Comparison of Low-Complexity Algorithms for Real-Time QRS Detection using Standard ECG Database

Silvestri, Francesca; Carlo, Cardarilli Gian; Nunzio, Luca Di; Fazzolari, Rocco; Re, M.

doi:10.18517/ijaseit.8.2.4956

Cited by 12 publications

(6 citation statements)

References 21 publications

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“…In Fig.7 the CNN power consumption is provided. Power consumption nowadays represents a crucial aspect especially for embedded systems where the power supply is usually provided by batteries [30][31][32][33][34][35][36]. There are three power dissipation components in CMOS digital circuits and consequently in microprocessors [23]: Among these contributions, the switching power represents the main one, and it is defined in Eq.2, where a is the switching activity, C is the switching capacitance, f is the clock frequency and Vdd the supply voltage.…”

Section: Resultsmentioning

confidence: 99%

FPGA Implementation of Hand-written Number Recognition Based on CNN

Giardino

Matta

Silvestri

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Convolutional Neural Networks (CNNs) are the state-of-the-art in computer vision for different purposes such as image and video classification, recommender systems and natural language processing. The connectivity pattern between CNNs neurons is inspired by the structure of the animal visual cortex. In order to allow the processing, they are realized with multiple parallel 2dimensional FIR filters that convolve the input signal with the learned feature maps. For this reason, a CNN implementation requires highly parallel computations that cannot be achieved using traditional general-purpose processors, which is why they benefit from a very significant speed-up when mapped and run on Field Programmable Gate Arrays (FPGAs). This is because FPGAs offer the capability to design full customizable hardware architectures, providing high flexibility and the availability of hundreds to thousands of on-chip Digital Signal Processing (DSP) blocks. This paper presents an FPGA implementation of a handwritten number recognition system based on CNN. The system has been characterized in terms of classification accuracy, area, speed, and power consumption. The neural network was implemented on a Xilinx XC7A100T FPGA, and it uses 29.69% of Slice LUTs, 4.42% of slice registers and 52.50% block RAMs. We designed the system using a 9-bit representation that allows for avoiding the use of DSP. For this reason, multipliers are implemented using LUTs. The proposed architecture can be easily scaled on different FPGA devices thank its regularity. CNN can reach a classification accuracy of 90%.

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Section: Resultsmentioning

confidence: 99%

FPGA Implementation of Hand-written Number Recognition Based on CNN

Giardino

Matta

Silvestri

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Different algorithms are used for the detection of QRS Complex, the popular ones include wavelet transformation [92], [93], neural networks [89], [94], and Pan and Tompkins algorithm [95], [96]. Pan Tompkins algorithm is the most popular algorithm used for the detection of QRS complex and it is found to outperform other methods in terms of detection accuracy and computational cost [97]. Authors in [98] has proposed Neural Network based approach for QRS cancellation which they consider to be more robust to noise and QRS variations.…”

Section: Features Extractionmentioning

confidence: 99%

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Rizwan

Zoha

Mabrouk

et al. 2021

IEEE Rev. Biomed. Eng.

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Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. n this paper, state-ofthe-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. It is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis at one place. It is observed that there is dire need of low energy, low cost but accurate auto diagnosis solutions for the proactive management of AF.

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“…ECG signal is a type of non-stationary signals and it is broadly used to monitor the heartbeats rate and their tuning. The main purpose of this exploration is to provide an academic diagnosis exemplary which can aid in human operating AI or deep learning and swarm optimization algorithms in ECG heartbeats classification [10]. As the number of death surges day by day because of heart complications and the central cause of this death is the absence of superior diagnosis exemplary accessibility.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA

Tyagi

Mehra

2021

SN Appl. Sci.

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Automatic heart disease detection from human heartbeats is a challenging and intellectual assignment in signal processing because periodically monitoring of the heart beat arrhythmia for patient is an essential task to reduce the death rate due to cardiovascular disease (CVD). In this paper, the focus of research is to design hybrid Convolutional Neural Network (CNN) architecture by making use of Grasshopper Optimization Algorithm (GOA) to classify different types of heart diseases from the ECG signal or human heartbeats. Convolutional Neural Network (CNN) as an artificial intelligence approach is widely used in computer vision-based medical data analysis. However, the traditional CNN cannot be used for classification of heart diseases from the ECG signal because lots of noise or irrelevant data is mixed with signal. So this study utilizes the pre-processing and selection of feature for proper heart diseases classification, where Discrete Wavelet Transform (DWT) is used for the noise reduction as well as segmentation of ECG signal and Grasshopper Optimization Algorithm (GOA) is used for selection of R-peaks features from the extracted feature sets in terms of R-peaks and R-R intervals that help to attain better classification accuracy. For training as well as testing of projected Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture. The assortment of proper R-peaks and R-R intervals is a major factor and because of the deficiency of apposite pre-processing phases like noise removal, signal decomposition, smoothing and filtering, the uniqueness of extracted feature is less. The experimental outcomes show that the planned HCM is effective for detecting irregular human heartbeats via R-peaks and R-R intervals. When the proposed Heartbeats Classification Model (HCM) was verified on the database, model achieved higher efficiency than other state-of-the-art techniques for 16 heartbeat disease categories and the average classification accuracy is 99.58% with fast and robust responses where the correctly classified heartbeats are 86,005 and misclassified beats is only 108 with 0.42% error rate.

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Comparison of Low-Complexity Algorithms for Real-Time QRS Detection using Standard ECG Database

Cited by 12 publications

References 21 publications

FPGA Implementation of Hand-written Number Recognition Based on CNN

FPGA Implementation of Hand-written Number Recognition Based on CNN

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA

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