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  1. Article ; Online: A wavelet leaders model with multiscale entropy measures for diagnosing arrhythmia and congestive heart failure

    Salim Lahmiri

    Healthcare Analytics, Vol 3, Iss , Pp 100171- (2023)

    2023  

    Abstract: This study proposes a wavelet leaders method with multiscale entropy measures to analyze multiscale complexities in electrocardiogram (ECG) signals to characterize arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The ... ...

    Abstract This study proposes a wavelet leaders method with multiscale entropy measures to analyze multiscale complexities in electrocardiogram (ECG) signals to characterize arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The statistical results show evidence of multiscale fractal and multiscale entropy in all health conditions. In addition, ECG signals under NSR conditions display the largest complexity compared to ARR and CHF. Further, statistical tests confirm the presence of differences in terms of multifractals between health conditions in ECG signals. Finally, multiscale entropy increases with scale. The results from statistical analyses indicate that healthy ECG signals are more complex than abnormal ones. Hence, abnormality alters and reduces complexity in arrhythmia and congestive heart failure signals.
    Keywords Wavelet leaders ; Multiscale entropy ; Electrocardiogram ; Arrhythmia ; Congestive heart failure ; Normal sinus rhythm ; Computer applications to medicine. Medical informatics ; R858-859.7
    Language English
    Publishing date 2023-11-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: A nonlinear analysis of cardiovascular diseases using multi-scale analysis and generalized hurst exponent

    Salim Lahmiri

    Healthcare Analytics, Vol 3, Iss , Pp 100142- (2023)

    2023  

    Abstract: Congestive heart failure (CHF) and arrhythmia (ARR) are common heart diseases that affect a growing population of patients worldwide. In this work, we employ multi-scale analysis (MSA) to estimate generalized Hurst exponent (GHE) from electrocardiogram ( ... ...

    Abstract Congestive heart failure (CHF) and arrhythmia (ARR) are common heart diseases that affect a growing population of patients worldwide. In this work, we employ multi-scale analysis (MSA) to estimate generalized Hurst exponent (GHE) from electrocardiogram (ECG) records under CHF, ARR, and normal sinus rhythm (NSR). As a result, fractal correlations in short and long fluctuations of CHF, ARR, and NSR are measured. Then, a set of six statistical tests are applied to GHE estimates to check how they are different at each time scale between two different ECG conditions. Particularly, the goal is verify if two different ECG conditions can be statistically differentiated by short or by long fluctuations. The battery of statistical tests includes Kolmogorov–Smirnov, Kruskal–Wallis, Wilcoxon rank sum, Student t-test, Ansari–Bradley, and F-test. The results from MSA show evidence that CHF, ARR, and NSR all exhibit multi-fractal properties. Besides, the results from statistical tests revealed that long fluctuations statistically differentiate CHF and ARR, ARR and NSR, and CHF and NSR. Therefore, long fluctuations account most for the characterization of CHF, ARR, and NSR. Our findings are helpful to better understand the mechanics of heart disease and normal heart beats, and also promising to eventually designing computer-aided diagnosis systems for CHF and ARR classification.
    Keywords Congestive heart failure ; Arrhythmia ; Normal sinus rhythm ; Multi-scale analysis ; Generalized Hurst exponent ; Statistical tests ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 612
    Language English
    Publishing date 2023-11-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: Fractals in Neuroimaging.

    Lahmiri, Salim / Boukadoum, Mounir / Di Ieva, Antonio

    Advances in neurobiology

    2024  Volume 36, Page(s) 429–444

    Abstract: Several natural phenomena can be described by studying their statistical scaling patterns, hence leading to simple geometrical interpretation. In this regard, fractal geometry is a powerful tool to describe the irregular or fragmented shape of natural ... ...

    Abstract Several natural phenomena can be described by studying their statistical scaling patterns, hence leading to simple geometrical interpretation. In this regard, fractal geometry is a powerful tool to describe the irregular or fragmented shape of natural features, using spatial or time-domain statistical scaling laws (power-law behavior) to characterize real-world physical systems. This chapter presents some works on the usefulness of fractal features, mainly the fractal dimension and the related Hurst exponent, in the characterization and identification of pathologies and radiological features in neuroimaging, mainly, magnetic resonance imaging.
    MeSH term(s) Humans ; Fractals ; Neuroimaging ; Magnetic Resonance Imaging
    Language English
    Publishing date 2024-03-12
    Publishing country United States
    Document type Journal Article
    ISSN 2190-5215
    ISSN 2190-5215
    DOI 10.1007/978-3-031-47606-8_22
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  4. Article ; Online: Hybrid deep learning and GARCH-family models for forecasting volatility of cryptocurrencies

    Bahareh Amirshahi / Salim Lahmiri

    Machine Learning with Applications, Vol 12, Iss , Pp 100465- (2023)

    2023  

    Abstract: The combination of Deep Learning and GARCH-type models has been proved to be superior to the single models in forecasting of volatility in various markets such as energy, main metals, and especially stock markets. To verify this hypothesis for ... ...

    Abstract The combination of Deep Learning and GARCH-type models has been proved to be superior to the single models in forecasting of volatility in various markets such as energy, main metals, and especially stock markets. To verify this hypothesis for cryptocurrencies market, we constructed various Deep Learning models based on Feed Forward Neural Networks (DFFNNs) and Long Short-Term Memory (LSTM) networks and evaluated their performance in forecasting the volatility of 27 cryptocurrencies. Then, different hybrid models were built in which the outputs of three GARCH-type models, namely GARCH, EGARCH, and APGARCH, with three different assumptions for the residuals’ distribution were fed into the DFFNN and LSTM networks. In other words, GARCH-type models were utilized as feature extractors and the deep learning models leveraged a sequence of extracted features as their inputs to produce the volatility of the next day. Our findings revealed that not only the deep learning models improve the forecasts of GARCH-type models with any distribution assumption, the forecasts of GARCH-type models as informative features can significantly increase the predictive power of the studied deep learning models; namely, the DFFNN and LSTM models.
    Keywords Deep learning ; GARCH-family models ; Cryptocurrency ; Volatility ; Statistical distribution ; Cybernetics ; Q300-390 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 330
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: The effect of COVID-19 pandemic on return-volume and return-volatility relationships in cryptocurrency markets.

    Foroutan, Parisa / Lahmiri, Salim

    Chaos, solitons, and fractals

    2022  Volume 162, Page(s) 112443

    Abstract: Understanding the dynamics of cryptocurrency markets during financial crises such as the recent one caused by the COVID-19 pandemic is crucial for policy makers and investors. In this study, the effect of COVID-19 pandemic on the return-volatility and ... ...

    Abstract Understanding the dynamics of cryptocurrency markets during financial crises such as the recent one caused by the COVID-19 pandemic is crucial for policy makers and investors. In this study, the effect of COVID-19 pandemic on the return-volatility and return-volume relationships for the ten most traded cryptocurrencies, namely Tether, Bitcoin, Ethereum, Ripple, Litecoin, Bitcoin Cash, EOS, Chainlink, Cardano, and Monero is examined. Further, the behavior of cryptocurrencies during COVID-19 pandemic is compared with less volatile markets such as Gold, WTI, and BRENT crude oil markets. To study the effect of volatility on cryptocurrency return, an EGARCH-M model is employed while for the return-volume relationships the VAR model and Granger causality tests are utilized. Results show that the return-volatility relationships for Tether, Ethereum, Ripple, Bitcoin Cash, EOS, and Monero are significant during COVID-19 pandemic, while the same relationship is not significant prior to the pandemic for any of the studied cryptocurrencies. Our findings of the return-volume relationship support the availability of causal relations from return to trading volume changes for Chainlink and Monero in the pre-COVID-19 period and for Ethereum, Ripple, Litecoin, EOS, and Cardano during the COVID-19 period. However, considering the absolute values of returns, we found a significant relationship from cryptocurrencies' absolute returns to trading volume changes for both the prior and during COVID-19 periods. From a managerial perspective, gold can be considered a suitable asset for portfolio hedging during the pandemic period and trading volume can help traders and investors identify the effect of momentum and potential trend in cryptocurrencies on their investments.
    Language English
    Publishing date 2022-07-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2022.112443
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: High-frequency-based features for low and high retina haemorrhage classification.

    Lahmiri, Salim

    Healthcare technology letters

    2017  Volume 4, Issue 1, Page(s) 20–24

    Abstract: Haemorrhages (HAs) presence in fundus images is one of the most important indicators of diabetic retinopathy that causes blindness. In this regard, accurate grading of HAs in fundus images is crucial for appropriate medical treatment. The purpose of this ...

    Abstract Haemorrhages (HAs) presence in fundus images is one of the most important indicators of diabetic retinopathy that causes blindness. In this regard, accurate grading of HAs in fundus images is crucial for appropriate medical treatment. The purpose of this Letter is to assess the relative performance of statistical features obtained with three different multi-resolution analysis (MRA) techniques and fed to support vector machine in grading retinal HAs. Considered MRA techniques are the common discrete wavelet transform (DWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD). The obtained experimental results show that statistical features obtained by EMD, VMD, and DWT, respectively, achieved 88.31% ± 0.0832, 71% ± 0.1782, and 64% ± 0.0949 accuracies. It also outperformed VMD and DWT in terms of sensitivity and specificity. Thus, the EMD-based features are promising for grading retinal HAs.
    Language English
    Publishing date 2017-02-16
    Publishing country England
    Document type Journal Article
    ZDB-ID 2782924-8
    ISSN 2053-3713
    ISSN 2053-3713
    DOI 10.1049/htl.2016.0067
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  7. Article ; Online: Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders.

    Lahmiri, Salim / Tadj, Chakib / Gargour, Christian

    Entropy (Basel, Switzerland)

    2022  Volume 24, Issue 8

    Abstract: Multifractal behavior in the cepstrum representation of healthy and unhealthy infant cry signals is examined by means of wavelet leaders and compared using the ... ...

    Abstract Multifractal behavior in the cepstrum representation of healthy and unhealthy infant cry signals is examined by means of wavelet leaders and compared using the Student
    Language English
    Publishing date 2022-08-22
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e24081166
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  8. Article: Denoising techniques in adaptive multi-resolution domains with applications to biomedical images.

    Lahmiri, Salim

    Healthcare technology letters

    2016  Volume 4, Issue 1, Page(s) 25–29

    Abstract: Variational mode decomposition (VMD) is a new adaptive multi-resolution technique suitable for signal denoising purpose. The main focus of this work has been to study the feasibility of several image denoising techniques in empirical mode decomposition ( ... ...

    Abstract Variational mode decomposition (VMD) is a new adaptive multi-resolution technique suitable for signal denoising purpose. The main focus of this work has been to study the feasibility of several image denoising techniques in empirical mode decomposition (EMD) and VMD domains. A comparative study is made using 11 techniques widely used in the literature, including Wiener filter, first-order local statistics, fourth partial differential equation, nonlinear complex diffusion process, linear complex diffusion process (LCDP), probabilistic non-local means, non-local Euclidean medians, non-local means, non-local patch regression, discrete wavelet transform and wavelet packet transform. On the basis of comparison of 396 denoising based on peak signal-to-noise ratio, it is found that the best performances are obtained in VMD domain when appropriate denoising techniques are applied. Particularly, it is found that LCDP in combination with VMD performs the best and that VMD is faster than EMD.
    Language English
    Publishing date 2016-12-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 2782924-8
    ISSN 2053-3713
    ISSN 2053-3713
    DOI 10.1049/htl.2016.0021
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets.

    Lahmiri, Salim / Bekiros, Stelios

    Chaos, solitons, and fractals

    2020  Volume 138, Page(s) 109936

    Abstract: We explore the evolution of the informational efficiency in 45 cryptocurrency markets and 16 international stock markets before and during COVID-19 pandemic. The measures of Largest Lyapunov Exponent (LLE) based on the Rosenstein's method and Approximate ...

    Abstract We explore the evolution of the informational efficiency in 45 cryptocurrency markets and 16 international stock markets before and during COVID-19 pandemic. The measures of Largest Lyapunov Exponent (LLE) based on the Rosenstein's method and Approximate Entropy (ApEn), which are robust to small samples, are applied to price time series in order to estimate degrees of stability and irregularity in cryptocurrency and international stock markets. The amount of regularity infers on the unpredictability of fluctuations. The
    Keywords covid19
    Language English
    Publishing date 2020-05-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 2003919-0
    ISSN 1873-2887 ; 0960-0779
    ISSN (online) 1873-2887
    ISSN 0960-0779
    DOI 10.1016/j.chaos.2020.109936
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic.

    Lahmiri, Salim / Bekiros, Stelios

    Entropy (Basel, Switzerland)

    2020  Volume 22, Issue 8

    Abstract: The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor's ... ...

    Abstract The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor's 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (
    Language English
    Publishing date 2020-07-30
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e22080833
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