Aortic Stenosis Heart Research Articles

Aortic Stenosis Detection Using Spectral Statistical Features of Heart Sound Signals

INTRODUCTION: Aortic stenosis (AS) is a severe complicated heart valve disease. This valve abnormality is a slow-progressive condition and mostly asymptomatic. Hence, there is a need for a rapid non-invasive AS diagnosis method with minimal feature extraction. 
 OBJECTIVE: In this paper, we proposed a rapid spectral analysis-based statistical feature extraction method to identify the AS stages with the minimum number of features.
 METHODS: In this study, the heart sound signals were collected from the medical database and transformed into the frequency domain for further spectral feature analysis. We used the windowing technique to condition the heart signals before spectral analysis. The spectral statistical features were extracted from the computed frequency spectrum. The range of statistical features was compared for normal, early, and delayed AS groups.
 RESULTS: In experiments, the normal, early, and delayed AS heart sound signals were used. The experimental results show the statistical difference between the normal and AS heart sound signal spectrums. The normal/unhealthy condition of a heart was identified using the statistical features of the frequency spectrum.
 CONCLUSION: The experimental results confirmed that the statistical features derived from the heart sound signal spectrums were varied according to the AS condition. Hence, the spectral statistical features can be considered as a rapid predictor of AS.

Aortic Stenosis Detection Using Spectral Statistical Features of Heart Sound Signals

INTRODUCTION: Aortic stenosis (AS) is a severe complicated heart valve disease. This valve abnormality is a slow-progressive condition and mostly asymptomatic. Hence, there is a need for a rapid non-invasive diagnosis method with minimal feature extraction.OBJECTIVE: In this paper, we proposed a spectral features-based rapid heart sound signal analysis method to identify the AS stages with minimum number of features.METHODS: In this study, the heart sound signals were collected from the medical database and transformed into the frequency domain for further spectral feature analysis. We used the windowing technique to conditioning the heart signals before spectral analysis. The spectral statistical features were extracted from the computed frequency spectrum. The range of statistical features was compared for normal, early, and AS sound signals.RESULTS: In experiments, the normal, early, and delayed AS heart sound signals were used. The normal/unhealthy condition of a heart was identified using the statistical features of the frequency spectrum. The experimental results show the statistical difference between the normal and AS heart sound signal spectrums.CONCLUSION: The experimental results confirmed that the statistical features derived from the heart sound signal spectrums were varied according to the AS condition. Hence, the spectral statistical features can be considered as rapid predictors of AS.

Early Detection and Implications of Degenerative Aortic Stenosis Heart Valvular Disease

Early Detection and Implications of Degenerative Aortic Stenosis Heart Valvular Disease

Early Detection and Implications of Degenerative Aortic Stenosis Heart Valvular Disease

Early Detection and Implications of Degenerative Aortic Stenosis Heart Valvular Disease

SMOOTHED-PSEUDO WIGNER–VILLE DISTRIBUTION OF NORMAL AND AORTIC STENOSIS HEART SOUNDS

In this paper, we are interested in the acquisition and the time-frequency analysis of the Phonocardiogram (PCG) signal. The interactive software "PCG Recorder" we implemented in MATLAB, drives the sound card of a personal computer for acquisition purposes. Normal and abnormal heart sounds were acquired with 16 bits resolution and at high sampling frequencies; the value 2 kHz was selected as sampling rate to avoid spectral aliasing. For each patient, additional information like the age, the gender, the weight as well as the auscultation area can be introduced within the saved data file. The aortic, the tricuspid, the mitral and the pulmonic areas are considered for the acquisition task. The Smoothed-Pseudo Wigner–Ville Distribution (SPWVD) yield adequate Time-Frequency Representations (TFRs) of such non-stationary signal as heart sounds. Moreover, by taking into account the corresponding auscultation area for each obtained TFR, we adopt exclusion reasoning to attribute each burst to its origins within the myocardium. Furthermore, the alternating functioning of heart valves and cavities in systole and diastole was characterized in the time and frequency domains. Aortic stenosis heart sounds were involved in our study in a view to confirm their pathological nature towards the normal heart sounds findings. Indeed, the weakened S1 and S2 heart sounds and the strong systolic ejection murmur which dominates the overall systole, confirm our hypotheses. Thus, modulations laws relating to the systolic ejection of blood through the stenosed orifice were characterized by means of the reliable SPWVD approach. A third heart sound (S3) which is an indicator of the presence of systolic dysfunction and the elevated filling pressure for aortic stenosis lesion was also characterized.

Evaluation by contrast-enhanced electron beam computed tomography of myocardial perfusion and tissue characteristics in congenital aortic stenosis.

The evaluation of myocardial perfusion and tissue characteristics gives useful information regarding the indication for surgery. In the present study, two-phase contrast enhanced electron beam computed tomography (EBT) was used to quantitatively examine myocardial perfusion and the tissue characteristics of the left ventricular (LV) myocardium in congenital aortic stenosis (AS). The AS group comprised 15 patients and the control group had 14 non-AS patients. Myocardial ischemia and tissue characteristics were evaluated by the ratio of the incremental CT number of the myocardium (M) and the lumen (L) of the LV (M/L) in, the early phase with and without dipyridamole (Dp) loading and in the late phase after enhancement (respectively). In a comparison between the AS group and the controls, mean early M/Ls were significantly lower in the AS group than in the controls (20+/-5% vs 37+/-10%, p<0.01) and mean late M/Ls in the AS group were also significantly lower (47+/-11% vs 62+/-13%, p<0.01). In the AS patients, early M/Ls in the subendocardial and apical regions were especially lower than those of other segments, but the late M/Ls in these segments were inversely higher than those of other segments. These results suggest 2 things: (1) the existence of latent LV myocardial ischemia in AS patients, which is probably because of a hypertrophied LV wall, in turn caused by afterload, and (2) the presence of more myocardial ischemia and interstitial fibrosis in the subendocardial and apical regions of the AS heart. Using Dp loading for the AS patients increased the clarity of the contrast images. Contrast-enhanced EBT is useful for evaluating myocardial characteristics and perfusion in AS, and gives new insight into the LV wall characteristics in AS, as well as an indication for surgical correction of congenital AS.