FPGA-Based Low-Cost Architecture for R-Peak Detection and Heart-Rate Calculation Using Lifting-Based Discrete Wavelet Transform

Gon, Anusaka; Mukherjee, Atin

doi:10.1007/s00034-022-02148-7

Cited by 11 publications

(4 citation statements)

References 45 publications

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“…Among the different techniques developed for ECG analysis, time-domain analysis, frequency-domain analysis, and wavelet transform are the predominant approaches. Many ECG R-peak detection methods utilizing transforms [25] primarily use Hilbert (HT) [26][27][28], Pan-Tompkins (PT) [29,30], and wavelet transforms [31][32][33]. Transform-based methods often require buffering of multiple samples before detection.…”

Section: Discussionmentioning

confidence: 99%

A New and Lightweight R-Peak Detector Using the TEDA Evolving Algorithm

Silva,

Souza

et al. 2024

MAKE

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The literature on ECG delineation algorithms has seen significant growth in recent decades. However, several challenges still need to be addressed. This work aims to propose a lightweight R-peak-detection algorithm that does not require pre-setting and performs classification on a sample-by-sample basis. The novelty of the proposed approach lies in the utilization of the typicality eccentricity detection anomaly (TEDA) algorithm for R-peak detection. The proposed method for R-peak detection consists of three phases. Firstly, the ECG signal is preprocessed by calculating the signal’s slope and applying filtering techniques. Next, the preprocessed signal is inputted into the TEDA algorithm for R-peak estimation. Finally, in the third and last step, the R-peak identification is carried out. To evaluate the effectiveness of the proposed technique, experiments were conducted on the MIT-BIH arrhythmia database (MIT-AD) for R-peak detection and validation. The results of the study demonstrated that the proposed evolutive algorithm achieved a sensitivity (Se in %), positive predictivity (+P in %), and accuracy (ACC in %) of 95.45%, 99.61%, and 95.09%, respectively, with a tolerance (TOL) of 100 milliseconds. One key advantage of the proposed technique is its low computational complexity, as it is based on a statistical framework calculated recursively. It employs the concepts of typicity and eccentricity to determine whether a given sample is normal or abnormal within the dataset. Unlike most traditional methods, it does not require signal buffering or windowing. Furthermore, the proposed technique employs simple decision rules rather than heuristic approaches, further contributing to its computational efficiency.

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Section: Discussionmentioning

confidence: 99%

A New and Lightweight R-Peak Detector Using the TEDA Evolving Algorithm

Silva,

Souza

et al. 2024

MAKE

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“…In addition, Vaishnavi et al (2023) came with a new adaptive filter, which plays efficaciously as an LMS adaptive filter to banish the baseline wander noise from the ECG signals, showing its outstanding result in improving the signals' quality. Gon and Mukherjee (2023) suggested the efficient reconstruction of FPGA with multiplierless lifting-based wavelet denoising for ECG noise cancellation. Their architecture with the ability to lower resource consumption as well as increase the operating frequency with existing ones and even future ones.…”

Section: Stage 1: Preprocessing (Denoising)mentioning

confidence: 99%

Real-Time Processing Stages of Electrocardiogram Signal: A Review

Waleed

2024

JMTE

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Signal analysis is a multidisciplinary field that combines various processes to create robust pipelines for automating data analysis. Within the medical field, its application revolves around physiological signals. Electrocardiogram (ECG) signal provides vital information concerning various cardiac conditions affecting the human heart. ECG analysis is a central pillar of medical research with the goal of detecting and preventing potentially fatal cardiac events. This review article aims to provide a comprehensive analysis of real-time processing techniques for electrocardiogram signals. It discusses the different methods and algorithms used for detecting and analyzing ECG signals in real-time, with a focus on their effectiveness and efficiency. Where, it evaluates the use of various techniques such as ECG signal preprocessing (denoising), ECG fiducial points detecting and ECG signal classification. It also highlights the challenges faced in real-time ECG signal processing, such as High-fidelity signal acquisition and noise reduction, and computational efficiency and resource constraints. Furthermore, the article presents an overview of the existing QRS-peak detection strategies, including methods such as Haar wavelet transform, and modified MaMeMi Filter.

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“…Consequently, the function β µ,ξ (υ) is also dependent on ξ. Figure 1 shows an example for the previous discussion using a generic window function and a Daubechies 4 wavelet [23][24][25]. The effect explained earlier gives a redundancy effect that offers the possibility for maximizing the noise attenuation and cross-terms attenuation.…”

Section: Definition 1 (T E Window)mentioning

confidence: 99%

The Te Transform: A High-Resolution Integral Transform and Its Key Properties

Trutié-Carrero,

Seuret-Jiménez,

Nieto-Jalil

et al. 2023

Mathematics

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In this paper, we present six new contributions: two novel definitions and four groundbreaking theorems related to the theoretical foundations of the integral Te transform, with a specific focus on analyzing functions with integrable modulus. The definitions referred to the Te window and the Te transform in two parameters, respectively. The theorems provide the main theoretical basis for the Te transform: the existence of the Te transform in two parameters, the Te transform ∈L1(R), the existence of the inverse Te transform, and uniqueness of the Te transform. These results reveal the importance of the fact that the Te transform only depends on two parameters (translation and dyadic frequency), obtaining its inverse transformation more directly; hence, breaking through a new approach in function analysis by representing a function in the scale-frequency plane. The theoretical results presented in this paper are supported by the previous works of the authors.

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FPGA-Based Low-Cost Architecture for R-Peak Detection and Heart-Rate Calculation Using Lifting-Based Discrete Wavelet Transform

Cited by 11 publications

References 45 publications

A New and Lightweight R-Peak Detector Using the TEDA Evolving Algorithm

A New and Lightweight R-Peak Detector Using the TEDA Evolving Algorithm

Real-Time Processing Stages of Electrocardiogram Signal: A Review

The Te Transform: A High-Resolution Integral Transform and Its Key Properties

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