Key result
A Random Forest machine learning model using features extracted from ECG and respiratory signals diagnosed atherosclerosis in mice with an accuracy of 97.57%.
Why the study?
Machine learning techniques have improved cardiovascular disease diagnosis, but models using ECG and respiratory features to diagnose atherosclerosis under MRI noise were needed.
Does a machine learning model using ECG and respiratory signals accurately diagnose atherosclerosis in mice under MRI?
Does a machine learning model using ECG and respiratory signals accurately diagnose atherosclerosis in mice under MRI?
A machine learning model using ECG and respiratory signals can accurately detect atherosclerosis in a murine model under MRI, presenting a step towards real-time MRI synchronous cardiovascular findings.
Answers are grounded in this paper and cite their sources.
Hypothesis-generating for ML-based atherosclerosis detection from ECG/respiratory signals in mice; leaves open human translation and validation.
Over the past few decades, the diagnosis of heart disease, namely atherosclerosis, has been of interest for many researchers. The use of electrocardiogram (ECG) triggered Magnetic Resonance Imaging (MRI) sequences, enables exploration of aortic arches with high-resolution images. Lately, implementation of numerous Machine Learning (ML) techniques improved the accuracy of diagnosing diseases especially cardiovascular ones. In this paper, we proposed a model to diagnose atherosclerosis in mice using features extracted from the electrocardiogram (ECG) and respiratory signals. We defined new variables, after adaptation to the altered nature of electrophysiological signals with superimposed MRI noise. Using the 5-fold cross validation test, we introduced the features into the Random Forest (RF) classifier and obtained an accuracy of 96.96%. Feature selection then took place using the Info Gain (IG) attribute evaluator, which increased our model's accuracy to 97.57%. This presents a first step towards a real-time MRI synchronous cardiovascular finding system.
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Sabbah et al. (2021) studied Atherosclerosis. Machine Learning (Random Forest classifier) was evaluated on Diagnostic accuracy. A Random Forest machine learning model using features extracted from ECG and respiratory signals diagnosed atherosclerosis in mice with an accuracy of 97.57%.
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