Detection of COPD’s auscultative symptoms using higher order statistics in the analysis of respiratory sounds

In this paper we present the method for determination of the specific auscultatory diagnostic signs in patients with chronic obstructive pulmonary disease (COPD), which is based upon the utilization of the polyspectral analysis and the calculation of higher order statistics. The main stages of the method are the calculation and construction of the bicoherence function of the lung sound signal in order to find its maximal value. The visual and numerical estimations of the obtained maximum allow us to conclude the presence or absence in this lung’s audio signal of the artifact, which indicates the pathology. For more accurate results one needs to determine asymmetry coefficient and to perform the estimation of bifrequency corresponding to the maximal value of the bicoherence coefficient. The calculation of skewness and kurtosis coefficients of cross-correlation functions of lung sound signals, which were recorded simultaneously in four channels, allows us to reduce the sensitivity of the method to noise components. Therefore, by analyzing all proposed calculated characteristics and parameters one can conclude the presence or absence of the pathology in this audio signal

Analysis of electrocardiosignals for formation of the diagnostic features of post-traumatic myocardial dystrophy

The possibilities of high-resolution electrocardiography (HR ECG) application for diagnostics of post-traumatic myocardial dystrophy having multifactorial genesis is considered in this paper. Numerical processing and analysis of electrocardiograms that belong to patients from armed forces after explosive-driven injuries have been performed based on clinical studies. Complex method of cardiosignal analysis based on combination of wavelet analysis, eigenvector decomposition and principal component analysis is developed. This method revealed that low-amplitude deviations in ECG signal in case of post-traumatic myocardial dystrophy have low-frequency nature that is linked to slow electro-physiological processes. It is shown that these low-frequency, low-amplitude components appear at a high levels (8th and 9th) of decomposition in case of 9-level wavelet decomposition of averaged cardio cycles. Integral parameters for identification of post-traumatic myocardial dystrophy features are suggested and determined on the basis of principal component analysis. These parameters are squared sum of signal projections to eigenspaces Hk and mean eigenvalues of covariance matrices of electrocardiosignals ensembles λmean

Detection of COPD’s auscultative symptoms using higher order statistics in the analysis of respiratory sounds

Полный текст доступен на сайте издания по подписке: http://radio.kpi.ua/article/view/S0021347016020059In this paper we present the method for determination of the specific auscultatory diagnostic signs in patients with chronic obstructive pulmonary disease (COPD), which is based upon the utilization of the polyspectral analysis and the calculation of higher order statistics. The main stages of the method are the calculation and construction of the bicoherence function of the lung sound signal in order to find its maximal value. The visual and numerical estimations of the obtained maximum allow us to conclude the presence or absence in this lung’s audio signal of the artifact, which indicates the pathology. For more accurate results one needs to determine asymmetry coefficient and to perform the estimation of bifrequency corresponding to the maximal value of the bicoherence coefficient. The calculation of skewness and kurtosis coefficients of cross-correlation functions of lung sound signals, which were recorded simultaneously in four channels, allows us to reduce the sensitivity of the method to noise components. Therefore, by analyzing all proposed calculated characteristics and parameters one can conclude the presence or absence of the pathology in this audio signal.Предложен метод определения характерных аускультативных диагностических признаков у больных ХОБЛ, основанный на использовании полиспектрального анализа и расчета статистик высшего порядка. Этапами метода являются расчет и построение функции бикогерентности сигнала звука дыхания для нахождения ее максимального значения. Визуальная и численная оценка полученного максимума позволяет сделать заключение о наличии или отсутствии в данном звуковом сигнале легкого артефакта, свидетельствующего о патологии. Для более точных результатов необходимо получить коэффициент асимметрии и выполнить оценку бичастоты, соответствующей максимальному значению коэффициента бикогерентности. Расчет коэффициентов асимметрии и эксцесса взаимокорреляционных функций сигналов звуков легких, снятых синхронно в четырех каналах, позволяет уменьшить чувствительность метода к шумовым составляющим. Таким образом, анализируя все предложенные рассчитанные характеристики и параметры, возможно принять решение о наличии или отсутствии в данном звуковом сигнале патологии