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Sensors 2017, 17(9), 1969;

A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices

Department of Aeronautics and Astronautics, National Cheng-Kung University, Tainan 70101, Taiwan
Industrial Technology Research Institute, Tainan 70101, Taiwan
Author to whom correspondence should be addressed.
Received: 28 July 2017 / Revised: 23 August 2017 / Accepted: 24 August 2017 / Published: 26 August 2017
(This article belongs to the Special Issue New Generation Sensors Enabling and Fostering IoT)
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In the new-generation wearable Electrocardiogram (ECG) system, signal processing with low power consumption is required to transmit data when detecting dangerous rhythms and to record signals when detecting abnormal rhythms. The QRS complex is a combination of three of the graphic deflection seen on a typical ECG. This study proposes a real-time QRS detection and R point recognition method with low computational complexity while maintaining a high accuracy. The enhancement of QRS segments and restraining of P and T waves are carried out by the proposed ECG signal transformation, which also leads to the elimination of baseline wandering. In this study, the QRS fiducial point is determined based on the detected crests and troughs of the transformed signal. Subsequently, the R point can be recognized based on four QRS waveform templates and preliminary heart rhythm classification can be also achieved at the same time. The performance of the proposed approach is demonstrated using the benchmark of the MIT-BIH Arrhythmia Database, where the QRS detected sensitivity (Se) and positive prediction (+P) are 99.82% and 99.81%, respectively. The result reveals the approach’s advantage of low computational complexity, as well as the feasibility of the real-time application on a mobile phone and an embedded system. View Full-Text
Keywords: ECG; QRS detection; heartbeat detection; mobile healthcare; IoT; wearable device; edge computing ECG; QRS detection; heartbeat detection; mobile healthcare; IoT; wearable device; edge computing

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Chen, C.-L.; Chuang, C.-T. A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices. Sensors 2017, 17, 1969.

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