Early detection of sudden cardiac death using Poincaré plots and recurrence plot-based features from HRV signals

Houshyarifar, Vahid; Amirani, Mehdi Chehel

doi:10.3906/elk-1509-149

Cited by 24 publications

(13 citation statements)

References 42 publications

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“…Riedl et al [19] analyzed the characteristics and application fields of generalized recurrence plots. In general, the nonlinear time-series analysis methods based on the recurrence plot theory have received much attention from researchers in various fields, and they have been successfully applied to many fields, such as geology [20][21][22][23], ecology and biology [24][25][26], neuroscience [27][28][29][30][31], economic dynamics [32][33][34], industrial manufacturing, mechanical damage, and monitoring [25,[35][36][37][38][39][40], medicine [41][42][43][44], image processing, and audio and video analysis [45][46][47]anda CNN-based magnetic fingerprinting system using recurrence plots (RPs) was proposed as sequence fingerprints. Ref.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis

2022

Journal of Environmental and Public Health

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The nonlinear time-series analysis method, based on the recurrence plot theory, has received great attention from researchers and has been successfully used in multiple fields. However, traditional recurrence plots that use Heaviside step functions to determine the recursive behavior of a point in the phase space have two problems: (1) Heaviside step functions produce a rigid boundary, resulting in information loss; and (2) the selection of the critical distance, ε , is crucial; if the selection is inappropriate, it will result in a low-dimensional dynamics error, and as of now, there exists no unified method for selecting this parameter. With regard to the problems described above, the novelty of this article lies in the following: (1) when determining the state-phase point recursiveness, a Gaussian function is used to replace the Heaviside function, thereby solving the rigidity and binary value problems of the recursive analysis results caused by the Heaviside step function; and (2) texture analysis is performed on a recurrence plot, new ways of studying complex system dynamics features are proposed, and a system of complex system dynamic-like measurement methods is built.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis

2022

Journal of Environmental and Public Health

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“…Hooshyarifar et al using their proposed algorithm and SVM and KNN classifiers predicted SCD 4 and 5 minutes before the occurrence with 92.14% (SVM) and 94.10% (KNN) accuracy [19].…”

Section: Discussionmentioning

confidence: 99%

“…Hooshyarifar et al (2014) studied ECG signal classification in the nonlinear domain [19]. By Poincaré feature extraction plot and mapping HRV signal inspection, they predicted SCD 5 minutes before with an accuracy greater than 92%.…”

Section: Introductionmentioning

confidence: 99%

A Review of the Methods for Sudden Cardiac Death Detection: A Guide for Emergency Physicians

Moridani

Marjani²

2020

Int. J. Onl. Eng.

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Sudden cardiac death (SCD) is an unexpected death of a person with or without knowing cardiac causes are often occurring in less than an hour after the incidence of symptoms. In the case of physicians' knowledge of this incident, they can make appropriate decisions for the patients at-risk and reduce the number of such deaths significantly. The purpose of this paper is to examine different methods for predicting sudden cardiac death using electrocardiogram (ECG) signal from 1998 to recent years that can contribute to researchers to become familiar with the wide range of research conducted in this field.In this paper, studies using various methods to predict sudden cardiac death that has applied the data from the Physionet and MIT-BIH databases with a sampling frequency of 256 samples per second are reviewed. Both types of data have normal and abnormal sampling labels and the data recording time varies from a few seconds to minutes. In the field of SCD prediction, various studies have addressed the processing of the electrocardiogram (ECG) signal as well as the heart rate variability (HRV) signal in different domains, including time, time-frequency, and nonlinear domain. In time-domain processing the statistical characteristics of time signal such as the mean and standard deviation of heart rate, the mean and standard deviation of RR intervals, and Root Mean Square of the Successive Differences (RSSD) are used. Also, in the frequency domain, the power spectral density (PSD) of the signal energy is used in a very-low-frequency band, low-frequency band and, high-frequency band. Similarly, in the nonlinear domain, features such as Poincare plot, detrended fluctuation analysis (DFA), common entropy, wavelet transform coefficients (WTC), and features of the recursive graph including Lmax, Lmean, correlation dimension (CD), etc. are used. In all of the proposed algorithms so far, researchers have been trying to inform the sudden death alarm in a larger interval than the time of death by separating the signals into different time periods and extracting various features.To evaluate the results of the proposed methods, each of the researchers analyzed the a-few-minute intervals before the SCD. Different classification methods are available to identify the efficiency of the proposed algorithm, such as support vector machine (SVM), multilayer perceptron neural network (MLP), radial base function neural network (RBF), k-nearest neighbor (KNN) and mixture expert (ME). The use of features introduced in different domains and different classifiers has led to the observation of different horizons of prediction in various studies. The results of these predictions are often free from the interpretations of clinical symptoms, and their maximum presented time with acceptable validity eventually reaches 4 minutes before the event, which is not an acceptable time for people who have attacked outside the hospital. Accordingly, the most prominent of these evaluations is the mixture expert methodology in which the best feature extraction methods are used in a new method for selecting the optimal feature space locally. This method makes it possible to select different features every minute before the event by choosing the optimal features for each one-minute interval of the signal as an episode which increases the prediction time from 4 minutes before the death to 12 minutes and allows the interpretation of clinical symptoms in terms of multiplication of the presence of the features per minute.Given the non-linear nature of the HRV signal and the similarity of the ECG signal in many time intervals, the use of the HRV signal has become more popular among scholars. The analysis of various studies shows that by approaching the time of death, linear features (time and frequency) can be predictive of death according to the sensible behavior and variations in patients’ signal. Instead of moving away from the death interval, the use of chaotic and non-linear features is more effective. Therefore, a more precise selection of features in this area can be useful for increasing the horizon of prediction of death.

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“…However, the differences gradually decreased as the frequency increased. Hence, five spectrum frequency ranges were proposed to better analyze the differences: (1-5 Hz), (1-10 Hz), (5-10 Hz), (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25), and . These ranges were suggested on the basis of the typical ECG frequency spectrum's distribution shown in Fig.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

“…Early detection and continuous monitoring of CVD could prevent or delay it from exacerbating to improve a person's productivity and quality of life, and consequently reduce the country's burden of disease cost. Researchers are actively developing cost-effective diagnosis methods, including pointof-care testing, for various types of CVDs such as coronary artery disease (CAD) [6,7], arrhythmia [8][9][10][11][12][13][14], myocardial ischemia (MI) [15][16][17][18][19], and sudden cardiac death (SCD) [20,21], which mostly utilize noninvasive methods of ECG.…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular Disease Prediction Among the Malaysian Cohort Participants Using Electrocardiogram

Dangi¹,

Bhagat²,

Dixit³

2022

Computers, Materials &Amp; Continua

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A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The Malaysian Cohort project. An automated peak detection algorithm to detect nine fiducial points of electrocardiogram (ECG) was developed. Forty-eight features were extracted in both time and frequency domains, including statistical features obtained from heart rate variability and Poincare plot analysis. These include five new features derived from spectrum counts of five different frequency ranges. Feature selection was then made based on p-value and correlation matrix. Selected features were used as input for five classifiers of artificial neural network (ANN), k-nearest neighbors (kNN), support vector machine (SVM), discriminant analysis (DA), and decision tree (DT). Results showed that six features related to T wave were statistically significant in distinguishing CVD and non-CVD groups. ANN had performed the best with 94.44% specificity and 86.3% accuracy, followed by kNN with 80.56% specificity, 86.49% sensitivity and 83.56% accuracy. The novelties of this study were in providing alternative solutions to detect P-onset, P-offset, T-offset as well as QRS-onset points using discrete wavelet transform method. Additionally, two out of the five newly proposed spectral features were significant in differentiating both groups, at frequency ranges of 1-10 Hz and 5-10 Hz. The prediction outcomes were also comparable to previous related studies and significantly important in using ECG to predict cardiac-related events among CVD and non-CVD subjects in the Malaysian population.

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Early detection of sudden cardiac death using Poincaré plots and recurrence plot-based features from HRV signals

Cited by 24 publications

References 42 publications

[Retracted] Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis

[Retracted] Research on Recurrence Plot Feature Quantization Method Based on Image Texture Analysis

A Review of the Methods for Sudden Cardiac Death Detection: A Guide for Emergency Physicians

Cardiovascular Disease Prediction Among the Malaysian Cohort Participants Using Electrocardiogram

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