Spectral composition of heart sounds before and after mechanical heart valve implantation using a modified forward-backward Prony's method

Innovative technologies for the assessment of cardiovascular medical devices: state-of-the-art techniques for artificial heart valve testing

Prosthetic heart valves (PHVs) are engineered devices used for replacing diseased natural cardiac valves. This article presents several investigational techniques for the evaluation of the performance of these clinical devices, whose implantation is not completely free of drawbacks. The state-of-the-art in the technological approach for PHV testing is addressed. As the fluid dynamics of PHVs are particularly complex, the main focus will be on experimental velocimetric techniques and computational analysis. A methodology for the analysis of the valve's signature, in terms of its characteristic sound in the opening and closing phases, is also presented. The aforementioned techniques are necessary to guarantee an operational life of the implanted device as free as possible from clinical complications. It can be realistically expected that this characterization will help designers in improving PHV performance.

Comparison of first and second heart sounds after mechanical heart valve replacement

In this article, the spectral features of first heart sounds (S1) and second heart sounds (S2), which comprise the mechanical heart valve sounds obtained after aortic valve replacement (AVR) and mitral valve replacement (MVR), are compared to find out the effect of mechanical heart valve replacement and recording area on S1 and S2. For this aim, the Welch method and the autoregressive (AR) method are applied on the S1 and S2 taken from 66 recordings of 8 patients with AVR and 98 recordings from 11 patients with MVR, thereby yielding power spectrum of the heart sounds. Three features relating to frequency of heart sounds and three features relating to energy of heart sounds are obtained. Results show that in comparison to natural heart valves, mechanical heart valves contain higher frequency components and energy, and energy and frequency components do not show common behaviour for either AVR or MVR depending on the recording areas. Aside from the frequency content and energy of the sound generated by mechanical heart valves being affected by the structure of the lungs-thorax and the recording areas, the pressure across the valve incurred during AVR or MVR is a significant factor in determining the frequency and energy levels of the valve sound produced. Though studies on native heart sounds as a non-invasive diagnostic method has been done for many years, it is observed that studies on mechanical heart valves sounds are limited. The results of this paper will contribute to other studies on using a non-invasive method for assessing the mechanical heart valve sounds.

Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders

Heart murmurs are pathological sounds produced by turbulent blood flow due to certain cardiac defects such as valves disorders. Detection of murmurs via auscultation is a task that depends on the proficiency of physician. There are many cases in which the accuracy of detection is questionable. The purpose of this study is development of a new mathematical model of systolic murmurs to extract their crucial features for identifying the heart diseases. A high resolution algorithm, multivariate matching pursuit, was used to model the murmurs by decomposing them into a series of parametric time-frequency atoms. Then, a novel model-based feature extraction method which uses the model parameters was performed to identify the cardiac sound signals. The proposed framework was applied to a database of 70 heart sound signals containing 35 normal and 35 abnormal samples. We achieved 92.5% accuracy in distinguishing subjects with valvular diseases using a MLP classifier, as compared to the matching pursuit-based features with an accuracy of 77.5%.

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.

Unsupervised and uncued segmentation of the fundamental heart sounds in phonocardiograms using a time-scale representation

A methodology is proposed to segment and label the fundamental activities, namely the first and second heart sounds, S1 and S2of the phonocardiogram (PCG). Information supplementary to the PCG, such as a cue from a synchronously acquired electrocardiogram (ECG), subject-specific prior information, or training examples regarding the activities, is not required by the proposed methodology. A bank of Morlet wavelet correlators is used to obtain a time-scale representation of the PCG. An energy profile of the time-scale representation and a singular value decomposition (SVD) technique are used to identify segments of the PCG that contain the fundamental activities. The robustness of the methodology is demonstrated by the correct segmentation of over 90% of 1068 fundamental activities in a challenging set of PCGs which were recorded from patients with normally functioning and abnormally functioning bioprosthetic valves. The PCGs included highly varying fundamental activities that overlapped in time and frequency with other aberrant non-fundamental activities such as murmurs and noise-like artifacts.

Beat to beat analysis of mechanical heart valves by means of return map

Three mechanical heart valves (two bileaflet prostheses and a tilting one) were investigated in a basic hardware setup in order to evaluate with a hydrophone their opening and closing action in time and in amplitude of each beat. The recorded signal was then segmented into the series of cycles xi(t) having a temporal duration equal to the working period imposed on the valve. Two return maps were defined, in order to evaluate the degree of dispersion of the resulting scatter plot: (i) the amplitude map xi(t) versus xi+1(t); (ii) the delay map for the closure of the valve within each beat versus the successive ones. To evaluate the results obtained, two indices were proposed based on both the degree of dispersion and the deviation of the regression line of the resulting scatter plot with respect to the bisector of the map plane. The tilting disc valve showed a lower degree of dispersion, both in the amplitude signal and in the closure time delays, with respect to the other two bileaflet heart valves. The methodology proposed here could be regarded as an alternative non-invasive tool to investigate the dynamic behaviour of prosthetic heart valves, especially in the case of their suspected failure.

Detection of heart murmurs using wavelet analysis and artificial neural networks

This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.

Nonlinear transient chirp signal modeling of the aortic and pulmonary components of the second heart sound

This paper describes a new approach based on the time-frequency representation of transient nonlinear chirp signals for modeling the aortic (A2) and the pulmonary (P2) components of the second heart sound (S2). It is demonstrated that each component is a narrow-band signal with decreasing instantaneous frequency defined by its instantaneous amplitude and its instantaneous phase. Each component is also a polynomial phase signal, the instantaneous phase of which can be accurately represented by a polynomial having an order of thirty. A dechirping approach is used to obtain the instantaneous amplitude of each component while reducing the effect of the background noise. The analysis-synthesis procedure is applied to 32 isolated A2 and 32 isolated P2 components recorded in four pigs with pulmonary hypertension. The mean +/- standard deviation of the normalized root-mean-squared error (NRMSE) and the correlation coefficient (rho) between the original and the synthesized signal components were: NRMSE = 2.1 +/- 0.3% and rho = 0.97 +/- 0.02 for A2 and NRMSE = 2.52 +/- 0.5% and rho = 0.96 +/- 0.02 for P2. These results confirm that each component can be modeled as mono-component nonlinear chirp signals of short duration with energy distributions concentrated along its decreasing instantaneous frequency.

Application of the matching pursuit method for structural decomposition and averaging of phonocardiographic signals

The paper evaluates the performance of an automatic adaptive time-frequency method to detect each cardiac cycle of a phonocardiogram (PCG) and extract average heart sounds and PCG cycles. The proposed method combines a global search of the PCG, in terms of the energy distribution of the most important components, with a local search relating to the specific events found within a cardiac cycle. The method is applied to 100 PCG recordings from 50 patients with an aortic bioprosthetic valve. The performance of the proposed method is compared with a commonly used semi-automatic method that is based on the combined analysis of an electrocardiogram (ECG) and the PCG signal. Results show that the proposed method clearly outperforms the semi-automatic method, especially in the case of patients with malfunctioning bioprostheses. By eliminating the need to record an ECG as the time-reference signal, this method reduces hardware overheads when analysis of PCG signals is the primary aim. It is also independent of subjective human judgment for selection of reference templates and threshold levels. Furthermore, the method is robust to artefacts, background noise and other kinds of signal interferences. With minor modifications, the procedure described could be applied to other types of biomedical signal in order to extract coherent transient components and identify specific events.

Spectral characterization and classification of Carpentier-Edwards heart valves implanted in the aortic position

This paper demonstrates an improvement in the performance of spectral phonocardiography, combined with pattern recognition techniques for monitoring the condition of bioprosthetic heart valves. The analysis of the heart sounds is performed using a modified forward-backward overdetermined Prony's method. Results show that the condition of the bioprosthesis affects mostly the higher part of the spectrum (i.e., above 250 Hz) where no frequency components were found for malfunctioning cases. Therefore, the amplitudes of the three highest frequency components are used as the input vector of an adaptive single layer perceptron-based classifier to identify normal and malfunctioning classes. For the sample set examined, this method gives 100% correct discrimination between normal and malfunctioning Carpentier-Edwards (C-E) valves.