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Digital Biomarkers for Assessing Respiratory Disorders in Parkinson’s Disease
Kováč, Daniel ; Cvetler, Dominik
Respiratory disorders are a significant part of hypokineticdysarthria (HD) that affects patients with Parkinson’sdisease (PD). Still, their potential role in the objective assessmentof HD has not yet been fully explored, which is the primary goalof this study. Several respiratory features were designed andextracted from acoustic signals recorded during text reading.Based on these features, the XGBoost model was able to predictclinical test scores of phonorespiration with an estimated errorrate of 12.54%. Statistical analysis revealed that measuring respirationrate and quantifying signal fluctuations during inspirationhave great potential in the objective assessment of respiratorydisorders in patients with PD.
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Development of features quantifying respiratory dysfunctions in Parkinson’s disease patients
Cvetler, Dominik ; Mekyska, Jiří (referee) ; Kováč, Daniel (advisor)
In the beginning of the thesis, Parkinson's disease and hypokinetic dysarthria are briefly described, which have a negative effect on speech production and cause breathing problems during speech in sick patients. The aim of the thesis is to create an algorithm for automated detection of breaths and the design of parameters for the quantification of respiratory disorders in patients with Parkinson's disease. In the MATLAB environment, the recordings of the researched subjects were processed and an algorithm was created for the detection of breaths, which used the logistic regression method. Based on the predicted breaths, proposed parameters were extracted from the recordings, which were then statistically analyzed and compared in healthy controls and patients with Parkinson's disease. By using a machine learning model, it was possible to predict the clinical data of patients from the proposed parameters to a certain extent. The average accuracy of the model for predicting puffs was 0.85. Of the 14 proposed parameters, 6 were suitable for quantifying respiratory disorders associated with hypokinetic dysarthria. The result of the work is a functional algorithm for the automated detection of breaths in the speech signal and proposed parameters that could be useful for the quantification of respiratory disorders in patients with Parkinson's disease.
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The Spectrum analysis of different single drums from a drum set differently tuned and played by different drumsticks
Cvetler, Dominik ; Mojdl, Edgar (referee) ; Jirásek, Ondřej (advisor)
The bachelor thesis deals with the analysis of the spectrum of membranophones, more precisely drums from the drum kit. Spectrum analysis evaluates aspects that affect the instrument’s spectrum, especially its inharmonicity and the generation of noise or tone modes. The aspects examined are the impact of the drumsticks hardness, the impact hit point on the drum membrane, the tuning of the resonance membrane and the presence of the tailpiece. The thesis examines inharmonicity in the spectrum with harmonic ratios between individual components in bands of harmonic series. The thesis also describes individual drums in terms of organology, drumsticks and membranes used for drums, the theoretical basis of creating membranophones sound and the way in which the measured samples were recorded and processed.
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