present a novel algorithm for the detection of individual heart beats in ballistocardiograms (BCGs). In a training step, unsupervised learning techniques are used to identify the shape of a single heart beat in the BCG. The learned parameters are combined with so-called “heart valve components” to detect the occurrence of individual heart beats in the signal. A refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to other algorithms this new approach offers heart rate estimates on a beat-to-beat basis and is designed to cope with arrhythmias. The proposed algorithm has been evaluated in laboratory and home settings for its agreement with an ECG reference. A beat-to-beat interval error of 14.16 ms with a coverage of 96.87% was achieved. Averaged over 10 s long epochs, the mean heart rate error was 0.39 bpm.

Heart rate estimation on a beat-to-beat basis via ballistocardiography - a hybrid approach

We present an algorithm for obtaining the heart rate from the signal of a single, contact-less sensor recording the mechanical activity of the heart. This vital parameter is required on a beat-to-beat basis for applications in sleep analysis and heart failure disease management. Our approach bundles information from various sources for first robust estimates. These estimates are further refined in a second step. An unambiguous comparison with the ECG RR-intervals taken as reference is possible for 98.5% of the heart beats. In these cases, a mean absolute error of 17 ms for the inter-beat interval lengths has been achieved, over a test corpus of 20 whole nights.

Estimation of vital signs in bed from a single unobtrusive mechanical sensor: Algorithms and real-life evaluation

A single contact-less mechanical sensor is exploited for estimating three vital signs during sleep, namely, the heart rate, the breathing rate and an activity index related to the body movements. Robust estimations are achieved over epochs of 30 seconds. The data processing is performed with standard DSP techniques leading to an integrated solution for dealing with body motion artifacts. The algorithms are described and evaluated over a one-hundred night corpus collected from real-life recordings of healthy subjects and sleep-laboratory patients. Results show that the average error of the heart rate is of 1.25 beat per minute compared to the reference values from an ECG and the coverage of the mechanically derived estimates is 83%.