Markov-Based Neural Networks for Heart Sound Segmentation: Using Domain Knowledge in a Principled Way

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Martins, ML (Autor) Outra; Coimbra, M (Autor) FCUP; Renna, F (Autor) FCUP

Revista

Título: IEEE Journal of Biomedical and Health Informatics

Vol. 27

Páginas: 5357-5368

ISSN: 2168-2194

Editora: IEEE

Indexação

ISI Web of Knowledge - 3 Citações

Outras Informações

ID Authenticus: P-00Z-0RD

DOI: 10.1109/jbhi.2023.3312597

Abstract (EN): This work considers the problem of segmenting heart sounds into their fundamental components. We unify statistical and data-driven solutions by introducing Markov-based Neural Networks (MNNs), a hybrid end-toend framework that exploits Markov models as statistical inductive biases for an Artificial Neural Network (ANN) discriminator. We show that an MNN leveraging a simple onedimensional Convolutional ANN significantly outperforms two recent purely data-driven solutions for this task in two publicly available datasets: PhysioNet 2016 (Sensitivity: 0.947 +/- 0.02; Positive Predictive Value : 0.937 +/- 0.025) and the CirCor DigiScope 2022 (Sensitivity: 0.950 +/- 0.008; Positive Predictive Value: 0.943 +/- 0.012). We also propose a novel gradient-based unsupervised learning algorithm that effectively makes the MNN adaptive to unseen datum sampled from unknown distributions. We perform a cross dataset analysis and show that an MNN pre-trained in the CirCor DigiScope 2022 can benefit from an average improvement of 3.90% Positive Predictive Value on unseen observations from the PhysioNet 2016 dataset using this method.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12