Automatic generation of realistic cardiopulmonary exercise test data with a conditional generative adversarial neural network

Zignoli, Andrea; Fruet, Damiano

doi:10.1109/star53492.2022.9859993

Cited by 1 publication

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Oxynet implements a cGAN [ 17 ], which can generate a sample window of CPET variables starting from an input of size 53 × 1. The first 50 elements of the input tensor are random values, whilst the last three values represent the exercise intensity domain that the generative model needs to replicate.…”

Section: Methodsmentioning

confidence: 99%

“…The output of the generator is delivered to the discriminator together with real examples, so the discriminator can learn how to separate between real and fake data. The reader interested in knowing more about the generation process is referred to the original publication [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

“…To the best of the author’s knowledge, CPET data generation counts only one example in the scientific literature. Zignoli et al [ 17 ] developed a conditional generative adversarial neural network (cGAN) to re-create a window of pre-selected CPET variables corresponding to an intensity-specific pattern. Third, the explanation of the why behind the detection of an exercise threshold.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Models for the Automatic Detection of Exercise Thresholds in Cardiopulmonary Exercising Tests: From Regression to Generation to Explanation

Zignoli

2023

Sensors

Self Cite

View full text Add to dashboard Cite

The cardiopulmonary exercise test (CPET) constitutes a gold standard for the assessment of an individual’s cardiovascular fitness. A trend is emerging for the development of new machine-learning techniques applied to the automatic process of CPET data. Some of these focus on the precise task of detecting the exercise thresholds, which represent important physiological parameters. Three are the major challenges tackled by this contribution: (A) regression (i.e., the process of correctly identifying the exercise intensity domains and their crossing points); (B) generation (i.e., the process of artificially creating a CPET data file ex-novo); and (C) explanation (i.e., proving an interpretable explanation about the output of the machine learning model). The following methods were used for each challenge: (A) a convolutional neural network adapted for multi-variable time series; (B) a conditional generative adversarial neural network; and (C) visual explanations and calculations of model decisions have been conducted using cooperative game theory (Shapley’s values). The results for the regression, generation, and explanatory techniques for AI-assisted CPET interpretation are presented here in a unique framework for the first time: (A) machine learning techniques reported an expert-level accuracy in the classification of exercise intensity domains; (B) experts are not able to substantially differentiate between a real vs an artificially generated CPET; and (C) Shapley’s values can provide an explanation about the choices of the algorithms in terms of ventilatory variables. With the aim to increase their technology-readiness level, all the models discussed in this contribution have been incorporated into a free-to-use Python package called pyoxynet (ver. 12.1). This contribution should therefore be of interest to major players operating in the CPET device market and engineering.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%