Published online 2004 Jun 29. doi: 10.1186/1475-925X-3-21

PMID: 15225347

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Abstract

Background

New technologies like echocardiography, color Doppler, CT, and MRI provide more direct and accurate evidence of heart disease than heart auscultation. However, these modalities are costly, large in size and operationally complex and therefore are not suitable for use in rural areas, in homecare and generally in primary healthcare set-ups. Furthermore the majority of internal medicine and cardiology training programs underestimate the value of cardiac auscultation and junior clinicians are not adequately trained in this field. Therefore efficient decision support systems would be very useful for supporting clinicians to make better heart sound diagnosis. In this study a rule-based method, based on decision trees, has been developed for differential diagnosis between 'clear' Aortic Stenosis (AS) and 'clear' Mitral Regurgitation (MR) using heart sounds.

Dsm differential diagnosis decision tree

Methods

For the purposes of our experiment we used a collection of 84 heart sound signals including 41 heart sound signals with 'clear' AS systolic murmur and 43 with 'clear' MR systolic murmur. Signals were initially preprocessed to detect 1st and 2nd heart sounds. Next a total of 100 features were determined for every heart sound signal and relevance to the differentiation between AS and MR was estimated. The performance of fully expanded decision tree classifiers and Pruned decision tree classifiers were studied based on various training and test datasets. Similarly, pruned decision tree classifiers were used to examine their differentiation capabilities. In order to build a generalized decision support system for heart sound diagnosis, we have divided the problem into sub problems, dealing with either one morphological characteristic of the heart-sound waveform or with difficult to distinguish cases.

Results

Relevance analysis on the different heart sound features demonstrated that the most relevant features are the frequency features and the morphological features that describe S1, S2 and the systolic murmur. The results are compatible with the physical understanding of the problem since AS and MR systolic murmurs have different frequency contents and different waveform shapes. On the contrary, in the diastolic phase there is no murmur in both diseases which results in the fact that the diastolic phase signals cannot contribute to the differentiation between AS and MR.

We used a fully expanded decision tree classifier with a training set of 34 records and a test set of 50 records which resulted in a classification accuracy (total corrects/total tested) of 90% (45 correct/50 total records). Furthermore, the method proved to correctly classify both AS and MR cases since the partial AS and MR accuracies were 91.6% and 88.5% respectively. Similar accuracy was achieved using decision trees with a fraction of the 100 features (the most relevant). Pruned Differentiation decision trees did not significantly change the classification accuracy of the decision trees both in terms of partial classification and overall classification as well.

Discussion

Present work has indicated that decision tree algorithms decision tree algorithms can be successfully used as a basis for a decision support system to assist young and inexperienced clinicians to make better heart sound diagnosis. Furthermore, Relevance Analysis can be used to determine a small critical subset, from the initial set of features, which contains most of the information required for the differentiation. Decision tree structures, if properly trained can increase their classification accuracy in new test data sets. The classification accuracy and the generalization capabilities of the Fully Expanded decision tree structures and the Pruned decision tree structures have not significant difference for this examined sub-problem. However, the generalization capabilities of the decision tree based methods were found to be satisfactory. Decision tree structures were tested on various training and test data set and the classification accuracy was found to be consistently high.

Background

New technologies like Echocardiography, Color Doppler, CT, and MRI provide more direct and accurate evidence of heart disease than heart auscultation. However, these modalities are costly, large in size and operationally complex []. Therefore these technologies are not suitable for use in rural areas, in homecare and generally in primary healthcare set-ups. Although heart sounds can provide low cost screening for pathologic conditions, internal medicine and cardiology training programs underestimate the value of cardiac auscultation and junior clinicians are not adequately trained in this field. The pool of skilled clinicians trained in the era before echocardiography continues to age, and the skills for cardiac auscultation is in danger to disappear [2]. Therefore efficient decision support systems would be very useful for supporting clinicians to make better heart sound diagnosis, especially in rural areas, in homecare and in primary healthcare. Recent advances in Information Technology systems, in digital electronic stethoscopes, in acoustic signal processing and in pattern recognition methods have inspired the design of systems based on electronic stethoscopes and computers [3,4]. In the last decade, many research activities were conducted concerning automated and semi-automated heart sound diagnosis, regarding it as a challenging and promising subject. Many researchers have conducted research on the segmentation of the heart sound into heart cycles [5-7], the discrimination of the first from the second heart sound [8], the analysis of the first, the second heart sound and the heart murmurs [9-], and also on features extraction and classification of heart sounds and murmurs [14,15]. These activities mainly focused on the morphological characteristics of the heart sound waveforms. On the contrary very few activities focused on the exploitation of heart sound patterns for the direct diagnosis of cardiac diseases. The research of Dr. Akay and co-workers in Coronary Artery Disease [16] is regarded as very important in this area. Another important research activity is by Hedben and Torry in the area of identification of Aortic Stenosis and Mitral Regurgitation [17]. The reason for this focus on the morphological characteristics is the following: Cardiac auscultation and diagnosis is quite complicated depending not only on the heart sound, but also on the phase of the respiration cycle, the patient's position, the physical examination variables (such as sex, age, body weight, smoking condition, diastolic and systolic pressure), the patient's history, medication etc. The heart sound information alone is not adequate in most cases for heart disease diagnosis, so researchers generally focused on the identification and extraction of the morphological characteristics of the heart sound.

The algorithms which have been utilized for this purpose were based on: i) Auto Regressive and Auto Regressive Moving Average Spectral Methods [5,11], ii) Power Spectral Density [5], iii) Trimmed Mean Spectrograms [18], iv) Sort Time Fourier Transform [11], v) Wavelet Transform [7,9,11], vi) Wigner-Ville distribution, and generally the ambiguous function [10]. The classification algorithms were mainly based on: i) Discriminant analysis [19], ii) Nearest Neighbour [], iii) Bayesian networks [,21], iv) Neural Networks [,8,18] (backpropagation, radial basis function, multiplayer perceptron, self organizing map, probabilistic neural networks etc) and v) rule-based methods [15].

In this paper a rule-based method, based on decision trees, has been developed for differential diagnosis between the Aortic Stenosis (AS) and the Mitral Regurgitation (MR) using heart sounds. This is a very significant problem in cardiology since there is very often confusion between these two diseases. The correct discrimination between them is of critical importance for

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