The master thesis deals with the issue of long-term ECG recording, used in medicine for monitoring of cardiac anomalies. Author focuses on explaning the general concepts in the first part. Author describes measuring methods and issues associated with measuring. Comparison of existing devices and design of solution with own features are in next chapter. Next is comparison and selecting of formats for recording measured data and description of device connectivity. In next chapter author focuses on hardware design and implementation of device. After that author describes software solution design for microcontroller, Bluetooth module and smartphone. Author introduces possible improvements for future generation of the device and then describes results of testing at the end of this work.

This master’s thesis deals with the automatic detection of myocardial infarction in ECG. Semester work consists of two parts. The theoretical part provides a description of the electrical conduction system of the heart, spreading of electrical activity through the heart muscle, the methods of ECG scanning and the ECG curve. There are also mentioned the causes of myocardial ischemia and various methods of its detection. Another part is devoted to high-frequency ECG, analysis of HFQRS and clinical studies which describe the use of high-frequency ECG in diagnosis of myocardial infarction. In the practical part is proposed an algorithm using low-frequency components ECG and an algorithm using high-frequency components ECG for automatic detection of myocardial infarction. The proposed algorithms are implemented in programming environment MATLAB and tested on signals from the PTB database. The final part of the master‘s thesis is devoted to the comparison of the success of myocardial infarction by means of low frequency and high frequency components of ECG and comparison of achieved results with results from clinical studies.

Signal compression is daily-used tool for memory capacities reduction and for fast data communication. Methods based on wavelet transform seem to be very effective nowadays. Signal decomposition with a suitable bank filters following with coefficients quantization represents one of the available technique. After packing quantized coefficients into one sequence, run length coding together with Huffman coding are implemented. This thesis focuses on compression effectiveness for the different wavelet transform and quantization settings.