Cyclic spectral analysis of electrocardiogram signals based on GARCH model

Mihandoost, Sara; Amirani, Mehdi Chehel

doi:10.1016/j.bspc.2016.07.012

Cited by 17 publications

(8 citation statements)

References 34 publications

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“…1. The considered datarecord length is significantly larger than that adopted in [18], [29], [43], and [46] (less than 10 s), where a cyclostationary model is assumed for the ECG signal y(t).…”

Section: A Experimentsmentioning

confidence: 99%

“…The structure of the electrocardiogram (ECG) signal is consequence of a periodic generating mechanism, the heart pulsation, and other less predictable phenomena like the propagation of the electrical wave throughout the heart and the body and possibly artifacts. This combination of periodic and random phenomena suggests that the cyclostationary model could be exploited for ECG signal modeling [18], [19], [29], [43], and [46]. The cyclostationary model has been exploited in [17] for the fetal PQRST extraction, in [15] for arrhythmia classification, in [16] for denoising the VOLUME 4, 2016 ECG signal, in [23] and [24] for heart and respiration rate monitoring, in [12] for removing ballistocardiogram artifacts, in [13] for heart and lung sound separation, and in [26] for heart sound selection.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Electrocardiogram as Oscillatory Almost-Cyclostationary Process

Napolitano

2022

IEEE Access

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A new model for the electrocardiogram (ECG) signal is proposed. Specifically, the ECG signal is modeled as an amplitude-modulated and time-warped version of a cyclostationary process which is the sum of a periodic signal and a zero-mean cyclostationary term. For the proposed model, the secondorder characterization is derived in both time and frequency domains. The autocorrelation function is shown to be the superposition of amplitude-and angle-modulated sine waves and the Loève bifrequency spectrum a spread version of that of the underlying cyclostationary process. The signal model belongs the recently introduced class of the oscillatory almost-cyclostationary processes. A procedure for estimating the secondorder statistical functions in both time and frequency domains is outlined. The effectiveness of the proposed model and of the estimation procedure is corroborated by measurements on real ECG signals which are in full agreement with the theoretical analytical expressions. The proposed model is shown to be effective with observation intervals much larger than those adopted up to now with the classical cyclostationary model and is suitable to be exploited for arrhythmia modeling and characterization, and for diagnosis and biometric purposes.

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Section: A Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling the Electrocardiogram as Oscillatory Almost-Cyclostationary Process

Napolitano

2022

IEEE Access

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“…[23] The ECG acquisition cart continued to include an integrated microprocessor to enable the processing of the ECG. [21] When the ECG was a noise signal, the interpretation algorithm had an increased failure rate. Through running digital signal processing algorithms, the microprocessor improved the signal to noise ratio to eliminate background drift and attenuate liner interference.…”

Section: Fig 1 Patients Risk For Heart Failurementioning

confidence: 99%

Emergency Notification Using Combination Algorithm with Recognition ECG Signal

Naijit

2020

Int. J. Onl. Eng.

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<p class="p1">Intensive Care Unit (ICU) Rooms usually have several detectors attached to each patient providing intensive care, and several processors control and interpret. If the processor detects an abnormality, the medical professional office will be alerted. Nevertheless, many patients with heart disease are concerned with day-to-day behaviors such as hard work, battle, exercise, shock, fight, and war. Become due to clinical depression and erectile impotence this induces anxiety and fear. The boundaries of your heart muscle and coronary strength are unclear. They want a warning that is quick and accurate before they lose control. We develop signal recognition for an algorithm that is very fast and accurate. It helps alert patients to avoid the operation of risk. Nevertheless, it is able to transfer information from the heartbeat network to the doctor's guidance system. The research would analyze the 200 signals from multiple ECG signals. Integration Component Diagnosis (ICD) is capable of extraordinary reliability of identification than the 17.10-41.93 average percentage of the Automata Matching Process which takes less time than the other 29.37 average percentage process and can alert within 12 seconds. It cannot be identified in a pattern other than without the detection of an ECG signal. In the experimental, the signal is used to distinguish 20 ECG signal patterns.</p>

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“…We investigated this theory, and the findings support it. In this study, we investigated the cyclostationarity of the Slow Cortical Potentials (SCP) EEG signals, following our previous studies on ECG [74] and EEG [75] signals. Cyclostationary signals are continuous random signals that undergo periodic changes in their statistical features across time [76].…”

Section: Introductionmentioning

confidence: 99%

“…According to the results of the experiments, the suggested method improves the retrieval accuracy, and the CCR of the recovered features is more distinct compared to typical discrete wavelet transform techniques. Mihandoost et al [74] and [75] presented the SCF analysis as a feature extraction approach for ECG representation and epilepsy diagnosis, respectively. The authors use FAM for SCF calculation and also employ a statistical model called Generalized Autoregressive Conditional Heteroscedasticity (GARCH), as a describing model by taking into account the heteroscedastic quality of SCF coefficients, and also to decrease the number of features.…”

Section: Introductionmentioning

confidence: 99%

An EEG-Based Brain-Computer Interface Using Spectral Correlation Function

2023

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Brain-Computer Interface (BCI) is a promising technique because of its wide variety of applications, from treating cognition in humans to person authentication. Brain signals can be transmitted straight to a prosthetic device from the BCI system, without the need for nerve or muscle activity. For accurately identifying the transmitted signals at the prosthetic device, considering the nature of the Electroencephalography (EEG) signal, and extracting the most informative features are effective keys. In this paper, we studied the cyclostationarity of the Slow Cortical Potential (SCP) EEG signals for BCI applications, following our previous studies. Cyclostationary analysis reveals the hidden periodicity in the signal and provides a second-order statistical description in the frequency domain. We used the FFT Accumulation Method (FAM), an effective computational algorithm, to extract the features of the Spectral Correlation Function (SCF). The features are classified using SVM RBF, SVM polynomial, and K-Nearest Neighbor classifiers, and they are considered with different pre-processing. Our research indicates that the SCP EEG signal has cyclostationary properties and this idea is applied to the BCIs as well. The classification accuracy on the BCI Competition 2003 dataset Ia's increased considerably, by spotting the intrinsic correlation between just two EEG signals.

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Cyclic spectral analysis of electrocardiogram signals based on GARCH model

Cited by 17 publications

References 34 publications

Modeling the Electrocardiogram as Oscillatory Almost-Cyclostationary Process

Modeling the Electrocardiogram as Oscillatory Almost-Cyclostationary Process

Emergency Notification Using Combination Algorithm with Recognition ECG Signal

An EEG-Based Brain-Computer Interface Using Spectral Correlation Function

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