Complexity measures of high oscillations in phonocardiogram as biomarkers to distinguish between normal heart sound and pathological murmur

2021 ◽  
pp. 111610
Author(s):  
Salim Lahmiri ◽  
Stelios Bekiros
Keyword(s):  
Heart Sound ◽  
Normal Heart ◽  
Complexity Measures

scholarly journals Wavelet Packet Entropy for Heart Murmurs Classification

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Fatemeh Safara ◽  
Shyamala Doraisamy ◽  
Azreen Azman ◽  
Azrul Jantan ◽  
Sri Ranga
Keyword(s):  
Mitral Regurgitation ◽  
Aortic Stenosis ◽  
Mitral Stenosis ◽  
Heart Sound ◽  
Wavelet Packet ◽  
Heart Sounds ◽  
Normal Heart ◽  
Audio Features ◽  
Heart Murmurs

Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.

HEART SOUND MODEL BASED ON CASCADED AND LOSSLESS ACOUSTIC TUBES

2019 ◽  
Vol 19 (05) ◽  
pp. 1950031
Author(s):  
KAI WANG ◽  
XIEFENG CHENG ◽  
YAMIN CHEN ◽  
CHENJUN SHE ◽  
KEXUE SUN ◽  
...  
Keyword(s):  
Transfer Function ◽  
Transmission Line ◽  
Approximation Method ◽  
Heart Sound ◽  
Generation Mechanism ◽  
Traditional Model ◽  
Normal Heart ◽  
Tube Radius ◽  
Formant Frequency ◽  
Model Based

In order to further understand the generation mechanism of Heart Sound, we introduce a new method for simulating Heart Sound by using cascaded and lossless acoustic tubes. Based on the theory of acoustics, we abstract the ventricles and arteries inside of the heart as multistage tubes with equal length and different radii. By controlling the radii of tubes, we simulate the process of relaxation and constriction of the ventricles and arteries. Then, we calculate the transfer function of the tubes based on the theory of reflective transmission line. To gain the tubes’ radii, we use formant frequency as the model target parameters and put forward an approximation method. Finally, the experimental results show that compared with traditional model, the model based on cascaded and lossless acoustic tubes could better reflect the state of the ventricles and arteries. Meanwhile, by comparing the model tube radius of normal Heart Sound and pathological Heart Sound, we can give a better explanation to the cause of pathological Heart Sound.

A Method for Analyzing Clinical Heart Sound Signal Based on Chaotic Characteristics

2014 ◽  
Vol 599-601 ◽  
pp. 1321-1327
Author(s):  
Min Fen Shen ◽  
Zhi Fei Su ◽  
Li Li Sun ◽  
Li Sha Sun
Keyword(s):  
Phase Space ◽  
Lyapunov Exponents ◽  
Heart Sound ◽  
Reference Method ◽  
Mean Value ◽  
Sound Signal ◽  
Normal Heart ◽  
The Mean ◽  
Largest Lyapunov Exponents ◽  
Heart Sound Signal

To seek a way non-invasive and adaptive to differentiate the normal and abnormal heart sound signals in order to provide more valuable reference method for clinical diagnosis. This paper made the largest Lyapunov exponent as the mainline. According to the unity of the whole signal in different stages, a method to study the characteristic in stage was proposed. First of all, we made phase space reconstitution to the typical seven normal and abnormal heart sound signals. Then, we calculated the largest Lyapunov exponents according to the phase space reconstitution parameters. At last, we compared and analyzed the mean values of the largest Lyapunov exponents. The mean value of the normal heart sound signal in S1 was 0.145, which was much larger than that of the abnormal signals and the mean value of the normal heart sound signal in S2 was larger than that of the abnormal ones, too. This conclusion means that there are chaotic characteristic in the heart sound signals and the degree of chaos in normal heart sounds is higher than that in the abnormal heart sound signals.

Chaotic Characteristics of Heart Sound Signals Based on the Largest Lyapunov Exponent

2013 ◽  
Vol 411-414 ◽  
pp. 1117-1124 ◽  
Author(s):  
Li Sha Sun ◽  
Li Li Sun
Keyword(s):  
Phase Space ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Heart Sound ◽  
Mean Value ◽  
Sound Signal ◽  
Largest Lyapunov Exponent ◽  
Normal Heart ◽  
The Mean ◽  
Largest Lyapunov Exponents

Seeking a way non-invasive and adaptive to differentiate the normal and abnormal heart sound signals to provide more valuable reference method to the clinical diagnosis. This paper made the largest Lyapunov exponent as the mainline. According to the unity of the whole signal in different stages, a method to study the characteristic in stage was proposed. First of all, we made phase space reconstitution to the typical seven normal and abnormal heart sound signals. Then, we calculated the largest Lyapunov exponents according to the phase space reconstitution parameters. At last, we compared and analyzed the mean values of the largest Lyapunov exponents. The mean value of the normal heart sound signal in S1 was 0.145, which was much larger than that of the abnormal signals and the mean value of the normal heart sound signal in S2 was larger than that of the abnormal ones, too. This conclusion means that there are chaotic characteristic in the heart sound signals and the degree of chaos in normal heart sounds is higher than that in the abnormal heart sound signals.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Bilateral Reduction Mammoplasty after Heart Transplantation

2014 ◽  
Vol 17 (4) ◽  
pp. 224
Author(s):  
Yue-Dong Shi ◽  
Fa-Zhi Qi ◽  
Zi-Hao Feng
Keyword(s):  
Heart Transplantation ◽  
Reduction Mammoplasty ◽  
Vertical Scar ◽  
Normal Heart ◽  
Breast Enlargement ◽  
Breast Morphology ◽  
Systolic And Diastolic Function ◽  
Bilateral Breast Enlargement ◽  
Scar Reduction

We report a bilateral reduction mammoplasty in a 15 year old female who suffered increasing back and shoulder pain and chest wall discomfort associated with bilateral breast enlargement during a 17 month period following heart transplantation. Cardiologic evaluation confirmed a structurally normal heart with good systolic and diastolic function, and ejection fraction of 80%. We performed a bilateral mammoplasty using dermal suspension flap in vertical-scar reduction. The patient recovered satisfactorily without incident, and breast morphology was excellent at the 2 year 9 month follow-up, with no recurrence of her previous symptoms or further hyperplasia.

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