The aging population requires new and innovative approaches to monitor and supervise medical and physical conditions in residential environments. For this purpose, various sensor and hardware systems are being developed by researchers and industrial companies. One way to monitor health status is the electrocardiogram (ECG), which noninvasively measures heart activity on the body surface. These measurements provide a simple and easy way to monitor health on a continuous basis. However, the use of ECG measurements outside a confined clinical setting, beyond purely medical purposes, is associated with considerable disadvantages resulting from the given freedom of movement. In this work, a substantial noise source in mobile ECG is examined: Motion artifacts. We study the spectral characteristics of motion artifacts for a set of different motions representing everyday activities, namely: standing up, bending forward, walking, running, jumping, and climbing stairs. Furthermore, we investigate to what extent the reference sensors (accelerometer, gyroscope, and skin-electrode impedance) are able to characterize and remove the recorded motion artifacts from the measurements. Our results demonstrate that motion artifacts markedly change their characteristics with a change in motion. While lowintensity movements manifest in lower frequency bands, higher intensity exercises provoke motion artifacts that are much more complex in their composition. These characteristics are correspondingly reflected in the correlation between reference sensors and artifacts. To overcome the drawbacks of motion artifacts in mobile measurements, we propose the application of tensor decomposition using canonical polyadic decomposition (CPD) as an example. A significant advantage of tensor factorization is that it can decompose the data without artificial constraints, unlike matrix factorization. We use CPD along with measurements obtained from different reference sensors to remove the artifacts. Wavelet transformation is utilized to transform ECG and reference data from vector to matrix format. Subsequently, a tensor is constructed by combining the heterogeneous measurements into a three-dimensional tensor. In this way, it is possible to access temporal and spectral features within the data simultaneously. Subsequently, we propose a methodology to predict the decomposition rank based on statistical features in the ECG that quantify the signal quality. To evaluate the performance of the decomposition process, we combine isolated motion artifacts recorded at the back with ECG obtained in rest to generate artificially corrupted data. The results suggest that CPD successfully removes motion artifacts from the data for all reference sensors regarded.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:76855 |
| Date | 03 December 2021 |
| Creators | Lilienthal, Jannis |
| Contributors | Schill, Alexander, Hirtz, Gangolf, Dargie, Waltenegus, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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