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National Repository of Grey Literature 3 records found  Search took 0.00 seconds. 
Detection of atrial fibrillation using ECG Signals
 Běhunčíková, Vendula ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
Atrial fibrillation is one of the most common cardiac rhythm disorders. The prevalence of atrial fibrillation is reported at 1-6 % of the adult population. The chances of developing atrial fibrillation increase with age. An early detection of this arrhythmia is a key to prevent more serious conditions. Many ways have been found to detect atrial fibrillation episodes in ECG including deep learning methods. The aim of this bachelor’s thesis is to describe the problem of atrial fibrillation and the methods used for detection in the ECG record, design an atrial fibrillation detector and test its results. Detector is implemented using a Matlab R2020b software.
Detailed record
Validation of commercially available smartwatches as a human health/activity monitoring tool
 Běhunčíková, Vendula ; Janoušek, Oto (referee) ; Němcová, Andrea (advisor)
This master's thesis deals with the topic of health and activity monitoring of individuals using smartwatches. The aim of the thesis was to collect a set of data according to a measurement protocol using various types of smartwatches, along with reference data from the Faros 180 device. The collected data from a total of nine smartwatches was synchronized, and the synchronized heart rate courses were evaluated using the mean absolute error (MAE) metric. The oxygen saturation and blood pressure parameters were statistically evaluated. In the final part of the thesis, the quality of ECG records obtained from the smartwatches and their diagnostic utility were assessed.
Detailed record
Detection of atrial fibrillation using ECG Signals
 Běhunčíková, Vendula ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
Atrial fibrillation is one of the most common cardiac rhythm disorders. The prevalence of atrial fibrillation is reported at 1-6 % of the adult population. The chances of developing atrial fibrillation increase with age. An early detection of this arrhythmia is a key to prevent more serious conditions. Many ways have been found to detect atrial fibrillation episodes in ECG including deep learning methods. The aim of this bachelor’s thesis is to describe the problem of atrial fibrillation and the methods used for detection in the ECG record, design an atrial fibrillation detector and test its results. Detector is implemented using a Matlab R2020b software.
Detailed record

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