**5. Conclusion**

The characteristic waves of an electrocardiogram (ECG) signal and the heart sounds of the phonocardiogram (PCG) signal provide important indicators for the diagnosis of heart disease; they reflect physiological processes of the heart and autonomic nervous system. The analysis and interpretation of these physiological signals *Mathematical Morphology and the Heart Signals DOI: http://dx.doi.org/10.5772/intechopen.104113*

make it possible to highlight new phenomena, which is sometimes possible to explain at the physiological level, and which leads to a better understanding of the overall functioning of the heart.

The automatic analysis of ECG and PCG signals provides the cardiologist with the information needed to diagnose cardiac pathologies. The implementation of reliable algorithms for the processing of ECG and PCG signals, making it possible to detect the useful information carried by the ECG and PCG signals remains a major concern for technicians.

A set of algorithms using morphology transforms has been developed. These algorithms concerning:


Morphological filtering which uses two morphological operators opening and closing for the correction of the baseline.

In noise suppression, the Top Hat transformation was used. It combines the subtraction of the closing and opening morphology operators.

For the detection of ECG signal parameters, typically the QRS complex, the T wave, and the P wave, two techniques have been presented which are called multi-scale morphology and morphological operators.

At the PCG signal level, we used a morphological filter for noise suppression and heart murmur detection.

These techniques, which are based on mathematical morphology, are very effective in estimating rapid changes in the morphology of ECG and PCG signals.
