Journal of Arak University of Medical Sciences

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Showing 2 results for Electrocardiogram

Enhancement and Denoising of ECG Signals using Adaptive Kalman Filter

Masoomeh Ashoorirad, Rasool Baghbani Khezerloo,
Volume 18, Issue 9 (12-2015)

Abstract

Background: Electrocardiogram signal (ECG) is a graphical representation of the heart activity. Processing and analysis of these morphological changes can result in visual diagnosing some cardiac diseases. However, various types of noises and disturbances in ECG influence the visual recognition and feature extraction from it. The aim of this research is to eliminate different noises from ECG and to enhance its quality.

Materials and Methods: In this study, an adaptive Kalman filter is developed by using Bayesian model. Considering simplification and Gaussian distribution for measurement noise, complicated mathematical equations were converted to simple relations and therefore implementation was simplified.

Results: In this paper, by designing an adaptive Kalman filter, the signal to noise ratio (SNR) has increased to 21.46dB. Adaptive Kalman filter based on Beyesian framework could model dynamic variations of ECG signal by estimating covariance matrix for measurement noise.

Conclusion: In despite of Kalman filters that use parametric functions to model ECG signal, the adaptive Kalman filter introduced in this paper uses real ECG records for modeling. Parametric functions which could model dynamic variations of ECG need a lot of analytical functions and this decreases the time of filtering process but the adaptive Kalman filter proposed in this research has a high speed and could be used in real time applications.

A Two-stage Approach Using Artificial Neural Networks for Diagnosis of Heart Diseases Based on ECG Data

Majid Mehrad, Majid Nojavan, Sedigh Raissi, Mehrdad Javadi,
Volume 25, Issue 2 (5-2022)

Abstract

Background and Aim Most heart diseases show symptoms on ECG, but diagnosing heart disease with ECG requires the knowledge and experience of medical specialized. Because these specialists may not always be available, it is necessary to design tools to diagnose heart disease in these situations. In this paper, a two-stage approach based on artificial neural networks is designed to diagnose heart disease using ECG information.In this study, we aim to propose a two-stage approach using artificial neural network (ANN) to diagnose heart disease based ECG data.
Methods & Materials To design the proposed approach, first the ECG data of 861 patients referred to medical centers in Arak, Iran were collected. The data were examined based on the opinions of specialists. Then, 154 features from ECG were used as inputs to the proposed model. In the first stage, an ANN was used to detect the ECG status (usable and unusable). In the second stage, using the usable ECG data, an ANN was used to diagnose the presence or absence of heart disease. Finally, the performance of the two-stage approach was evaluated and its accuracy and precision in determining the ECG quality and heart disease diagnosis were determined.
Ethical Considerations This study was approved by the ethics committee of Arak University of Medical Sciences (Code: IR.ARAKMU.REC.1400.138).
Results In the proposed approach, the ANN used for the determining the ECG status had a precision of 97.1% and an accuracy of 97.3%. The ANN used for the diagnosis of heart disease had a precision of 95.8% and an accuracy of 95.4%.
Conclusion Considering the high efficiency of the proposed approach in determining of ECG status and diagnosing heart disease, it is possible to use this approach to help the treatment staff.

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