# Application of Wavelet Transform and Fractal Analysis for
## 4 Conclusions  
The research led to the development of a new method to successfully access the esophageal pH-metry data. Summing up the results of the research, it can be stated that the results of the study confirmed that the tested signals have features that enable their fast and automatic classification and assignment to a group of sick or healthy people with the use of the various methods of wavelet transforms. The results with the WT methods provided the high value of the success rate (89.5%--17 successes out of 19 attempts) as well as the values of sensitivity (93.33%) and specificity (75%). Therefore, this approach can be implemented in medical programs for research as well as for clinical use. The main advantage of the presented method is that it allows not only for a simple classification of the patients into sick or healthy groups but also for an indication of the stage of the pathology. This has been achieved by the clear clustering of the curves for the patients diagnosed with GERD above the curve taken as the cut-off (scale value greater than zero) and the clustering of healthy patients below the cut-off (scale value less than zero). In addition, the results of the applied method do not require interpretation by a specialist, which significantly accelerates and simplifies the procedure of the preliminary diagnosis of the examined patient. The method also does not require sophisticated and high-performance computing equipment, which allows its potential implementation and application as early as the stage of real-time measurement data recording. Further research on the development of the signal analysis methods related to the measurement of esophageal acid requires the collection of high-quality, verified measurement data from a large number of patients with different states of health. This would enable the authors not only to validate the procedures presented in the article but also to use advanced methods based on machine learning, which make it possible to determine the correlations between the parameters of seemingly unrelated signals. The authors realise that modern medical diagnostic tests are increasingly based on multi-parametric analysis, using not only the main measurement quantity (e.g., impedance or pH) but also additional biosignal parameters such as temperature, SCL, HR, oxygen saturation level, or even EEG. Recording, analysing, and studying the correlations between several different bio-parameters would make it possible not only to determine the patient's current state of health but also, potentially, to assess how the patient's physiological system reacts to the applied drug treatment or its effectiveness. The proposed methods can be used as a preliminary assessment procedure when analysing esophageal pH courses, which would be subject to verification by a medical specialist in the course of further analysis. Using the developed methods, it is possible to obtain the result/diagnosis of gastroesophageal reflux disease in a few seconds. Using the traditional method, the total DeMeester count, the test result can be obtained even after a few hours (the time needed by a gastroenterology specialist to calculate the total DeMeester count, perform an analysis of the pH course with appropriate exclusions, interpret the test, and finally make a diagnosis). The amount of work performed by the doctor is significant, lasting up to several hours per examination. It also requires the physician to be experienced in reading and interpreting pH waveforms. This all adds up to a significant increase in testing time and cost. For the computer analysis of the pH waveforms, only the pH waveform itself is needed. The algorithm developed by the authors of the work allows the result of the analysis (and possibly a diagnosis proposal) to be provided in a few to several seconds (depending on the computing power of the computer). A particular advantage of the presented method is that its implementation on a computer is quick, and the calculation process takes a small amount of time.