Keyword search (4,163 papers available)

"Lahmiri S" Authored Publications:

Title Authors PubMed ID
1 Energy Measures as Biomarkers of SARS-CoV-2 Variants and Receptors Ghannoum Al Chawaf K; Lahmiri S; 41596038
JMSB
2 A Deep Learning-Based Ensemble System for Brent and WTI Crude Oil Price Analysis and Prediction Zhang Y; Lahmiri S; 41294965
JMSB
3 Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm Lahmiri S; Tadj C; Gargour C; 41294952
ENCS
4 An Effective and Fast Model for Characterization of Cardiac Arrhythmia and Congestive Heart Failure Lahmiri S; Bekiros S; 40218199
JMSB
5 Fractals in Neuroimaging Lahmiri S; Boukadoum M; Di Ieva A; 38468046
JMSB
6 The effect of COVID-19 pandemic on return-volume and return-volatility relationships in cryptocurrency markets Foroutan P; Lahmiri S; 36068915
CONCORDIA
7 Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders Lahmiri S; Tadj C; Gargour C; 36010830
ENCS
8 Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic Lahmiri S; Bekiros S; 33286604
JMSB
9 Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic Lahmiri S; Bekiros S; 32834621
JMSB
10 The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets Lahmiri S; Bekiros S; 32501379
JMSB

 

Title:Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders
Authors:Lahmiri STadj CGargour C
Link:https://pubmed.ncbi.nlm.nih.gov/36010830/
DOI:10.3390/e24081166
Publication:Entropy (Basel, Switzerland)
Keywords:cepstrumcumulantsexpirationinfant cry signalinspirationmultifractal spectrummultifractal wavelet leaders
PMID:36010830 Category: Date Added:2022-08-26
Dept Affiliation: ENCS
1 Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC H3G 1M8, Canada.
2 Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada.

Description:

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 t-test. The empirical results show that both expiration and inspiration signals exhibit clear evidence of multifractal properties under healthy and unhealthy conditions. In addition, expiration and inspiration signals exhibit more complexity under healthy conditions than under unhealthy conditions. Furthermore, distributions of multifractal characteristics are different across healthy and unhealthy conditions. Hence, this study improves the understanding of infant crying by providing a complete description of its intrinsic dynamics to better evaluate its health status.





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