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13 articles for “ECG signal analysis”
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Survey of Techniques for Clustering and Classification of ECG using WEKA
Abstract: ECG analysis can be done for automatic detection of abnormality in cardiac activity. This can be helpful in generating alert for saving a precious life. Various techniques have been proposed in literature for feature detection and classification such as; fuzzy logic methods, artificial neural networks (ANN), and support vector machines (SVM), wavelet transform, Hilbert transform, etc. Recently, numerous researches and techniques have been developed for analyzing the ECG signal on …
Published in Journal of Microcontroller Engineering and Applications · Vol. 3, Issue 2, 2016 · pp. 14–19 Read article
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Congenital Heart Disease (CHD) Detection Technique Based on Analysis and Classification of ECG Signal
Abstract: As per American Heart Association information, every year 125 babies out of 1000 are born with congenital heart disease (CHD). It was estimated that 36000 children are live born with CHD each year in the European Union. Child with CHD may have hole in heart up to 3–5 mm as small defects and for large defects it may go up to 5–8 mm. Depending upon the size of hole in …
Published in Current Trends in Signal Processing · Vol. 6, Issue 3, 2016 · pp. 29–27 Read article
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Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems
Abstract: This paper presents a novel Fractal-Entropy Guided Adaptive Signal Reconstruction (FEG- ASR) framework designed for accurate processing of non-stationary signals in biomedical and communication systems. The proposed approach integrates fractal dimension analysis with entropy- based feature evaluation to capture the intrinsic complexity and irregularity of time-varying signals. By dynamically adapting reconstruction parameters based on fractal-entropy measures, the method effectively separates noise from meaningful signal components while preserving critical information. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article
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Performance Analysis of FIR Digital Filter using Artificial Neural Network for ECG Signal
Abstract: AbstractIn performance of electrocardiography (ECG) signal, signal acquisition must be noise free. This paper deals with the application of the digital finite impulse response (FIR) filter on the raw ECG signal. In this paper different window techniques used to design FIR filter and their signal to noise ratio are compared. The dataset configured for multilayer perceptron (MLP) training with feed forward algorithm. Finally the MLP is trained and the results …
Published in Journal of Communication Engineering & Systems · Vol. 3, Issue 2, 2013 · pp. 28–32 Read article
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Study of ECG Signal Features Extraction using Wavelet Transform: A Review
Abstract: QRS-complex detection is used for analysis of ECG signal. ECG gives the information about electrical activity of the heart Since an ECG is a non-stationary signal and consists of various types of noise such as baseline wander, power line interference and muscle noise. To eliminate such types of noise wavelet transform is used. Mother wavelet is widely used to extract information from these types of signal by denoising. This paper …
Published in Current Trends in Signal Processing · Vol. 4, Issue 2, 2014 · pp. 1–6 Read article
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AI-Powered ECG Prediction System for Detecting Cardiovascular Disease
Abstract: The proposed AI-powered CardioSmart Analyzer, an electrocardiogram (ECG) prediction system, presents an innovative and scientifically rigorous approach to the real-time automated analysis of ECG signals for diagnosing various heart conditions. This research focused on building a predictive model to identify cardiovascular diseases (CVD) using ECG data. A dataset comprising 2,840 12-lead ECG recordings was gathered from medical facilities in Gazipur, Bangladesh, over the period from June to August 2024. The …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 3, 2025 · pp. 51–85 Read article
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A Survey on Neural Network based Classifier for Arrhythmia Detection
Abstract: Electrocardiogram (ECG) is one of the important diagnostic tool for the detection of the heart problem. Increasing number of cardiac patients need automatic detection techniques for various abnormalities or arrhythmias of the heart to reduce pressure on physicians and share their load. Coronary Care Units (CCUs) emphasizes on the task of accurate analysis of ECG signal at an early stage that can prevent disease, like tachycardia, to escalate there by …
Published in Journal of Communication Engineering & Systems · Vol. 8, Issue 2, 2018 · pp. 88–97 Read article
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Elimination of Noise on ECG Signal Using Adaptive Filter
Abstract: There are various biomedical signals present in the human body, by examining these biomedical signal one can check the health condition of a person who is clinically fit or not. Electrocardiogram (ECG) is one of them. ECG plays an important role in the primary diagnosis, prognosis and survival analysis of heart diseases. This ECG signal is corrupted by various noises like power line interference, baseline wandering etc. One way to …
Published in Current Trends in Signal Processing · Vol. 6, Issue 3, 2016 · pp. 9–15 Read article
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ECG De-noising Techniques and Optimal Feature Selection Using Principle Component Analysis
Abstract: AbstractECG (Electrocardiography) is used to record and determine the condition of the heart. This paper provides an overview of various ECG de-noising techniques that are used to eliminate different type of noises; therefore, noise reduction procedure to be performed to eliminate different type of noises such as baseline wander, dc offset and high frequency interference, and then the pre-processed signal is used to extract features from the ECG signal. This …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 5, Issue 1, 2018 · pp. 14–20 Read article
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Ventricular Arrhythmia Detection Techniques for ECG Signal: A Survey Approach
Abstract: Electrocardiogram (ECG) represents the electrical activity of the heart and is used to measure the rate and regularity of heartbeats. In this paper we propose a method for an Independent Component Analysis (ICA) based detection and classification of the ventricular arrhythmia. The malignant ventricular arrhythmia database from www.physionet.org/physiobank/database/vfdb has been utilized for evaluating the algorithm over MATLAB interface. This scheme assimilates ICA and probabilistic neural network for classifying critical arrhythmia …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 5, Issue 1, 2015 · pp. 25–30 Read article
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Stationary Wavelet and Recursive Least Square Filtering Based Fetal ECG Data Extraction from Composite Abdominal Signal
Abstract: This paper introduces a fast methodology for fetal ECG extraction based on stationary wavelet and noise canceler adaptive filter with the recursive least square filter. Firstly, signals are preprocessed by moving averaging filter for removing baseline wander. The stationary wavelet and the recursive least square filter is applied on preprocessed signal that effectively separates the maternal ECG and extracts fetal ECG (fECG) from the abdominal ECG. Finally, fetal ECG(fECG) is …
Published in Current Trends in Signal Processing · Vol. 7, Issue 2, 2017 · pp. 46–58 Read article
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A Combined ECG and PPG Signal Powered Artificial Intelligence-Based Prediction Model for Stroke
Abstract: Stroke is one of the most common causes of morbidity and mortality around the world, and emphasis on prevention and early detection strategies cannot be overstated. This review aims to integrate techniques of artificial intelligence with electrocardiogram and photoplethysmogram signals to enhance stroke prediction and monitoring of cardiovascular health. All in all, the application of artificial intelligence that incorporates machine learning, deep learning, or hybrid models gives robust tools toward …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 2, 2025 · pp. 18–26 Read article
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Evaluation of OTA Amplifier Using EEG Signals
Abstract: AbstractOver the last few years, there has been a tremendous exploration in VLSI industries in response to scaling trends towards deep submicron technology. Demand for low power and efficient amplification are rising in day-to-day life. In the process of scaling the CMOS nanometer demand low supply, which is helped to design digital circuit realization at very low power consumption. But it is not valid for analog circuit realization. The related …
Published in Trends in Opto-electro & Optical Communication · Vol. 10, Issue 2, 2020 · pp. 5–14 Read article