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Evaluation of QRS detection algorithm implemented for mobile applications based on ECG data acquired from sensorized garments

: Tantinger, D.; Feilner, S.; Schmitz, D.; Weigand, C.; Hofmann, C.; Struck, M.

Deutsche Gesellschaft für Biomedizinische Technik -DGBMT-; Univ. Freiburg/Brsg., Institut für Mikrosystemtechnik -IMTEK-:
BMT 2012, Biomedizinische Technik. Proceedings 46. DGBMT Jahrestagung : Jena, September 16 - 19, 2012
Berlin: De Gruyter, 2012 (Biomedizinische Technik 57, 2012, Supplement 1)
Deutsche Gesellschaft für Biomedizinische Technik (DGBMT Jahrestagung) <46, 2012, Jena>
Fraunhofer IIS ()

People can greatly benefit from mobile technologies that continuously monitor their vital signs, in medicine as well as in home environments and sports. In order to meet the requirements of mobile systems the algorithms have to be robust, reliable, take the limited resources into account and overcome the drawback of motion artefacts. This paper presents the evaluation of an algorithm for QRS detection based on ECG signals from a sensorized garment. The system saves the ECG data, measured via two textile electrodes sewed into the shirt, on a microSD card using the EDF+-format. The raw data is processed on a desktop PC using a modified state-of-the-art algorithm. QRS complexes and R-peaks of electrocardiographic signals are detected using the technique of zero crossings. Hereby, main focus has to be placed on the proper specification of the band pass filter, which is the basis for high accuracy. For the evaluation a well-defined test protocol has been specified. Six activities respectively postures were defined: Sitting, standing, walking, running, cycling and rowing. Each activity was performed by 10 test persons for a fixed time interval. Various parameters, where the temporal location of the R-peak is of importance, can be derived from the recorded ECG raw data, such as heart rate, heart rate variability or ECG classification. This method is robust and provides high accuracy even in case of noisy signals. Motion artefacts could be compensated on a high level. The performed study illustrates that even validated state-of-the-art R-peak detection algorithms have to be adapted and optimized for the mobile and daily usage. Due to its computational efficiency it is suitable for mobile applications in real-time.