ECG Signal Spectral Analysis Approaches for High-Resolution Electrocardiography

Baldin, Alexander V.; Dosko, Sergey; Kucherov, K. V.; Liu, Bin; Spasenov, A. Yu.; Utenkov, V. M.; Zhuk, D. M.

doi:10.1007/978-3-030-12082-5_18

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Pre-processing is done by: Segmentation (gray scale and binary), Morphology (Dilation and Erosion), as well as changes to ECG graphic image, while feature extraction is to find the peaks of PQRST, Heart Rate (HR) and ST-Deviation [8]. Conducted the study by implementing a one-dimensional analysis approach (duration of the heart cycle) of heart rate variability to multidimensional analysis (shape parameters and peak location of the heart cycle) analysis of heart parameters [9]. Conducted an ECG signal study with a wavelet, where the threshold function is improved, and resulted in the best SNR improvement using the MIT-BIH arrhythmia database [10].…”

Section: Related Workmentioning

confidence: 99%

Image Analysis of 12 Lead Electrocardiogram Using Wavelet Transformation

Darwan¹,

Anwar²,

Indra³

et al. 2023

ETJ

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The heart is one of the most important human organs. One of instruments to detect cardiac abnormalities is the electrocardiogram (ECG). This research tries to analyze ECG image in normal heart condition from ECG machine. The previous research related to the pre-processing process is the same, only at the feature extraction process look for peaks P, Q, R, S, T, heart rate, and Deviation-ST. While this research is the characteristic extraction process using wavelet transformation. The image of lead ECG 12 is processed using discrete wavelet transforms with decomposition up to ten levels, by searching for mean square error (MSE). The type of mother wavelet and the wavelet order used are Daubechies (db) with 1 (db1 (Haar)). The smallest MSE value decomposition results are obtained at the level 5, which are lead I, II, III, aVR, aVF, V4 and V5, lead V1 & V2 on level 4, for aVL (level 9), V3 (level 7) and V4 (level 6). It is expected that such research can be followed up to the identification model of cardiac abnormalities using wavelets.

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Section: Related Workmentioning

confidence: 99%

Image Analysis of 12 Lead Electrocardiogram Using Wavelet Transformation

Darwan¹,

Anwar²,

Indra³

et al. 2023

ETJ

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show abstract

“…To improve the capability of methods to diagnose, a group of researchers was able to focus on the utilization of high-resolution electrocardiography spectral methods. They applied the electrocardiography spectral methods in terms of analyzing the variability of cardiac cycle spectral analysis results [8]. To implement their work, the researchers proposed a method wherein they initially start with a single dimension analysis of the heart rate variability, similar to that in [9].…”

Section: Electrocardiogramsmentioning

confidence: 99%

Development of a Real-Time ECG Signal Transmission Monitoring Algorithm

Don¹

2020

IJETER

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This research will focus on the creation of a simulated real-time ECG signal transmission Algorithm. A MATLAB program would be created to implement the algorithm and monitor the heart rate of the patient. The study focuses on the implementation of Electrocardiograms (ECG) through simulation and would receive the data through a transmitter to a receiver in real-time. The researchers tackled this topic because of its timeliness in present time society wherein most people neglect their physical health. The researchers will solve this problem through a simulation of an ECG signal using the Matrix Laboratory (MATLAB) software. The system will utilize an ECG sampled signal that was converted digitally so that it could be manipulated by MATLAB. An FSK line encoder will be used, and an additive white Gaussian noise will be applied to simulate real-life noise. The expected results will show that the simulation that will be carried out will create an accurate result with a good signal resolution. This study will prove that the MATLAB software is capable of transmitting and receiving ECG signals.

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“…Numerous problems in signals acquired from biological systems can be tackled by applying spectrum analysis as a preliminary measurement before further processing is performed (Fong et al, 2016). Sophisticated spectrum measurements can be used to obtain information from bioelectrical signals; for example, EEG (Manshouri, Maleki, and Kayikcioglu, 2018;Costa and Cabral, 2000) and ECG (Baldin et al, 2020). Spectrum analysis by PSD encompasses various measurements.…”

Section: Introductionmentioning

confidence: 99%