Detection of the third heart sound using a tailored wavelet approach

Abstract

The third heart sound is normally heard during auscultation of younger individuals but disappears with increasing age. However, this sound can appear in patients with heart failure and is thus of potential diagnostic use in these patients. Auscultation of the heart involves a high degree of subjectivity. Furthermore, the third heart sound has low amplitude and a low-frequency content compared with the first and second heart sounds, which makes it difficult for the human ear to detect this sound. It is our belief that it would be of great help to the physician to receive computer-based support through an intelligent stethoscope, to determine whether a third heart sound is present or not. A precise, accurate and low-cost instrument of this kind would potentially provide objective means for the detection of early heart failure, and could even be used in primary health care. In the first step, phonocardiograms from ten children, all known to have a third heart sound, were analysed, to provide knowledge about the sound features without interference from pathological sounds. Using this knowledge, a tailored wavelet analysis procedure was developed to identify the third heart sound automatically, a technique that was shown to be superior to Fourier transform techniques. In the second step, the method was applied to phonocardiograms from heart patients known to have heart failure. The features of the third heart sound in children and of that in patients were shown to be similar. This resulted in a method for the automatic detection of third heart sounds. The method was able to detect third heart sounds effectively (90%), with a low false detection rate (3.7%), which supports its clinical use. The detection rate was almost equal in both the children and patient groups. The method is therefore capable of detecting, not only distinct and clearly visible/audible third heart sounds found in children, but also third heart sounds in phonocardiograms from patients suffering from heart failure.

Image-Based Simulations Show Important Flow Fluctuations in a Normal Left Ventricle: What Could be the Implications?

“…While we do not directly prove that the reported fluctuations are responsible for the S 3 bruit, extrapolation from our results would support a cause-and-effect relationship 46 . Indeed, the present time-frequency analysis showed consistent results regarding a) the S 3 main frequency content previously reported 22 (10-100 Hz) as well as b) the time window when the most intense fluctuations occurred 11,22 (120-200 ms). These observations support the fact that the hemodynamic fluctuations reported in this paper are a plausible physical explanation for the S 3 bruit.…”

“…It is worth noting that any CFD study where flow instabilities are not properly represented cannot address the prevalence of ventricular bruits and murmurs 31,46 . Among the sounds usually reported, the S 3 bruit is a low-frequency brief sound harbouring a main frequency content in the range of 10-100 Hz 22 and occurring at the end of the E wave, 120-200 ms after the start of the diastole 11,22 . Although the genesis of this sound is controversial, it is widely reported that "vibrations" occurring during the deceleration of the E wave generate this sound 20,27 .…”

Automated heartsound analysis/computer-aided auscultation: A cardiologist's perspective and suggestions for future development

“…S3 and S4 heartsounds are an early abnormality in heart failure patients, yet difficult to hear and frequently missed by healthcare providers due to their frequency characteristics [4]. However, S3 and S4 sounds may be detectable by CAA, leading to potentially better outcomes via earlier medical intervention [16]. Furthermore, serial evaluation via CAA, even at home, could guide individualized, real-time therapy decisions.…”

Automatic phonocardiogram segmentation using the Sliding Window Autocorrelation algorithm

“…Much of the previous PCG literature has focused on automatic sound analysis and murmur analysis assuming accurate PCG segmentation, such as in [1], [2], [6], [14], [15], [16], [17], [18], [22], [24], [25], [27], [29], [31], [36], [37], [46], [47], [48], [54], [62], [65], [66], [69], [70]. Many of such previous works rely on accurate PCG segmentation.…”