Implementation of Wavelet-Transform-Based Algorithms in an FPGA for Heart Rate and RT Interval Automatic Measurements in Real Time: Application in a Long-Term Ambulatory Electrocardiogram Monitor

Abstract

Cardiovascular diseases are currently the leading cause of death worldwide. Thus, there is a need for non-invasive ambulatory (Holter) ECG monitors with automatic measurements of ECG intervals to evaluate electrocardiographic abnormalities of patients with cardiac diseases. This work presents the implementation of algorithms in an FPGA for beat-to-beat heart rate and RT interval measurements based on the continuous wavelet transform (CWT) with splines for a prototype of an ambulatory ECG monitor of three leads. The prototype’s main elements are an analog–digital converter ADS1294, an FPGA of Xilinx XC7A35T-ICPG236C of the Artix-7 family of low consumption, immersed in a low-scale Cmod-A7 development card integration, an LCD display and a micro-SD memory of 16 Gb. A main state machine initializes and manages the simultaneous acquisition of three leads from the ADS1294 and filters the signals using a FIR filter. The algorithm based on the CWT with splines detects the QRS complex (R or S wave) and then the T-wave end using a search window. Finally, the heart rate (60/RR interval) and the RT interval (from R peak to T-wave end) are calculated for analysis of its dynamics. The micro-SD memory stores the three leads and the RR and RT intervals, and an LCD screen displays the beat-to-beat values of heart rate, RT interval and the electrode connection. The algorithm implemented on the FPGA achieved satisfactory results in detecting different morphologies of QRS complexes and T wave in real time for the analysis of heart rate and RT interval dynamics.

Robust algorithm for the detection and classification of QRS complexes with different morphologies using the continuous spline wavelet transform with automatic scale detection

“…To calculate the CWT at any integer scale we used the method based on B-splines proposed by Unser et al (1994) and García-Limón et al (2023). In this method, the input signal x(t) and the wavelet function ψ(t) are polynomial spline functions of degree n 1 = 1 (the signal is linear between samples) and n 2 = 3 (the degree of the spline is cubic) respectively.…”

“…The third test was to evaluate the QRS detection on the DI lead from a 27 h recording of a normal subject, obtained with a sampling frequency of 1000 Hz, using the Holter monitor developed by Martínez-Suárez et al (2023). Given that the record analyzed is of long-term, an application developed in Visual Studio Community 2019 was employed that uses the algorithm described in this article.…”

Low-Power Long-Term Ambulatory Electrocardiography Monitor of Three Leads with Beat-to-Beat Heart Rate Measurement in Real Time

“…In addition, it is possible to select between the three leads DI, aVF and V2, which will be visible on the display. Finally, the HR parameter will show the heart rate of the selected lead since the heart rate is calculated independently for each lead with the algorithm developed in [11,21] (Figure 5h).…”

“…In recent years, we proposed two prototypes of CAECG monitors based on the Field Programmable Gate Array (FPGA) with the beat-to-beat real-time detection of the QRS complex [10] and the QRS complex and T-wave end [11]. In [12], we presented a CAECG monitor based on the low-power dual-core ESP32 microcontroller with three selectable sampling rates, simultaneous acquisition and the storage of three leads, a beat-to-beat heart rate measurement in real time and lead-off detection for continuous periods until 84 h.…”

“…It also allows one to increase the number of ECG leads or the measurement of different biopotentials by replacing the ADS1294 (four-channel, analog-to-digital converter with an integrated ECG front end) with ADS1296, ADS1298 or ADS1299 by making small adjustments to the software and hardware. The detection algorithms of the ECG characteristic points used in [10][11][12] were based on the continuous wavelet transform (CWT) with splines [13]. This technique allows for the evaluation of the CWT on any integer scale, reducing noise, interference and artifacts more efficiently [14].…”