TY  - JOUR
A1  - Chromik, Jonas
A1  - Pirl, Lukas
A1  - Beilharz, Jossekin Jakob
A1  - Arnrich, Bert
A1  - Polze, Andreas
T1  - Certainty in QRS detection with artificial neural networks
JF  - Biomedical signal processing and control
N2  - Detection of the QRS complex is a long-standing topic in the context of electrocardiography and many algorithms build upon the knowledge of the QRS positions. Although the first solutions to this problem were proposed in the 1970s and 1980s, there is still potential for improvements. Advancements in neural network technology made in recent years also lead to the emergence of enhanced QRS detectors based on artificial neural networks. In this work, we propose a method for assessing the certainty that is in each of the detected QRS complexes, i.e. how confident the QRS detector is that there is, in fact, a QRS complex in the position where it was detected. We further show how this metric can be utilised to distinguish correctly detected QRS complexes from false detections.
KW  - QRS detection
KW  - Electrocardiography
KW  - Artificial neural networks
KW  - Machine
KW  - learning
KW  - Signal-to-noise ratio
Y1  - 2021
U6  - https://doi.org/10.1016/j.bspc.2021.102628
SN  - 1746-8094
SN  - 1746-8108
VL  - 68
PB  - Elsevier
CY  - Oxford
ER  - 
TY  - GEN
A1  - Stojanovic, Vladeta
A1  - Trapp, Matthias
A1  - Richter, Rico
A1  - Döllner, Jürgen Roland Friedrich
T1  - A service-oriented approach for classifying 3D points clouds by example of office furniture classification
T2  - Web3D 2018: Proceedings of the 23rd International ACM Conference on 3D Web Technology
N2  - The rapid digitalization of the Facility Management (FM) sector has increased the demand for mobile, interactive analytics approaches concerning the operational state of a building. These approaches provide the key to increasing stakeholder engagement associated with Operation and Maintenance (O&M) procedures of living and working areas, buildings, and other built environment spaces. We present a generic and fast approach to process and analyze given 3D point clouds of typical indoor office spaces to create corresponding up-to-date approximations of classified segments and object-based 3D models that can be used to analyze, record and highlight changes of spatial configurations. The approach is based on machine-learning methods used to classify the scanned 3D point cloud data using 2D images. This approach can be used to primarily track changes of objects over time for comparison, allowing for routine classification, and presentation of results used for decision making. We specifically focus on classification, segmentation, and reconstruction of multiple different object types in a 3D point-cloud scene. We present our current research and describe the implementation of these technologies as a web-based application using a services-oriented methodology.
KW  - Indoor Models
KW  - 3D Point Clouds
KW  - Machine
KW  - Learning
KW  - BIM
KW  - Service-Oriented
Y1  - 2018
SN  - 978-1-4503-5800-2
U6  - https://doi.org/10.1145/3208806.3208810
SP  - 1
EP  - 9
PB  - Association for Computing Machinery
CY  - New York
ER  -