Embedded QRS complex detection based on ECG signal strength and trend

Cardiovascular diseases (CVD) are among the most fatal diseases and its mitigation is a huge challenge in healthcare. Electrocardiography (ECG) remains the most commonly used method and device for the visualization and analysis cardiac rhythm. QRS complex detection has been extensively used for this purpose. Recently, several PC based QRS detection algorithms were proposed wherein the QRS complex is detected from previously stored ECG signals. However, with the expansion of personalized medicine backed by advances in the field of wearable medical devices, it is imperative to have an algorithm that is robust, accurate, real-time, and computationally efficient for low resource microcontroller units (MCU). In this paper, a novel embedded QRS complex detection algorithm based on the ECG signal strength and its trend is presented. The algorithm is based on a simulated capacitor threshold that captures the signal strength two other thresholds based on the signal trend to find the position of the R-peak. Subsequently, the location of the end of the R-peak and the tangent to the ECG curve are used to find the onset of the QRS complex. The algorithm obtained average F1 scores of 99.75%, 99.84%, 97.39%, and 86.63% on the MIT-BIH Arrhythmia, MIT-BIH Normal Sinus, the MIT-BIH Noise Stress Test, and the 2014 PhysioNet/CinC Challenge databases respectively. Average run-times for the processing of a single data point were 3.38μs, and 383μs when the algorithm was running respectively on an ESP32-WROOM-32 MCU @240MHz and an Arduino Nano MCU @16MHz. This algorithm has a great potential in the field of wearable medical devices and remote monitoring systems.