Hardware Architecture for Adaptive Dual Threshold Filter and Discrete Wavelet Transform based ECG Signal Denoising

Abstract

The ECG signal, like all signals obtained when instrumenting a data acquisition system, is affected by noises of physiological and technical sources such as Electromyogram (EMG) and power line interferences, which can deteriorate its morphology. To overcome this issue, it's subjected to apply a preprocessing step to remove these noises. Filtring techniques are complex computations becoming more common in medical applications, which must be completed in real-time. As a result, these applications are geared at integrating high-performance embedded architectures. This paper presents an FPGA (Field Programmable Gate Array) embedded architecture designed for an ECG denoising hybrid technique based on the Discrete Wavelet transform (DWT) and the Adaptive Dual Threshold Filter (ADTF), dedicated to handle with noises affecting ECG signals.The architecture was designed following a hardware-software codesign using a high-level description language and synthetized to be implemented on different FPGAs due to the structural description flexibility. The global architecture was divided into a set of functional blocks to allow parallel processing of ECG data. The simulation results confirm the high performance of the system in noise reduction without affecting the morphology of the signal. The process takes 0.3 ms with an acquisition frequency of 360 Hz. The whole architecture requires a small area in different FPGAs in terms of resources utilization. It uses less than 1% of the total registers for all FPGA devices which represents a total of 292 registers for Cyclone III LS, Cyclone IV GX, Cyclone IV E, and Arria II GX; and a total of 329 registers for Cyclone V. The logic elements occupancy varies between 3% using Cyclone V and 60% using Cyclone IV GX freeing up space for other parallel processing tasks.

Real-Time Processing Stages of Electrocardiogram Signal: A Review

“…The Kaiser filter, similar to Hamming, incorporates a beta variable to control the filter threshold and is known for its high efficiency and low power consumption. Mejhoudi et al (2021) developed an FPGA architecture for ECG signal denoising using a hybrid technique: DWT and ADTF. The hardware-software co-design divided the architecture into functional blocks for parallel processing.…”

Non-contact Respiratory Rate Monitoring Based on the Principal Component Analysis

“…A discrete Fourier transform is used to convert the resulting signal to the frequency domain [34]. The maximum of the frequency index is extracted as the frequency corresponding to the breathing.…”

Video-based Heart Rate Estimation using Embedded Architectures

“…Measuring heart rate usually requires an ECG that records the electrical heart activity caused by the repolarization and depolarization of the muscle [4], [5]. Different methods exists to estimate heart rate in vehicles.…”