Dall'Olio, Lorenzo
(2020)

*Estimation of biological vascular ageing via photoplethysmography: a comparison between statistical learning and deep learning.*
[Laurea magistrale], Università di Bologna, Corso di Studio in

Physics [LM-DM270]

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## Abstract

This work aims to exploit the biological ageing phenomena which affects human blood vessels. The analysis is performed starting from a database of photoplethysmographic signals acquired through smartphones. The further step involves a preprocessing phase, where the signals are detrended using a central moving average filter, demoduled using the envelope of the analytic signal obtained from the Hilbert transform, denoised using the central moving average filter over the envelope. After the preprocessing we compared two different approaches. The first one regards Statistical Learning, which involves feature extraction and selection through the usage of statistics and machine learning algorithms. This in order to perform a classification supervised task over the chronological age of the individual, which is used as a proxy for healthy/non healthy vascular ageing. The second one regards Deep Learning, which involves the realisation of a convolutional neural network to perform the same task, but avoiding the feature extraction/selection step and so possible bias introduced by such phases. Doing so we obtained comparable outcomes in terms of area under the curve metrics from a 12 layers ResNet convolutional network and a support vector machine using just covariates together with a couple of extracted features, acquiring clues regarding the possible usage of such features as biomarkers for the vascular ageing process. The two mentioned features can be related with increasing arterial stiffness and increasing signal randomness due to ageing.

Abstract

This work aims to exploit the biological ageing phenomena which affects human blood vessels. The analysis is performed starting from a database of photoplethysmographic signals acquired through smartphones. The further step involves a preprocessing phase, where the signals are detrended using a central moving average filter, demoduled using the envelope of the analytic signal obtained from the Hilbert transform, denoised using the central moving average filter over the envelope. After the preprocessing we compared two different approaches. The first one regards Statistical Learning, which involves feature extraction and selection through the usage of statistics and machine learning algorithms. This in order to perform a classification supervised task over the chronological age of the individual, which is used as a proxy for healthy/non healthy vascular ageing. The second one regards Deep Learning, which involves the realisation of a convolutional neural network to perform the same task, but avoiding the feature extraction/selection step and so possible bias introduced by such phases. Doing so we obtained comparable outcomes in terms of area under the curve metrics from a 12 layers ResNet convolutional network and a support vector machine using just covariates together with a couple of extracted features, acquiring clues regarding the possible usage of such features as biomarkers for the vascular ageing process. The two mentioned features can be related with increasing arterial stiffness and increasing signal randomness due to ageing.

Tipologia del documento

Tesi di laurea
(Laurea magistrale)

Autore della tesi

Dall'Olio, Lorenzo

Relatore della tesi

Scuola

Corso di studio

Indirizzo

Applied Physics

Ordinamento Cds

DM270

Parole chiave

vascular ageing,biological ageing,machine learning,statistical learning,deep learning,ridge regression,spectral embedding,convolutional neural network,arterial stiffness,support vector machine,tpr,moving average,hilbert transform

Data di discussione della Tesi

23 Ottobre 2020

URI

## Altri metadati

Tipologia del documento

Tesi di laurea
(NON SPECIFICATO)

Autore della tesi

Dall'Olio, Lorenzo

Relatore della tesi

Scuola

Corso di studio

Indirizzo

Applied Physics

Ordinamento Cds

DM270

Parole chiave

vascular ageing,biological ageing,machine learning,statistical learning,deep learning,ridge regression,spectral embedding,convolutional neural network,arterial stiffness,support vector machine,tpr,moving average,hilbert transform

Data di discussione della Tesi

23 Ottobre 2020

URI

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