A system for heart sounds classification

Abstract

The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases - one of the major causes of death around the globe - a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability.

Figure 1. A flowchart of the hybrid LPC-SVM-MCS system training process.

The system first collects PCG signals and performs their segmentation to extract useful information for LPC estimation. Then the training process commences where the Modified Cuckoo Search algorithm optimizes parameters of a Support Vector Machine classifier.

Figure 2. Result of the heart tone segmentation algorithm.

The waveform presented contains normal S1, S2 and S3 heart sounds, which are segmented by a variable size time window for further analysis.

Figure 3. A S3 heart tone and LPC filter spectra.

A comparison between a real S3 heart tone spectrum and spectra of filters estimated by the LPC algorithm. The 24th order filter provides the closest representation of the original heart tone spectrum.

Figure 4. Spectrum matching error for different filter orders.

A comparison of matching errors in replicating a S3 heart tone spectrum. The presented curves indicate errors for three filters estimated by a Linear Predictive Coding algorithm with a transfer function of the 5th (red dotted line), 18th (green dotted line) and 24th (blue line) order. The 24th order filter obtained significantly lower error.

Figure 5. Spectrum comparison of selected heart sounds and LPC filters.

Presented curves demonstrate the effectiveness of the modified LPC algorithm in estimating different heart sounds.

Figure 6. Data flow in the proposed classification system.

The picture presents data transfer in the proposed classification system. The modified LPC algorithm estimates filter coefficients – and passes them to the training part. After the training and optimisation process, the selection of appropriate coefficients, selected kernel function and its parameters, and the penalty parameter C can be used in validation of the testing data set.

Figure 7. Example heart sounds used in the tests.

A – early systolic murmur, B – S4, C – pansystolic murmur, D – S3, E – late systolic murmur, F – normal split S2, G – normal split S1, H – ejection click, I – diastolic rumble, J – opening snap.

Figure 8. Comparison of classification results for all test groups.

A comparison of accuracy of all tested methods (being: ANN – Artificial Neural Network, SVM-poly – Support Vector Machine with polynomial kernel function, SVM-rbf – Support Vector Machine with radial basis kernel function, SVM-quad – Support Vector Machine with quadratic kernel function, SVM-MCS-ca – Support Vector Machine with the Modified Cuckoo Search optimizer and classification accuracy fitness function) for a different number of recognizable classes. The SVM-MCS-ce method shows overall the best quality of classification.