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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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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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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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Article ; Online: Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets

Salim Lahmiri / Stelios Bekiros

Entropy, Vol 22, Iss 833, p

The Role of the COVID-19 Pandemic

2020 Volume 833

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 ( i ) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while ( ii ) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, ( iii ) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and ( iv ) the S&P500 volatility represents a unique cluster, while ( v ) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, ( vi ) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and ( vii ) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that ( i ) investors may diversify their portfolios across single constituents of clusters, ( ii ) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that ( iii ) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.
Keywords	COVID-19 pandemic ; Bitcoin ; stock market ; precious metal market ; energy market ; GARCH ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999 ; covid19
Subject code	330
Language	English
Publishing date	2020-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks

Salim Lahmiri

Journal of King Saud University: Computer and Information Sciences, Vol 26, Iss 2, Pp 218-

2014 Volume 227

Abstract: This paper presents a forecasting model that integrates the discrete wavelet transform (DWT) and backpropagation neural networks (BPNN) for predicting financial time series. The presented model first uses the DWT to decompose the financial time series ... ...

Abstract	This paper presents a forecasting model that integrates the discrete wavelet transform (DWT) and backpropagation neural networks (BPNN) for predicting financial time series. The presented model first uses the DWT to decompose the financial time series data. Then, the obtained approximation (low-frequency) and detail (high-frequency) components after decomposition of the original time series are used as input variables to forecast future stock prices. Indeed, while high-frequency components can capture discontinuities, ruptures and singularities in the original data, low-frequency components characterize the coarse structure of the data, to identify the long-term trends in the original data. As a result, high-frequency components act as a complementary part of low-frequency components. The model was applied to seven datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model that uses only low-frequency components. In addition, the presented model outperforms both the well-known auto-regressive moving-average (ARMA) model and the random walk (RW) process.
Keywords	Stock prices ; Discrete wavelet transform ; Approximation and detail coefficients ; Backpropagation neural networks ; Forecasting ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	330
Language	English
Publishing date	2014-07-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Estimating the risk-return tradeoff in MENA Stock Markets

Salim Lahmiri

Decision Science Letters, Vol 2, Iss 2, Pp 119-

2013 Volume 124

Abstract: This study employs the generalized autoregressive conditionally heteroskedastic in the mean (GARCH-M) methodology to investigate the return generating process of Jordan, Kingdom of Saudi Arabia (KSA), Kuwait, and Morocco stock market indices. The ... ...

Abstract	This study employs the generalized autoregressive conditionally heteroskedastic in the mean (GARCH-M) methodology to investigate the return generating process of Jordan, Kingdom of Saudi Arabia (KSA), Kuwait, and Morocco stock market indices. The tradeoff between returns and the conditional variance is found to be positive in all markets. In other words, the empirical findings show that investors are rewarded for their exposure to more risk in these financial markets. This result is consistent with both financial theory and empirical finance.
Keywords	MENA Stock Markets ; GARCH-M ; Econometrics ; Mathematics ; QA1-939 ; Science ; Q ; DOAJ:Mathematics ; DOAJ:Mathematics and Statistics
Language	English
Publishing date	2013-04-01T00:00:00Z
Publisher	Growing Science
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Features Extraction from High Frequency Domain for Retina Digital Images Classification

Salim Lahmiri

Journal of Advances in Information Technology, Vol 4, Iss 4, Pp 194-

2013 Volume 198

Abstract: The purpose of this paper is to extract features from retina digital images based on a further analysis of high frequency components (HH) obtained with the discrete wavelet transform (DWT). In particular, the DWT is applied to the retina photograph to ... ...

Abstract	The purpose of this paper is to extract features from retina digital images based on a further analysis of high frequency components (HH) obtained with the discrete wavelet transform (DWT). In particular, the DWT is applied to the retina photograph to obtain its high-high (HH) image subband. Then, a further decomposition by DWT is applied to the HH image subband of the previous step to obtain HH. Finally, statistical features are computed from HH. The support vector machines (SVM) are employed to classify normal versus abnormal images using leave-one-out cross-validation method (LOOM). The simulation results show strong evidence of the effectiveness of features extracted from HH* than features extracted from HH. Thus, they are in accordance with our previous work where our approach was applied to mammograms. In summary, our methodology based on a further analysis of high frequency images using DWT helps extracting suitable features for automatic classification of normal and abnormal retina digital images.
Keywords	retina digital image ; discrete wavelet transform ; high frequency subband ; features extraction ; support vector machines ; classification ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
Subject code	006
Language	English
Publishing date	2013-11-01T00:00:00Z
Publisher	ACADEMY PUBLISHER
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Linear and nonlinear dynamic systems in financial time series prediction

Salim Lahmiri

Management Science Letters, Vol 2, Iss 7, Pp 2551-

2012 Volume 2556

Abstract: Autoregressive moving average (ARMA) process and dynamic neural networks namely the nonlinear autoregressive moving average with exogenous inputs (NARX) are compared by evaluating their ability to predict financial time series; for instance the S&P500 ... ...

Abstract	Autoregressive moving average (ARMA) process and dynamic neural networks namely the nonlinear autoregressive moving average with exogenous inputs (NARX) are compared by evaluating their ability to predict financial time series; for instance the S&P500 returns. Two classes of ARMA are considered. The first one is the standard ARMA model which is a linear static system. The second one uses Kalman filter (KF) to estimate and predict ARMA coefficients. This model is a linear dynamic system. The forecasting ability of each system is evaluated by means of mean absolute error (MAE) and mean absolute deviation (MAD) statistics. Simulation results indicate that the ARMA-KF system performs better than the standard ARMA alone. Thus, introducing dynamics into the ARMA process improves the forecasting accuracy. In addition, the ARMA-KF outperformed the NARX. This result may suggest that the linear component found in the S&P500 return series is more dominant than the nonlinear part. In sum, we conclude that introducing dynamics into the ARMA process provides an effective system for S&P500 time series prediction.
Keywords	Linear Systems ; Nonlinear Systems ; ARMA ; Kalman Filter ; Dynamic Neural Networks ; Time Series ; Management. Industrial management ; HD28-70 ; Industries. Land use. Labor ; HD28-9999 ; Social Sciences ; H ; DOAJ:Business and Management ; DOAJ:Business and Economics
Subject code	330
Language	English
Publishing date	2012-10-01T00:00:00Z
Publisher	Growing Science
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An entropy-LVQ system for S&P500 downward shifts forecasting

Salim Lahmiri

Management Science Letters, Vol 2, Iss 1, Pp 21-

2012 Volume 28

Abstract: The purpose of this paper is to predict the S&P500 down moves with technical analysis indicators using learning vector quantization (LVQ) neural networks and probabilistic neural networks (PNN). In addition, entropy-based input selection technique is ... ...

Abstract	The purpose of this paper is to predict the S&P500 down moves with technical analysis indicators using learning vector quantization (LVQ) neural networks and probabilistic neural networks (PNN). In addition, entropy-based input selection technique is employed to improve the prediction accuracies. The out-of-sample simulations show that LVQ outperforms PNN. In addition, the Entropy-LVQ system achieved higher accuracy in comparison with the literature.
Keywords	Stock market ; Neural networks ; Loss limit ; Forecasting ; Management. Industrial management ; HD28-70 ; Industries. Land use. Labor ; HD28-9999 ; Social Sciences ; H ; DOAJ:Business and Management ; DOAJ:Business and Economics
Language	English
Publishing date	2012-01-01T00:00:00Z
Publisher	Growing Science
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A clustering approach to examine the dynamics of the NASDAQ topology in times of crisis

Salim Lahmiri

Management Science Letters, Vol 2, Iss 6, Pp 2113-

2012 Volume 2118

Abstract: This paper investigates the dynamics of the NASDAQ topology before, during, and after 2008 financial crisis. First, multiresolution analysis by virtue of wavelet transform is employed to denoise each NASDAQ sector return series. Second, the correlation ... ...

Abstract	This paper investigates the dynamics of the NASDAQ topology before, during, and after 2008 financial crisis. First, multiresolution analysis by virtue of wavelet transform is employed to denoise each NASDAQ sector return series. Second, the correlation matrix of sectors is built and analyzed in each time period to view comovements of sectors. Third, hierarchical clustering trees are constructed in each time period to find out how the structure of the NASDAQ market evolves through time. Our results suggest that interrelationships between sectors become stronger in times of crisis and especially in post-crisis period. In addition, some markets tend to form the same cluster in all time periods; for instance the Industrial and Bank sectors and the Telecommunication and Computer sectors. However, the general topology of the NASDAQ market has been considerably changed over periods. In sum, the complex structure of the NASDAQ market is dynamic and is more integrated after 2008 financial crisis. This result indicates that there are less diversification opportunities in the post-crisis period in comparison with pre-crisis period. These empirical findings are important for the development of subsequent portfolio strategies.
Keywords	Financial crisis ; Market dynamics ; Wavelet analysis ; Time series clustering ; Management. Industrial management ; HD28-70 ; Industries. Land use. Labor ; HD28-9999 ; Social Sciences ; H ; DOAJ:Business and Management ; DOAJ:Business and Economics
Subject code	332
Language	English
Publishing date	2012-10-01T00:00:00Z
Publisher	Growing Science
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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