An Effective QRS Selection Based on the Level-Crossing Sampling and Activity Selection

S.-M. Qaisar (1) , Laurent Fesquet (2)

1 University of Jeddah
2 TIMA-CDSI - Circuits, Devices and System Integration

The Internet of Things (IoT) healthcare framework is a new trend. In this context, biomedical wearable devices are linked to the cloud. This work contributes to the development of efficient Electrocardiogram (ECG) wearables by redesigning their signal processing chain. The emphasis is on developing a system for effective and precise QRS selection. QRS complexes of heartbeats contain most important arrhythmia related information. The proposed system uses an analog band-pass filter to reduce the impact of Baseline Wander (BW) and aliasing. The band-limited signal is then digitized by using a 5-Bit resolution level-crossing A/D converter. Onward, an original activity selection algorithm is used for an effective selection of the QRS complexes. Results show that the proposed solution attains an average QRS detection sensitivity of 98.4% and positive predictive value of 100% while securing on average 4.77-fold and 2.1-fold compression gains respectively in terms of count of data samples and bits over the classical counterparts.

Mots clés: level-crossing sampling scheme; activity selection; electrocardiogram (ECG)

Domaines: Micro et nanotechnologies/Microélectronique

S.-M. Qaisar, Laurent Fesquet. An Effective QRS Selection Based on the Level-Crossing Sampling and Activity Selection. 6th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP 2020), Sep 2020, Krakow, Poland. ⟨hal-03106771⟩