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Non-invasive method for blood glucose monitoring using ECG signal

Fellah Arbi Khadidja, Soulimane Sofiane, Saffih Faycal

Polish Journal of Medical Physics and Engineering

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2023

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Vol. 29, Iss. 1

1--9

EN

Introduction: Tight glucose monitoring is crucial for diabetic patients by using a Continuous Glucose Monitor (CGM). The existing CGMs measure the Blood Glucose Concentration (BGC) from the interstitial fluid. These technologies are quite expensive, and most of them are invasive. Previous studies have demonstrated that hypoglycemia and hyperglycemia episodes affect the electrophysiology of the heart. However, they did not determine a cohort relationship between BGC and ECG parameters. Material and method: In this work, we propose a new method for determining the BGC using surface ECG signals. Recurrent Convolutional Neural Networks (RCNN) were applied to segment the ECG signals. Then, the extracted features were employed to determine the BGC using two mathematical equations. This method has been tested on 04 patients over multiple days from the D1namo dataset, using surface ECG signals instead of intracardiac signal. Results: We were able to segment the ECG signals with an accuracy of 94% using the RCNN algorithm. According to the results, the proposed method was able to estimate the BGC with a Mean Absolute Error (MAE) of 0.0539, and a Mean Squared Error (MSE) of 0.1604. In addition, the linear relationship between BGC and ECG features has been confirmed in this paper. Conclusion: In this paper, we propose the potential use of ECG features to determine the BGC. Additionally, we confirmed the linear relationship between BGC and ECG features. That fact will open new perspectives for further research, namely physiological models. Furthermore, the findings point to the possible application of ECG wearable devices for non-invasive continuous blood glucose monitoring via machine learning.

2

Entropies for automated detection of coronary artery disease using ECG signals: A review

Acharya U. R., Hagiwara Y., Koh J. E. W., Oh S. L., Tan J. H., Adam M., Tan R. S.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 2

373--384

EN

Coronary artery disease (CAD) develops when coronary arteries are unable to supply oxygen-rich blood to the heart due to the accumulation of cholesterol plaque on the inner walls of the arteries. Chronic insufficient blood flow leads to the complications, including angina and heart failure. In addition, acute plaque rupture may lead to vessel occlusion, causing a heart attack. Thus, it is encouraged to have regular check-ups to diagnose CAD early and avert complications. The electrocardiogram (ECG) is a widely used diagnostic tool to study the electrical activity of the heart. However, ECG signals are highly chaotic, complex, and non-stationary in their behaviour. It is laborious, and requires expertise, to visually interpret these signals. Hence, the computer-aided detection system (CADS) is developed to assist clinicians to interpret the ECG signals fast and reliably. In this work, we have employed sixteen entropies to extract the various hidden signatures from ECG signals of normal healthy persons as well as patients with CAD. We observed that the majority of extracted entropy features showed lower values for CAD patients compared to normal subjects. We believe that there is one possible reason which could be the decreased in the variability of ECG signals is associated with reduced heart pump function.

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Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique

Kumar S., Panigrahy D., Sahu P. K.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 2

297--312

EN

In this paper, the investigation on effectiveness of the empirical mode decomposition (EMD) with non-local mean (NLM) technique by using the value of differential standard deviation for denoising of ECG signal is performed. Differential standard deviation is calculated for collecting information related to the input noise so that appropriate formation in EMD and NLM framework can be performed. EMD framework in the proposed methodology is used for reduction of the noise from the ECG signal. The output of the EMD passes through NLM framework for preservation of the edges and cancel the noise present in the ECG signal after the EMD process. The performance of the proposed methodology has been validated by using added white and color Gaussian noise to the clean ECG signal from MIT-BIH arrhythmia database at different signal to noise ratio (SNR). The proposed denoising technique shows lesser mean of percent root mean square difference (PRD), mean square error (MSE), and better mean SNR improvement compared to other well-known methods at different input SNR. The proposed methodology also shows lesser standard deviation PRD, MSE, and SNR improvement compared to other well-known methods at different input SNR.