Embedded System Based on an ARM Microcontroller to Analyze Heart Rate Variability in Real Time Using Wavelets

Abstract

The analyses of electrocardiogram (ECG) and heart rate variability (HRV) are of primordial interest for cardiovascular diseases. The algorithm used for the detection of the QRS complex is the basis for HRV analysis and HRV quality will depend strongly on it. The aim of this paper is to implement HRV analysis in real time on an ARM microcontroller (MCU). Thus, there is no need to send raw data to a cloud server for real time HRV monitoring and, consequently, the communication requirements and the power consumption of the local sensor node would be far lower. The system would facilitate the integration into edge computing, for instance, in small local networks, such as hospitals. A QRS detector based on wavelets is proposed, which is able to autonomously select the coefficients the QRS complex will be detected with. To validate it, the MITBIH and NSRDB databases were used. This detector was implemented in real time using an MCU. Subsequently HRV analysis was implemented in the time, frequency, and nonlinear domains. When evaluating the QRS detector with the MITBIH database, 99.61% positive prediction (PP), 99.3% sensitivity (SE), and a prediction error rate (DER) of 1.12% were obtained. For the NSRDB database the results were a PP of 99.95%, an SE of 99.98%, and a DER of 0.0006%. The execution of the QRS detector in the MCU took 52 milliseconds. On the other hand, the time required to calculate the HRV depends on the data size, but it took only a few seconds to analyze several thousands of interbeat intervals. The results obtained for the detector were superior to 99%, so it is expected that the HRV is reliable. It has also been shown that the detection of QRS complex can be done in real time using advanced processing techniques such as wavelets.

An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring

“…Moreover, incorporating Raspberry Pi offers advantages in terms of data storage and facilitates signal analysis and classification using machine learning models. Our proposed work provides more comprehensive details about the entire system, including improved signal quality with all the necessary peaks, making it more reliable and suitable for researchers and industries compared to previous publications [11,[25][26][27][28][29][30]. By contrast, the system described in [5] exhibits inferior signal conditioning, allowing noise to affect the ECG signal shape and peaks, potentially impacting the accuracy of heart disease diagnosis.…”

“…A real-time analysis and remote monitoring of the heart rate variability (HRV) was done successfully using a portable ECG monitor based on an ARM microcontroller [30]. Patil et al [31] proposed a real time ECG on internet using Raspberry Pi, they built a simple circuit on a bread board and displayed the results on an Oscilloscope.…”