Classification of Prolapsed Mitral Valve versus Healthy Heart from Phonocardiograms by Multifractal Analysis

Abstract

Phonocardiography has shown a great potential for developing low-cost computer-aided diagnosis systems for cardiovascular monitoring. So far, most of the work reported regarding cardiosignal analysis using multifractals is oriented towards heartbeat dynamics. This paper represents a step towards automatic detection of one of the most common pathological syndromes, so-called mitral valve prolapse (MVP), using phonocardiograms and multifractal analysis. Subtle features characteristic for MVP in phonocardiograms may be difficult to detect. The approach for revealing such features should be locally based rather than globally based. Nevertheless, if their appearances are specific and frequent, they can affect a multifractal spectrum. This has been the case in our experiment with the click syndrome. Totally, 117 pediatric phonocardiographic recordings (PCGs), 8 seconds long each, obtained from 117 patients were used for PMV automatic detection. We propose a two-step algorithm to distinguish PCGs that belong to children with healthy hearts and children with prolapsed mitral valves (PMVs). Obtained results show high accuracy of the method. We achieved 96.91% accuracy on the dataset (97 recordings). Additionally, 90% accuracy is achieved for the evaluation dataset (20 recordings). Content of the datasets is confirmed by the echocardiographic screening.

Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context

“…Namely, the similar shape may arise when observing a structure across the scales, where these structures (fractals) can be described by their fractal dimension [17]. The fractal approaches were found as efficient tools for characterization of complex structures, where different covering techniques and extensions from the envelopes to measuring area can be used [12,13,17,18].…”

“…The principle for calculating box-counting FD (BCFD), as one of the most frequently used methods, is based on covering a waveform with boxes of size by appropriate grid, defined by evenly spaced squares, where the number of boxes, ( ), needed to cover a signal is calculated [12]. The size of squares, 1/ , changes with scale.…”

“…Similar auxiliary envelopes are applied using different joint time-frequency representations [8,10,11]. The identification methods for the primary heart sounds usually consider signal/envelope characteristics (e.g., maximum, variance, frequency, and positive/negative area [6,9,12]). It is noticed that the labels S1 and S2 are traditionally assigned without taking into account varying energy of the sounds [8,10], where 2 Complexity the improved characterization is needed regardless of the assumptions concerning the intervals between the candidates (e.g., recurring sequence of S1-S2 pairs, no excitement, and neither missed candidates nor misinterpreted systoles as intervals of short duration [9,10]).…”

“…In the previous work of our group, initial examination of blanket method is shown as a possible tool for primary heart sounds as well [21]. Moreover, in our previous works [11,12], novel methods are proposed for efficient classification of the auscultatory recordings without the primary heart sound detection using a few multifractal spectrum related features.…”

Cascaded techniques for improving emphysema classification in computed tomography images

“…Several methods of multi-fractal analysis of medical images have been suggested and evaluated in different ways. [3][4][5][6][7][8] Hemsley and Mukundan [3] developed a two-pass algorithm for the computation of multi-fractal spectrum and used the calculated spectra for the classification in a tissue image database. In Ref., [4] the Holder exponent for the power law approximation of intensity measures in pixel neighbourhood is used for resolving local density variations in the CT lung images.…”