A Review Paper on Analysis of Electrocardiograph (ECG) Signal for the Detection of Arrhythmia Abnormalities

Joshi, Anand Kumar; Tomar, Arun; Tomar, Mangesh

doi:10.15662/ijareeie.2014.0310028

Cited by 39 publications

(17 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The duration of each phase in the cardiac cycle can be extracted from the ECG signal. 12 Concerning the presented study and with regard to heart dimension, there corresponds a resonant frequency and, consequently at each time, we can correspond a frequency, as shown in Figure 7. The results prove well to reveal that it is possible to extract the ECG signals from the antenna's resonance frequencies and that the implanted antenna will be useful for detecting not only the heart movement but also the ECG signals.…”

Section: Resultsmentioning

confidence: 82%

“…As already mentioned in Section 2, the heartbeat is a time‐dependent process. The duration of each phase in the cardiac cycle can be extracted from the ECG signal . Concerning the presented study and with regard to heart dimension, there corresponds a resonant frequency and, consequently at each time, we can correspond a frequency, as shown in Figure .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Remote monitoring of cardiac activity using a flexible loop antenna

Bouazizi

Zaibi

Basir

et al. 2018

Int J RF Microw Comput Aided Eng

View full text Add to dashboard Cite

In this paper, a meandered loop antenna, useful for medical cardiac monitoring application, more specifically, for heart beating close scrutiny is designed and experimentally tested. The proposed antenna is operating at the MICS band displays a high flexibility which allowing it to be curved easily around the heart's wall. In both, Simulation and measured results the antenna shows a sensitive nature any change introduced in heart's model size and would certainly bring about a noticeable shift in the antenna's resonant frequency. Linking the frequency variations taking place in concord with the heart movement turns out to stand as an effective key that provides extremely valuable information about the heart's activities. A correspondence between the obtained frequency variation linking with the change in size of the heart. In addition to the frequency response evaluation, the antenna characteristically displays an omni‐directional radiation pattern, which necessarily fits it for in‐body operation.

show abstract

Section: Resultsmentioning

confidence: 82%

Section: Resultsmentioning

confidence: 99%

Remote monitoring of cardiac activity using a flexible loop antenna

Bouazizi

Zaibi

Basir

et al. 2018

Int J RF Microw Comput Aided Eng

View full text Add to dashboard Cite

show abstract

“…To track and interpret the electric activity of the heart, ECG can be used [33]. There is a constant variation in human heart beat, which is recorded by ECG.…”

Section: Feature Extraction Of Heart Rate Variabilitymentioning

confidence: 99%

Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

2021

View full text Add to dashboard Cite

Recent developments of portable sensor devices, cloud computing, and machine learning algorithms have led to the emergence of big data analytics in healthcare. The condition of the human body, e.g. the ECG signal, can be monitored regularly by means of a portable sensor device. The use of the machine learning algorithm would then provide an overview of a patient’s current health on a regular basis compared to a medical doctor’s diagnosis that can only be made during a hospital visit. This work aimed to develop an accurate model for classifying sleep stages by features of Heart Rate Variability (HRV) extracted from Electrocardiogram (ECG). The sleep stages classification can be utilized to predict the sleep stages proportion. Where sleep stages proportion information can provide an insight of human sleep quality. The integration of Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) was utilized for selecting features and determining the number of hidden nodes. The results were compared to Support Vector Machine (SVM) and ELM methods which are lower than the integration of ELM with PSO. The results of accuracy tests for the combined ELM and PSO were 62.66%, 71.52%, 76.77%, and 82.1% respectively for 6, 4, 3, and 2 classes. To sum up, the classification accuracy can be improved by deploying PSO algorithm for feature selection.

show abstract

“…The ratio of LF to HF appears to be a sensitive measure of the autonomic nervous system's (ANS) response to a sudden change in cardiovascular control and this ratio represents an evaluation of the ANS balance. [1][2][3][4][5][6][7][8][9][10]…”

Section: Introductionmentioning

confidence: 99%

Analysis of heart rate variability based on quantitative approach

Tun¹

2018

MOJPB

View full text Add to dashboard Cite

Heart rate variability (HRV) is a measure of variations of heart rate between two successive heart beats and it is a relatively new method for assessing the effects of stress on our body. It is measured as the time gap between our heart beats that varies as we breathe in and out. Simple measures of the small changes in each beat of our heart can provide a wealth of information on the health of our heart and nervous system; such measures are called heart rate variability or HRV. It is usually calculated by analyzing the time series of beat-to-beat intervals from ECG signal. HRV is measured based on variation of time in milliseconds between two heartbeats also known as RR interval where R is a point corresponding to the peak of the QRS complex of the ECG wave, and RR is the interval between successive Rs. The evaluation of HRV can provide an indication of cardiovascular health. To accomplish this evaluation, the raw ECG signal is firstly de-noised by the application of DWT by automatically determining the optimal order of decomposition. After the purification, the wavelet filtering is used for R-peak detection since this method is efficient and accurate in the compute of the R peaks positions without changing of the shape or position of the original signal. Finally, the RR interval is analyzed because it consists in studying the HRV. The values of RR intervals are then plotted versus time, giving a curve called RR tachogram. After all, the Lomb-Scargleperiodogram of frequency-domain method (spectral analysis) is used to investigate the sympathovagal balance of HRV from RR tachogram. By using the Lomb-Scargleperiodogram for power spectral density estimation, we have no need to make de-trending and re-sampling. As a result, all signal shows as arrhythamia by comparing with the normal value of standard measurement. And then, the traditional time-domain method (statistical analysis) and spectral analysis of frequency-domain method are applied to analyze the variation of heart rate in arrhythmia database. The MATLAB programming is used to implement the algorithm for HRV analysis and MIT/BIH arrhythmia databases is used as data inputs. Citation: Tun HM, Naing ZM, Moe WK, et al. Analysis of heart rate variability based on quantitative approach. Citation: Tun HM, Naing ZM, Moe WK, et al. Analysis of heart rate variability based on quantitative approach. Citation: Tun HM, Naing ZM, Moe WK, et al. Analysis of heart rate variability based on quantitative approach. Citation: Tun HM, Naing ZM, Moe WK, et al. Analysis of heart rate variability based on quantitative approach. Citation: Tun HM, Naing ZM, Moe WK, et al. Analysis of heart rate variability based on quantitative approach.

show abstract

A Review Paper on Analysis of Electrocardiograph (ECG) Signal for the Detection of Arrhythmia Abnormalities

Cited by 39 publications

References 10 publications

Remote monitoring of cardiac activity using a flexible loop antenna

Remote monitoring of cardiac activity using a flexible loop antenna

Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

Analysis of heart rate variability based on quantitative approach

Contact Info

Product

Resources

About