Intelligent electrocardiogram pattern classification and recognition using low‐cost cardio‐care system

Sutar, Rajendra G.; Kothari, Ashwin

doi:10.1049/iet-smt.2013.0156

Cited by 11 publications

(5 citation statements)

References 30 publications

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“…ECG signal is the most common physiological signal that is recorded for cardiac disease identification and monitoring. Previous studies [15–17] showed the probability of using ECG as a biometric analysis tool for a small group of subjects. For the compatibility of the process, both ECG beat shape and statistical features are extracted from ECG.…”

Section: Methodsmentioning

confidence: 99%

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Biometric analysis using fused feature set from side face texture and electrocardiogram

Chakraborty¹,

Mitra²,

Pal³

2017

IET Science, Measurement & Technology

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Section: Methodsmentioning

confidence: 99%

“…Recently, cardiovascular signals, have also been used for identity recognition solution [15–17]. A comparative analysis on electrocardiogram (ECG) based biometric recognition can be found in [18].…”

Section: Literature Surveymentioning

confidence: 99%

Biometric analysis using fused feature set from side face texture and electrocardiogram

Chakraborty¹,

Mitra²,

Pal³

2017

IET Science, Measurement & Technology

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“…Gabor filter is a type of linear filters and its response for impulse signal is characterized as a Gaussian function [28][29][30]. The requirement of minimal space bandwidth product makes this filter highly suitable for the proposed work.…”

Section: Preprocessingmentioning

confidence: 99%

A Novel Radial Basis Function Neural Network Approach for ECG Signal Classification

Sathishkumar¹,

Priya²

2023

Intelligent Automation &Amp; Soft Computing

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Electrocardiogram (ECG) is a diagnostic method that helps to assess and record the electrical impulses of heart. The traditional methods in the extraction of ECG features is inneffective for avoiding the computational abstractions in the ECG signal. The cardiologist and medical specialist find numerous difficulties in the process of traditional approaches. The specified restrictions are eliminated in the proposed classifier. The fundamental aim of this work is to find the R-R interval. To analyze the blockage, different approaches are implemented, which make the computation as facile with high accuracy. The information are recovered from the MIT-BIH dataset. The retrieved data contain normal and pathological ECG signals. To obtain a noiseless signal, Gabor filter is employed and to compute the amplitude of the signal, DCT-DOST (Discrete cosine based Discrete orthogonal stock well transform) is implemented. The amplitude is computed to detect the cardiac abnormality. The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified. The Genetic algorithm (GA) retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification. In addition, the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement. Finally, the RBFNN (Radial basis function neural network) is applied, which diminishes the local minima present in the signal. It shows enhancement in characterizing the ordinary and anomalous ECG signals.

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“…Gabor filter is a type of linear filters whose response for impulse signal is characterized as a Gaussian function paired with a coherence function [ 22 , 23 ]. The requirement of minimal space bandwidth product makes this filter highly suitable for our proposed work.…”

Section: Proposed Workmentioning

confidence: 99%

An Innovative Machine Learning Approach for Classifying ECG Signals in Healthcare Devices

Bhamidipati

Reddy²,

Chillara

et al. 2022

Journal of Healthcare Engineering

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An ECG is a diagnostic technique that examines and records the heart’s electrical impulses. It is easy to categorise and prevent computational abstractions in the ECG signal using the conventional method for obtaining ECG features. It is a significant issue, but it is also a difficult and time-consuming chore for cardiologists and medical professionals. The proposed classifier eliminates all of the following limitations. Machine learning in healthcare equipment reduces moral transgressions. This study’s primary purpose is to calculate the R-R interval and analyze the blockage utilising simple algorithms and approaches that give high accuracy. The MIT-BIH dataset may be used to rebuild the data. The acquired data may include both normal and abnormal ECGs. A Gabor filter is employed to generate a noiseless signal, and DCT-DOST is used to calculate the signal’s amplitude. The amplitude is computed to detect any cardiac anomalies. A genetic algorithm derives the main highlights from the R peak and cycle segment length underlying the ECG signal. So, combining data with specific qualities maximises identification. The genetic algorithm aids in hereditary computations, which aids in multitarget improvement. Finally, Radial Basis Function Neural Network (RBFNN) is presented as an example. An efficient feedforward neural network lowers the number of local minima in the signal. It shows progress in identifying both normal and abnormal ECG signals.

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Intelligent electrocardiogram pattern classification and recognition using low‐cost cardio‐care system

Cited by 11 publications

References 30 publications

Biometric analysis using fused feature set from side face texture and electrocardiogram

Biometric analysis using fused feature set from side face texture and electrocardiogram

A Novel Radial Basis Function Neural Network Approach for ECG Signal Classification

An Innovative Machine Learning Approach for Classifying ECG Signals in Healthcare Devices

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