scholarly journals Hurst Exponent-Based Nonlinear Analysis for the Identification of Arrhythmia-Affected Cardiac Systems

2021 ◽  
Author(s):  
Chiranjit Maji ◽  
Pratyay Sengupta ◽  
Anandi Batabyal ◽  
Hirok Chaudhuri
Keyword(s):  
Hurst Exponent ◽  
Human Heart ◽  
Normal Subjects ◽  
Temporal Variations ◽  
Intrinsic Mode Functions ◽  
Complex Biological System ◽  
Medical Practitioners ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Non Linear

Abstract The stochastic nature of the human heart, a complex biological system, is evident from electrocardiogram (ECG) signals, which are weak, non-linear and non-stationary signals. These temporal variations of electromagnetic pulses emanated from the heart are instrumental in indicating the cardiac health. The Empirical Mode Decomposition (EMD) technique was employed in order to decompose a total of 64 ECG signal data of arrhythmic and normal subjects, obtained from widely used MIT-BIH databases, into a finite number of Intrinsic Mode Functions (IMFs). The rationale behind using this strategy was to extract non-linear features of ECG signals which are not explicitly expressed, while keeping the original signal unaltered. Following removal of non-stationary noises from the ECG signals by the Savitzky-Golay (SG) filter, popular non-linear parameter Hurst Exponent (H) was estimated for every IMF by employing the R/S technique. A distinct difference between H values of 1st IMFs between normal individuals and arrhythmia affected patients was identified. This observation was further validated through an age-based and gender-based analysis, which demonstrated a unique alteration pattern with age. The study showed 94.92% probability in detection of arrhythmia in a patient. Adopting this EMD-based procedure for ECG data analysis and disease prediction may assist in reducing our dependence on intuition-based diagnosis of ECG reports by medical practitioners and may provide novel insights into the functioning of the human heart which might help develop new biomedical strategies to combat cardiac disorders.

Synchrosqueezing Transform Based Powerline Interference Reduction in ECG Recording

2020 ◽  
Vol 10 (10) ◽  
pp. 2259-2273
Author(s):  
M. Suresh Kumar ◽  
G. Krishnamoorthy ◽  
D. Vaithiyanathan
Keyword(s):  
Operation Mode ◽  
Wiener Filter ◽  
Wiener Filtering ◽  
Ecg Signal ◽  
Intrinsic Mode Functions ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Synchrosqueezing Transform ◽  
Signal Mode ◽  
Powerline Interference

This paper presents an adaptive ECG enhancement procedure based on Synchrosqueezing Transform (SST) to eliminate Powerline interference (PLI) from ECG signal. This work also incorporates the principles of modified discrete cosine transform (MDCT) and wiener filter. PLI is a major source of artifacts in the ECG signal which can affect its interpretation. Separating PLI from ECG signal poses a great challenge in the ECG analysis. The existing PLI removal techniques suffer from two major drawbacks such as Mode Mixing, inability to deal with non-stationary characteristics of signal. In this paper, we propose SST based wiener filtering approaches which can overcome the limitation of existing PLI suppression techniques. The proposed approaches undergo three stages of operation: mode decomposition, mode determination and peak restoration to filter out PLI from ECG recording. The mode decomposition uses SST to decompose the corrupted ECG signal into a sum of well separated intrinsic mode functions (IMFs). The objective is to filter out PLI from these IMFs. To do so, mode determination step which is based on Kurtosis and Crest factor is applied to categorize decomposition result into groups such as signal mode and noisy mode. Direct subtraction of the noisy mode from the corrupted ECG observation results in ECG signal with reduced peak since noise mode carries part of signal components in addition to interference. Hence, to restore the peak, wiener filter is applied on noisy mode to estimate actual PLI component. Finally, Noise free ECG signal is reconstructed by subtracting estimated PLI from the corrupted ECG signal. This paper discusses four possible PLI suppression methods which are derived by combining SST domain with wiener filter in various ways. Simulations are carried out to test the effectiveness of proposed methods. It is evident from the simulation results that the proposed methods can remove PLI of 50 Hz and its harmonics. The proposed techniques effectively removed PLI in both real and artificial ECG signals and to test its performance they are compared with state of the art methods. The SST based filtering methods outperformed other methods under the condition of PLI frequency variations. The experimental results also suggest that the SST based wiener filtering with modified reference approach offers better PLI suppression than all other methods.

APPLICATION OF EMPIRICAL MODE DECOMPOSITION–BASED FEATURES FOR ANALYSIS OF NORMAL AND CAD HEART RATE SIGNALS

2016 ◽  
Vol 16 (01) ◽  
pp. 1640002 ◽  
Author(s):  
SURABHI SOOD ◽  
MOHIT KUMAR ◽  
RAM BILAS PACHORI ◽  
U. RAJENDRA ACHARYA
Keyword(s):  
Heart Rate ◽  
Empirical Mode Decomposition ◽  
P Value ◽  
Computer Assisted ◽  
Intrinsic Mode Functions ◽  
Mean Frequency ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Artery Disease ◽  
Assisted Diagnosis

Coronary Artery Disease (CAD) is a heart disease caused due to insufficient supply of nutrients and oxygen to the heart muscles. Hence, reduced supply of nutrients and oxygen causes heart attack or stroke and may cause death. Also significant number of people are suffering from CAD around the world so timely diagnosis of CAD can save the life of patients. In this work, we have proposed computer assisted diagnosis of CAD using Heart Rate (HR) signals obtained from Electrocardiogram (ECG) signals. We have used the Empirical Mode Decomposition (EMD) technique to process the HR signals. The features namely: Second-Order Difference Plot (SODP) area, Analytic Signal Representation (ASR) area, Amplitude Modulation (AM) bandwidth, Frequency Modulation (FM) bandwidth and Fourier–Bessel expansion (FBE)- based mean frequency computed from the Intrinsic Mode Functions (IMFs) are extracted to discriminate normal and CAD subjects. Thereafter, Kruskal–Wallis statistical test is performed on these features. The features having p-value less than 0.05 are considered to be significant. Our results show that three features namely: AM bandwidth, FM bandwidth and FBE-based mean frequency are more suitable than ASR area and SODP area features for discrimination of normal and CAD subjects.

scholarly journals ANALYSIS OF TEMPORAL VARIATIONS IN TURBIDITY FOR A COASTAL AREA USING THE HILBERT-HUANG-TRANSFORM

2011 ◽  
Vol 1 (32) ◽  
pp. 25
Author(s):  
Shigeru Kato ◽  
Magnus Larson ◽  
Takumi Okabe ◽  
Shin-ichi Aoki
Keyword(s):  
Instantaneous Frequency ◽  
River Mouth ◽  
Original Data ◽  
Temporal Variations ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
The Hilbert Transform

Turbidity data obtained by field observations off the Tenryu River mouth were analyzed using the Hilbert-Huang Transform (HHT) in order to investigate the characteristic variations in time and in the frequency domain. The Empirical Mode Decomposition (EMD) decomposed the original data into only eight intrinsic mode functions (IMFs) and a residue in the first step of the HHT. In the second step, the Hilbert transform was applied to the IMFs to calculate the Hilbert spectrum, which is the time-frequency distribution of the instantaneous frequency and energy. The changes in instantaneous frequencies showed correspondence to high turbidity events in the Hilbert spectrum. The investigation of instantaneous frequency variations can be used to understand transitions in the state of the turbidity. The comparison between the Fourier spectrum and the Hilbert spectrum integrated in time showed that the Hilbert spectrum makes it possible to detect and quantify the cycle of locally repeated events.

Identification of MDOF Non-Linear Uncoupled Dynamical Systems Using Hilbert Transform and Empirical Mode Decomposition Method

2011 ◽  
Vol 255-260 ◽  
pp. 1676-1680
Author(s):  
Tian Li Huang ◽  
Wei Xin Ren ◽  
Meng Lin Lou
Keyword(s):  
Dynamical Systems ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Degree Of Freedom ◽  
Intrinsic Mode Functions ◽  
Nonlinear Properties ◽  
Identification Method ◽  
Mode Decomposition ◽  
Non Linear ◽  
The Hilbert Transform

A non-linear dynamical system identification method using Hilbert transform (HT) and empirical mode decomposition (EMD) is proposed. For a single-degree-of-freedom (SDOF) nonlinear system, the Hilbert transform identification method is good at identifying the instantaneous modal parameters (natural frequencies, damping characteristics and their dependencies on a vibration amplitude and frequency). For the multi-degree-of-freedom (MDOF) non-linear uncoupled dynamical systems, the EMD method is attempting for the decomposition of response signals into a collection of mono-components signals, termed intrinsic mode functions (IMFs). Considering the IMFs admit a well-behaved Hilbert transform, the HT identification method has been applied for the identification of nonlinear properties. The numerical simulation of a 2-dof shear-beam building model with nonlinear stiffness illustrated the proposed technique.

scholarly journals Potato price analysis of Delhi market through ensemble empirical mode decomposition

2019 ◽  
Vol 34 (01) ◽  
Author(s):  
Kapil Choudhary ◽  
Girish Kumar Jha ◽  
Rajeev Ranjan Kumar
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Agricultural Commodities ◽  
Intrinsic Mode Functions ◽  
Price Series ◽  
Mode Decomposition ◽  
Non Linear ◽  
Agricultural Price ◽  
Commodities Prices

Agricultural commodities prices depends on production, unnecessary demand, production uncertainty, market flaws etc. Due to these factors agricultural price series are non-stationary and non-linear in nature. Therefore analyzing agricultural commodities prices is considered as a challenging task. The traditional stationary approach of time series is unable to capture non-stationary and non-linear properties of agricultural price series. Non-stationary and non-linear properties present in the price series may be accurately analyzed through empirical mode decongation (EMD). In this technique, the original time series decomposed into intrinsic mode functions and residue. One of the major limitation of EMD is the presence of the mode mixing. To overcome this limitation of the EMD, we use ensemble empirical mode decomposition (EEMD). Using this technique in this study, Delhi market potato prices have been analyzed.

ANALYSIS OF ECG SIGNALS USING DATA-ADAPTIVE TIME DOMAIN FILTERING FOR CARDIOVASCULAR DISEASE DIAGNOSIS

2012 ◽  
Vol 04 (01n02) ◽  
pp. 1250006
Author(s):  
MD. RABIUL ISLAM ◽  
SOMLAL DAS ◽  
KEIKICHI HIROSE ◽  
MD. KHADEMUL ISLAM MOLLA
Keyword(s):  
Cardiovascular Disease ◽  
Low Frequency ◽  
Disease Diagnosis ◽  
Intrinsic Mode Functions ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Finite Set ◽  
Data Adaptive ◽  
Band Limited ◽  
Using Data

This paper presents a data-adaptive technique of cardiovascular disease diagnosis by analyzing electrocardiogram (ECG) signals. The separation of high-frequency (HF) and low-frequency (LF) components are performed by employing empirical mode decomposition (EMD) designed for analyzing nonstationary and non-linear signals. The EMD is used to decompose ECG signal into a finite set of band-limited AM–FM signals termed as intrinsic mode functions (IMFs). Then the LF and HF components of ECG signals are obtained by partial reconstruction based on the energy distribution of IMFs. The extracted HF and LF signals of the ECG are analyzed separately to make the remarks for better diagnosis of the cardiovascular diseases. The experimental results are also illustrated using some ECG signals of normal and abnormal subjects.

CLASSIFICATION OF ARRHYTHMIA IN ELECTROCARDIOGRAM USING EMD BASED FEATURES AND SUPPORT VECTOR MACHINE WITH MARGIN SAMPLING

2013 ◽  
Vol 12 (03) ◽  
pp. 1350015 ◽  
Author(s):  
M. SUCHETHA ◽  
N. KUMARAVEL
Keyword(s):  
Support Vector Machine ◽  
Information Source ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Mode Decomposition ◽  
Computer Based ◽  
Value Decomposition

Electrocardiogram (ECG) signals represent a useful information source about the rhythm and the functioning of the heart. Any disturbance in the heart's normal rhythmic contraction is called an arrhythmia. Analysis of Electrocardiogram signals is the most effective available method for diagnosing cardiac arrhythmias. Computer based classification of ECG provides higher accuracy and offer a potential of an affordable cardiac abnormality mass screening. The empirical mode decomposition is performed on various arrhythmia signals and different levels of intrinsic mode functions (IMF) are obtained. Singular value decomposition (SVD) is used to extract features from the IMF and classification is performed using support vector machine. This method is more efficient for classification of ECG signals and at the same time provides good generalization properties.

DECISION SUPPORT SYSTEM FOR ARRHYTHMIA BEATS USING ECG SIGNALS WITH DCT, DWT AND EMD METHODS: A COMPARATIVE STUDY

2016 ◽  
Vol 16 (01) ◽  
pp. 1640012 ◽  
Author(s):  
USHA DESAI ◽  
ROSHAN JOY MARTIS ◽  
C. GURUDAS NAYAK ◽  
G. SESHIKALA ◽  
K. SARIKA ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Subjective Evaluation ◽  
Kappa Statistic ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Non Linear ◽  
Invasive Method ◽  
Clinically Significant

Electrocardiogram (ECG) signal is a non-invasive method, used to diagnose the patients with cardiac abnormalities. The subjective evaluation of interval and amplitude of ECG by physician can be tedious, time consuming, and susceptible to observer bias. ECG signals are generated due to the excitation of many cardiac myocytes and hence resultant signals are non-linear in nature. These subtle changes can be well represented and discriminated in transform and non-linear domains. In this paper, performance of Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) methods are compared for automated diagnosis of five classes namely Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) beats. Six different approaches: (i) Principal Components (PCs) on DCT, (ii) Independent Components (ICs) on DCT, (iii) PCs on DWT, (iv) ICs on DWT, (v) PCs on EMD and (vi) ICs on EMD are employed in this work. Clinically significant features are selected using ANOVA test ([Formula: see text]) and fed to k-Nearest Neighbor (k-NN) classifier. We have obtained a classification accuracy of 99.77% using ICs on DWT method. Consistency of performance is evaluated using Cohen’s kappa statistic. Developed approach is robust, accurate and can be employed for mass diagnosis of cardiac healthcare.

scholarly journals An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dengyong Zhang ◽  
Shanshan Wang ◽  
Feng Li ◽  
Shang Tian ◽  
Jin Wang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Threshold Function ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Ecg Denoising

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( SNR imp ) and mean square error ( MSE ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher SNR imp and a lower MSE . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

scholarly journals Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu
Keyword(s):  
Machine Learning ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Intrinsic Mode Functions ◽  
Detection Range ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Learning Classifier ◽  
Mode Decomposition ◽  
Flux Leakage

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

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