An Efficient Architecture for QRS Detection in FPGA Using Integer Haar Wavelet Transform

Talukder, Soham; Singh, Rajan; Bora, Satyajit; Paily, Roy

doi:10.1007/s00034-019-01328-2

Cited by 18 publications

(5 citation statements)

References 19 publications

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“…Our study focuses on long-term ECG signals from the MIT-BIH database, aiming to classify heartbeats into three categories: N, S, and V. To achieve this, a QRS detection algorithm is essential for extracting distinct ECG heartbeats from a single recording. Numerous approaches have been www.ijacsa.thesai.org explored in the realm of QRS detection, ranging from the foundational Pan-Tompkins algorithm [28] to wavelet-based techniques [29][30], and even machine learning models [31].…”

Section: B Data Preprocessingmentioning

confidence: 99%

A Novel Inter Patient ECG Arrhythmia Classification Approach with Deep Feature Extraction and 1D Convolutional Neural Network

Bourkha,

Hatim,

Nasir

et al. 2024

IJACSA

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The World Health Organization (WHO) sheds light on the escalating prevalence of heart diseases, foreseeing a substantial rise in the years ahead, impacting a vast global population. Swift and accurate early detection becomes pivotal in managing severe complications, underscoring the urgency of timely identification. While Ventricular Ectopic Beats (V) might initially be considered normal, their frequent occurrence could serve as a potential red flag for progressing to severe conditions like atrial fibrillation, Ventricular Tachycardia, and even cardiac arrest. This accentuates the need for developing an automated approach for early detection of cardiovascular diseases (CVD). This paper presents a novel method to classify arrhythmias. Leveraging the Wavelet Scattering Transform (WST) to extract morphological features from Electrocardiogram heartbeats (ECG), these features seamlessly integrate into a 1D Convolutional Neural Network (CNN).The CNN is finely tuned to distinguish between V, Supraventricular Ectopic Beats (S), and Non-Ectopic Beats (N). Our model's performance surpasses state-of-the-art models, boasting precision, sensitivity, and specificity of 94.56%, 97.26%, and 99.54% for V, and 99.25%, 98.65%, and 93.26% for N. Remarkably, it achieves 68.01% precision, 77.75% sensitivity, and 99.14% specificity for S.

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Section: B Data Preprocessingmentioning

confidence: 99%

A Novel Inter Patient ECG Arrhythmia Classification Approach with Deep Feature Extraction and 1D Convolutional Neural Network

Bourkha,

Hatim,

Nasir

et al. 2024

IJACSA

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“…Following Wei et al (2012), the Daubechies wavelet of order 5 is used to decompose the streamflow time series. For a streamflow time series q(t), the discrete wavelet transform is (Talukder et al, 2020):…”

Section: Time Series Decompositionmentioning

confidence: 99%

“…( )    22 q t q t = (3) Therefore, streamflow time series is decomposed into the approximation components and detail components (Talukder et al, 2020):…”

mentioning

confidence: 99%

A decomposition approach to evaluating the local performance of global streamflow reanalysis

Zhao,

Chen,

Tian

et al. 2024

Preprint

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Abstract. While global streamflow reanalysis provides valuable information for water resources management, its local performance in the time-frequency domain is yet to be investigated. This paper presents a novel decomposition approach to evaluating streamflow reanalysis by combining wavelet transform with machine learning. Specifically, the time series of streamflow reanalysis and observation are respectively decomposed and then the approximation components of reanalysis are compared to those of observed streamflow. Furthermore, the accumulated local effects are derived to showcase the influences of catchment attributes on the performance of raw reanalysis at different scales. For streamflow reanalysis generated by the Global Flood Awareness System, a case study is devised based on streamflow observations from the Catchment Attributes and Meteorology for Large-sample Studies. The results highlight that the reanalysis tends to be more effective in characterizing seasonal, annual and multi-annual features than daily, weekly and monthly features. The Kling-Gupta Efficiency (KGE) values of raw reanalysis and approximation components are primarily influenced by precipitation seasonality. That is, high values of KGE tend to be observed in catchments where there is more precipitation in winter, which can be due to low evaporation that results in reasonable simulations of soil moisture and baseflow processes. The longitude, mean precipitation and mean slope also influence the local performance of approximation components. On the other hand, attributes on geology, soils and vegetation appear to play a relatively small part in the performance of approximation components. Overall, this paper provides useful information for practical applications of global streamflow reanalysis.

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“…The Fig. 2: Haar wavelet function [7] Equations 3 and 4 show how to calculate the coefficients of approximation and details at the first scale, respectively [16].…”

Section: B Haar Transformmentioning

confidence: 99%

Exploring Multi-Level Composition and Efficient MCM Schemes for an Energy-Efficient Wavelet Haar Architecture

Seidel

Rosa

Paim

et al. 2021

JICS

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This work explores multi-level fixed-point Haar transform compositions combined with MCM (multiple constant multiplication) schemes for an energy-efficient hardware architecture. We investigate a set of six lower-level Haar transforms for composing a Haar-9 architecture. The multiple-level Haar transforms use as a base M=1, M=2, and M=3 resolution levels. The processing module (PM) of the Haar explores efficient MCM schemes. The architectures were described in VHDL and synthesized employing the ST 65nm CMOS cell library. The results show that Haar-II architecture presents the lower circuit area results since this architecture requires fewer arithmetic operators. However, the most energy-efficient Haar-9 hardware architecture employs a combination of two M=2 with five M=1 blocks with a efficient MCM architecture reduced to only two arithmetic operators.

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An Efficient Architecture for QRS Detection in FPGA Using Integer Haar Wavelet Transform

Cited by 18 publications

References 19 publications

A Novel Inter Patient ECG Arrhythmia Classification Approach with Deep Feature Extraction and 1D Convolutional Neural Network

A Novel Inter Patient ECG Arrhythmia Classification Approach with Deep Feature Extraction and 1D Convolutional Neural Network

A decomposition approach to evaluating the local performance of global streamflow reanalysis

Exploring Multi-Level Composition and Efficient MCM Schemes for an Energy-Efficient Wavelet Haar Architecture

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