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1 Ergebnisse
1
Automatic classification of cardiovascular age of healthy p..:
kurian pullolickal, priya
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-187234. , 2022
Link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-18723
RT Journal T1
Automatic classification of cardiovascular age of healthy people by dynamical patterns of the heart rhythm
UL https://suche.suub.uni-bremen.de/peid=base-ftlinkoepinguniv:oai:DiVA.org:liu-187234&Exemplar=1&LAN=DE A1 kurian pullolickal, priya PB Linköpings universitet, Statistik och maskininlärning YR 2022 K1 Electrocardiogram (ECG) measures the electrical impulses of the heart. The time inter- val between two successive R peaks measured in millisecond using an ECG is called as an RR-interval. The distribution of the RR-intervals as well as classification of cardiovascu- lar age of healthy people from RR-interval was done in this thesis. For that K1 the data was preprocessed and time series plots were analyzed from sample dataset. The RR-intervals were then aligned to have the same start time using functions written and then an aver- age RR-interval series for each decade was created. The coefficient of variation was very less for this averaged dataset which concluded that averaging the RR-interval was a good approach. The averaged dataset per age decade as well agreed to the conclusion of the sample data set that the heart rate variability decreases with increasing age. Three clusters of age decade were also visible in the averaged dataset. The kurtosis K1 skew K1 mean K1 me- dian K1 histograms and Q-Q Plot were calculated for the sample as well as averaged dataset to find the distribution. The values all concluded that the RR-intervals follow Gaussian distribution or mixture of Gaussian distribution. The Poincaré plots showed that the dis- tribution of RR-interval is comet shaped for healthy individuals. The features were ex- tracted from the distribution as well as from the distribution of Discrete Fourier Transform (DFT) for classifying the age group from RR-intervals. Svitzky-Golay filtering was done to smooth the signal before taking the features from DFT. Random Forest and Support Vector Machine was the machine learning algorithms used to classify the age decade. Later the results were compared using a dataset from physionet that had RR-intervals of individuals suffering from myocardial infraction. The age classification using Random Forest and Sup- port Vector Machine concluded that the Gdańsk dataset using Random Forest Algorithm and ... K1 Engineering and Technology K1 Teknik och teknologier JF http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-187234 LK http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-187234 DO http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-187234 SF ELIB - SuUB Bremen
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