Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

Awan, Imtiaz Ahmed; Aziz, Wajid; Shah, Imran Hussain; Habib, Nazneen; Alowibdi, Jalal S.; Saeed, Sharjil; Nadeem, Malik Sajjad Ahmed; Shah, Syed Ahsin Ali

doi:10.1371/journal.pone.0196823

Cited by 24 publications

(21 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The complex biological systems exhibit structures at multiple time scales and they function in a dynamic environment. The traditional approaches measure complexity at a single time scale and fail to account for multiple temporal scales inherent in the output signals of these systems [10,14]. In 2002, Costa et al proposed Multiscale Entropy (MSE), to represent heart rate dynamics at multiple time scales [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extraction of Dynamical Information and Classification of Heart Rate Variability Signals Using Scale Based Permutation Entropy Measures

Shah¹,

Habib²,

Nadeem³

et al. 2020

Self Cite

View full text Add to dashboard Cite

The dynamical fluctuations in the Heart Rate Variability (HRV) signals show structures at multiple time scales revealing that complexity of the autonomic nervous system control of the heart is multiscale and hierarchical. Multiscale Entropy (MSE) and its variant Composite MSE (CMSE) were proposed to quantify the complexity at multiple time scales, however, these measures failed to quantify complexity accurately for short duration signals at large temporal scales. To address the downsides of MSE and CMSE, Multiscale Permutation Entropy (MPE) and Improved MPE (IMPE) were proposed. The preliminary results reveal that MPE and IMPE were able to distinguish healthy and pathological subjects, however, further studies are needed to investigate the robustness of these measures. In this study, we investigate the robustness of scale based PE measures in terms of dynamical information, induction of undefined entropy estimates for short duration signals and to classify HRV signals under different physiological and pathological conditions. The results were compared with SE, PE, MSE and CMSE. The MPE and IMPE along with MSE and CMSE provided accurate dynamical information. The results revealed that MPE and IMPE resolved the issue of inducing undefined entropy estimates and are robust in classifying healthy and different pathological subjects.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Awan et al [14] proposed Multiscale Normalized Corrected Shannon Entropy (MNCSE) to study the dynamics of interbeat interval time series of healthy and diseased subjects. They compared the results of MNCSE with traditional MSE and found that MNCSE is more reliable and stable than MSE.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Dynamical Information and Classification of Heart Rate Variability Signals Using Scale Based Permutation Entropy Measures

Shah¹,

Habib²,

Nadeem³

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Their reported accuracy was 75.9% for the analysis of the RR interval time series and 85.6% for the differential RR interval time series. Awan et al [50] calculated MSE with scales from 1 to 20 and then utilized SVM, KNN, and random forest classifiers for discrimination, achieving testing accuracies of less than 85%. To the best of our knowledge, no study has reported a testing accuracy greater than 86% using MSE alone.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure

et al. 2019

View full text Add to dashboard Cite

Multiscale entropy (MSE) is widely used to analyze heartbeat signals. Even though cardiologists do not use MSE to diagnose heart failure at present, these studies are of importance and have potential clinical applications. In previous studies, MSE discrimination between old congestive heart failure (CHF) and healthy individuals has remained controversial. Few studies have been published on the discrimination between them, using only MSE with machine learning for automatic multidimensional analysis, with reported testing accuracies of less than 86%. In this study, we determined the optimal MSE scales for discrimination by using a low-dimensional exhaustive search along with three classifiers—linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). In younger people (<55 years), the results showed an accuracy of up to 95.5% with two optimal MSE scales (2D) and up to 97.7% with four optimal MSE scales (4D) in discriminating between young CHF and healthy participants. In older people (≥55 years), the discrimination accuracy reached 90.1% using LDA in 2D, SVM in 3D (three optimal MSE scales), and KNN in 5D (five optimal MSE scales). LDA with a 3D exhaustive search also achieved 94.4% accuracy in older people. Therefore, the results indicate that MSE analysis can differentiate between CHF and healthy individuals of any age.

show abstract

“…e previous studies [26][27][28] used last embedding dimension data points of PSR time series as the target set. Recently, the concept of multiple time scales has been introduced to study dynamics of healthy and pathological physiological systems such as regularity mechanism of cardiovascular system [29,30], postural control [31], and gait dynamics [32]. e APM mass concentrations are an outcome of complex natural and anthropogenic contributors evolving with time, which may operate on multiple time scales.…”

Section: Introductionmentioning

confidence: 99%

A Novel Phase Space Reconstruction- (PSR-) Based Predictive Algorithm to Forecast Atmospheric Particulate Matter Concentration

Shah

Aziz

Nadeem

et al. 2019

Scientific Programming

Self Cite

View full text Add to dashboard Cite

e prediction of atmospheric particulate matter (APM) concentration is essential to reduce adverse effects on human health and to enforce emission restrictions. e dynamics of APM are inherently nonlinear and chaotic. Phase space reconstruction (PSR) is one of the widely used methods for chaotic time series analysis. e APM mass concentrations are an outcome of complex anthropogenic contributors evolving with time, which may operate on multiple time scales. us, the traditional single-variable PSR-based prediction algorithm in which data points of last embedding dimension are used as a target set may fail to account for multiple time scales inherent in APM concentrations. To address this issue, we propose a novel PSR-based scientific solution that accounts for the information contained at multiple time scales. Different machine learning algorithms are used to evaluate the performance of the proposed and traditional PSR techniques for predicting mass concentrations of particulate matter up to 2.5 micron (PM 2.5 ), up to 10 micron (PM 10.0 ), and ratio of PM 2.5 /PM 10.0 . Hourly time series data of PM 2.5 and PM 10.0 mass concentrations are collected from January 2014 to September 2015 at the Masfalah air quality monitoring station (couple of kilometers from the Holy Mosque in Makkah, Saudi Arabia). e performances of various learning algorithms are evaluated using RMSE and MAE. e results demonstrated that prediction error of all the machine learning techniques is smaller for the proposed PSR approach compared to traditional approach. For PM 2.5 , FFNN leads to best results (both RMSE and MAE 0.04 μgm −3 ), followed by SVR-L (RMSE 0.01 μgm −3 and MAE 0.09 μgm −3 ) and RF (RMSE 1.27 μgm −3 and MAE 0.86 μgm −3 ). For PM 10.0 , SVR-L leads to best results (both RMSE and MAE 0.06 μgm −3 ), followed by FFNN (RMSE 0.13 μgm −3 and MAE 0.09 μgm −3 ) and RF (RMSE 1.60 μgm −3 and MAE 1.16 μgm −3 ). For PM 2.5 /PM 10.0 , FFNN is the best and accurate method for prediction (0.001 for both RMSE and MAE), followed by RF (0.02 for both RMSE and MAE) and SVR-L (RMSE 0.05 μgm −3 and MAE 0.04).

show abstract

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

Cited by 24 publications

References 35 publications

Extraction of Dynamical Information and Classification of Heart Rate Variability Signals Using Scale Based Permutation Entropy Measures

Extraction of Dynamical Information and Classification of Heart Rate Variability Signals Using Scale Based Permutation Entropy Measures

Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure

A Novel Phase Space Reconstruction- (PSR-) Based Predictive Algorithm to Forecast Atmospheric Particulate Matter Concentration

Contact Info

Product

Resources

About