Mathematical Modeling of Different Heart Rhythms to Diagnose Chronic Heart Disease | Chapter 13 | Current Progress in Medicine and Medical Research Vol. 6

 In this episode, it is explored that the possibility that ECG records belong to class of multifractal process for which a a lot of scaling exponents are necessary to characterize their scaling forms. There are many recent studies which confirmed that the healthy heartbeat shows regular cardiac beat based on Homeostasis principals since physiologically, our physique system tries to reduce soul rate variability (HRV). We use the BIDMC Congestive Heart Failure database containing long term ECG recordings from 11 brothers, aged 22 to 71, and 4 wives, aged 54 to 63 with harsh congestive heart failure and the MIT-BIH Arrhythmia database that holds 48 half-hour excerpts of two-channel ambulatory ECG records, obtained from 47 cases studied by the BIH Arrhythmia Laboratory middle from two points 1975 and 1979. We compare these two chronic soul diseases with the control folk in the MIT-BIH Normal Sinus Rhythm database that includes 18 long-term ECG records of 5 men, aged 26 to 45, and 13 mothers, aged 20 to 50 without important arrhythmia. The vibration analysis in the way that power ghostly densities (PSD) analysis has been acted for differentiating the time order. Multifractal spectrum analysis has judged the multifractal dynamics of pulse interval signals to distinguish between subjects with severe heart attack and normal signals with arrhythmia. The fractal complicatedness of each heart beat is determined utilizing the Higuchi algorithm, and the signals are therefore contrasted over various opportunity intervals [1]. According to our analysis, when multifractal study and scaling exponent were secondhand as a classifier, the three classes were well separated. In addition, multifractal study revealed that we have a narrow range of exponents for arrhythmia and congestive heart failure issues and as a result, a clear loss of multifractality for bureaucracy. We continue the analysis of pulse interval time succession by estimating the capacity law scaling exponents for active subjects and compare bureaucracy with scaling exponents of victims with heart attack and arrhythmia. Then we apply multifractal analysis to study the multifractal makeup and complex dynamics of these three groups of signals. Our findings support a comprehensive framework for demonstrative and classifying different subjects with cardiac disease to a degree arrhythmia and congestive heart failure and differentiate bureaucracy with normal crowd without heart disease that is crucial in judgment the best diagnostic and ruling strategy in fight against chronic myocardial infarction.

Author(s) Details:

Tahmineh Azizi,
Department of Mechanical Engineering, Florida State University, Tallahassee, FL, USA.

Please see the link here: https://stm.bookpi.org/CPMMR-V6/article/view/11486

Characterization of FECG Signals to Uncover the Complexity of Fetal Heart Rate | Chapter 12 | Current Progress in Medicine and Medical Research Vol. 6

 In this member, we study the fetal essence rate from abdominal signals using multifractal ranges and fractal analysis. There lie many studies that discovered for cardiac ailments the cardiac rhythm displays self affine monofractal properties and allure complexity and the multifractal forms have been regulated by neuroautonomic control means. The Fetal electrocardiogram (FECG) signal may determine precisely itemized information that could help clinicians create more timely and acceptable decisions all along labour. The primary factor forceful interest in FECG signal analysis is allure potential for medical diagnostics and requests. Fetal monitoring is more requiring the ancestry and detection of the FECG signal from composite abdominal dossier using forceful and sophisticated methods. We use the Abdominal and Direct Fetal Electrocar-diogram Database contains multichannel FECG recordings acquired from 5 different daughters in labor, between 38 and 41 weeks of ripening. On these five FECG recordings, we use autocorrelation or power ghostly densities (PSD) analysis to decide whether the signal of interest exhibits a power-regulation PSD and to estimate the exponent from realisations of these processes. We use multi-fractal study to see if unconnected statistical moments at differing scales of these FECG signals exhibit any somewhat power-society scaling. We plot the multi-fractal spectra concerning this database to equate the width of the scaling advocate for each range. A quantitative analysis usually known as the Fractal Dimension (FD) using the Higuchi treasure has been completed activity to illustrate the fractal complicatedness of input signals. Our finding shows that the fractal arithmetic can be secondhand as a mathematical model and computational foundation to further analysis and classification of dispassionate database. This study displays that fractal dimension can be secondhand as a complexity index for FECG records but needs further analysis to find a opening for clinical studies to be secondhand as a biomarker and diagnosis finish in these types studies.

Author(s) Details:

Tahmineh Azizi,
Department of Mechanical Engineering, Florida State University, Tallahassee, FL, USA.

Please see the link here: https://stm.bookpi.org/CPMMR-V6/article/view/11484