Semiautomatic detection of cardiac diseases employing dual tree complex wavelet transform

Prashar, Navdeep; Sood, Meenakshi; Jain, Shruti

doi:10.21533/pen.v6i2.188

Cited by 11 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For thresholding, the detail coefficients, the "Wave Shrink" known as the universal threshold is applied as, where σ standard deviation, and M represents the size of the ECG signal at any detail decomposition or level as reported in (Georgieva-Tsaneva & Tcheshmedjiev, 2013). The DTCWT denoising technique is also proved in (Prashar, Sood, & Jain, 2018), and it shows the improvement in Signal to Noise ratio (SNR) from -4.9666dB to 27.7500 db.…”

Section: Proposed Ecg Classification Approachmentioning

confidence: 99%

Application-Specific Discriminant Analysis of Cardiac Anomalies Using Shift-Invariant Wavelet Transform

Singh

Rajpal

Mehta

2021

International Journal of E-Health and Medical Communications

View full text Add to dashboard Cite

Automatic arrhythmia detection in electrocardiogram (ECG) using supervised learning has gained significant considerations in recent years. This paper projects the performance analysis of classifiers such as support vector machine (SVM), extreme learning machine (ELM), and k-nearest neighbor (KNN) with efficient time utilization showing multi-classification for specific medical application. The wavelet double decomposition is used to show the shift-invariant use of dual-tree complex wavelet transform for noise filtering and beat segmentation is done to extract 130 informative samples. Further, the linear discriminant analysis is applied to dimensionally reduce and elite the 12 most relevant features for classifying normal and four abnormal beats collected from MIT/BIH ECG database. The proposed executed system distinguishes SVM, ELM, and KNN with percentage accuracy of 99.8, 97, and 99.8 having classifier testing time as 0.0081s, 0.0031s, and 0.0234s, respectively. The simulated experimental outcomes in comparison with existing work yields adequate accuracy, and computational time.

show abstract

Section: Proposed Ecg Classification Approachmentioning

confidence: 99%

Application-Specific Discriminant Analysis of Cardiac Anomalies Using Shift-Invariant Wavelet Transform

Singh

Rajpal

Mehta

2021

International Journal of E-Health and Medical Communications

View full text Add to dashboard Cite

show abstract

“…To counter this restriction, the ECG signal has been denoised employing the DTCWT technique that has been empowered by good directional selectivity with the better shift-invariance and reduced spectral aliasing properties that made this technique more suitable to denoise the non-stationary signals than other conventional techniques. This multilevel decomposition employing the DTCWT strategy is broadly depicted in (Prashar et al, 2018b). The block diagram of the DTCWT technique is shown in Figure 2.…”

Section: Filtering Stagementioning

confidence: 99%

“…ECG signal is non-stationary and non-linear that resulted in the induction of various artefacts at different frequencies classified as burst noise, baseline wander noise, power-line interference and electromyography noise (Prashar et al, 2019). These artefacts influenced the morphological characteristics (P-QRS-T) of an ECG signal that resulted in the false extraction of its features (Prashar et al, 2018b). Contamination of ECG signals with various noises causes major challenges for cardiac disease identification.…”

Section: Introductionmentioning

confidence: 99%

Dual-tree complex wavelet transform technique-based optimal threshold tuning system to deliver denoised ECG signal

Prashar

Sood

Jain

2020

Transactions of the Institute of Measurement and Control

Self Cite

View full text Add to dashboard Cite

The primary output for any health monitoring system that offers telecardiology services is the recovery of the electrocardiographic (ECG) signal from the noised signal. The mechanized investigation of the ECG signal is the most inspiring challenge for accurate detection of cardiac disease. This could be accomplished by eliminating the various noises from the acquired signal. In this paper, a noise reduction approach employing DTCWT is executed on an ECG signal by proposing a noise estimator along with a detailed assessment of the effect of the choice of the threshold value, threshold algorithm and distribution function. The thresholding technique is executed by varying the threshold value ( γ) and its function ( fn) applied to the proposed estimator (α*). The proposed estimator is scaled by 2n factor to study its impact on performance metrics and the nature of the reconstructed signal utilizing different distribution functions. The best combination of threshold function with threshold value selection has been chosen in this work from eight different sets of threshold value selection rules along with six distinct threshold functions. The experimental results show that the proposed noise reduction approach using a universal modified threshold level-dependent threshold with non-negative garrote threshold function for normal distribution with n = 3 delivers 80.72dB SNR with a subsequent reduction in MSE and PRD as compared with other standard techniques. An elaborate empirical analysis for selecting the distribution function for obtaining the best possible threshold function and technique is the prime objective and novelty of this research work.

show abstract

“…Recently, modern methods digital signal processing and nonlinear dynamics have been actively introduced for the study of financial time series, in particular, wavelet analysis, fractal and multifractal analysis, entropy analysis, recurrent diagrams and Lyapunov's exponent, etc. [21]- [25].…”

Section: Introductionmentioning

confidence: 99%

Modelling multifractal properties of cryptocurrency market

Derbentsev¹,

Kibalnyk²,

Radzihovska³

2019

PEN

View full text Add to dashboard Cite

The paper focuses on the study of the effect of long memory and the analysis of the multifractal properties of the time series of the most capitalized cryptocurrencies for the period from 2010 to 2018. To do this, the Hurst exponent is calculated by both R/S analysis and the Detrended Fluctuation Analysis being more stable in the case of non-stationary time series. Our results show that time series of cryptocurrencies to be persistent during almost the whole study period that do not allow accepting the hypothesis concerning the efficiency of the cryptocurrency market. We also found that (i) time series became anti-persistent during the periods of market crisis phenomena and turbulence; (ii) the Hurst exponents showed significant fluctuations about the value of 0.5. In addition, we conduct a multifractal analysis of cryptocurrency time series that allows us to assess the state and stability of the market.The calculated spectrum of multifractality shows that the cryptocurrency market comes out of a crisis state, since the width of the multifractality spectrum has the maximum value for all cryptocurrencies.

show abstract

Semiautomatic detection of cardiac diseases employing dual tree complex wavelet transform

Cited by 11 publications

References 15 publications

Application-Specific Discriminant Analysis of Cardiac Anomalies Using Shift-Invariant Wavelet Transform

Application-Specific Discriminant Analysis of Cardiac Anomalies Using Shift-Invariant Wavelet Transform

Dual-tree complex wavelet transform technique-based optimal threshold tuning system to deliver denoised ECG signal

Modelling multifractal properties of cryptocurrency market

Contact Info

Product

Resources

About