Clustered photoplethysmogram pulse wave shapes and their associations with clinical data - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Frontiers in Physiology Année : 2023

Clustered photoplethysmogram pulse wave shapes and their associations with clinical data

Résumé

Photopletysmography (PPG) is a non-invasive and well known technology that enables the recording of the digital volume pulse (DVP). Although PPG is largely employed in research, several aspects remain unknown. One of these is represented by the lack of information about how many waveform classes best express the variability in shape. In the literature, it is common to classify DVPs into four classes based on the dicrotic notch position. However, when working with real data, labelling waveforms with one of these four classes is no longer straightforward and may be challenging. The correct identification of the DVP shape could enhance the precision and the reliability of the extracted bio markers. In this work we proposed unsupervised machine learning and deep learning approaches to overcome the data labelling limitations. Concretely we performed a K-medoids based clustering that takes as input 1) DVP handcrafted features, 2) similarity matrix computed with the Derivative Dynamic Time Warping and 3) DVP features extracted from a CNN AutoEncoder. All the cited methods have been tested first by imposing four medoids representative of the Dawber classes, and after by automatically searching four clusters. We then searched the optimal number of clusters for each method using silhouette score, the prediction strength and inertia. To validate the proposed approaches we analyse the dissimilarities in the clinical data related to obtained clusters.
Fichier principal
Vignette du fichier
Zanelli (2023) - Clustered photoplethysmogram pulse wave shapes and their associations with clinical data.pdf (39.42 Mo) Télécharger le fichier
Zanelli (2023) - Clustered photoplethysmogram pulse wave shapes and their associations with clinical data (1).pdf (39.42 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04304385 , version 1 (29-11-2023)

Identifiants

Citer

Serena Zanelli, Kornelia Eveilleau, Peter H Charlton, Mehdi Ammi, Magid Hallab, et al.. Clustered photoplethysmogram pulse wave shapes and their associations with clinical data. Frontiers in Physiology, 2023, 14, ⟨10.3389/fphys.2023.1176753⟩. ⟨hal-04304385⟩
7 Consultations
3 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More