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• W4377029449 abstract "<b>Abstract ID 20324</b> <b>Poster Board 99</b> Machine learning (ML) models have demonstrated impressive applicability in classifying hemodynamic signals according to disease-specific features of overt cardiovascular (CV) conditions like myocardial infarction and hypertension. However, there remains a pressing need for identifying subtle, subclinical deviations in hemodynamic time-series associated with early disease states, to predict prognosis and prevent deteriorating processes. Our previous work indicates the predictive capacity of non-linear parameters of blood pressure variability in discriminating between healthy and prediabetic rats in a sex- and age-specific manner. Whilst pre-diabetic rats lack signs of gross CV insult, they possess blunted baroreceptor sensitivity and dysfunctional endothelial-dependent vasorelaxation only discernible upon complex, invasive hemodynamic manipulations. Our results show that female rats are relatively protected from prediabetic CV manifestations. Here, we evaluate the capacity of a ML convolutional neural network (CNN) in segregating arterial pressure (AP) signals collected from pre-diabetic vs. healthy controls according to disease state, age, and sex. 4-week-old Sprague Dawley, male or female rats were fed a high-calorie (HC) or a normal diet for 12 or 24 weeks (wks), representing young vs. old rats, respectively. Female rats fed for 24 wks were further divided into three subcategories: a sham group, one undergoing ovariectomy at wk 12 (OVX), and another receiving estrogen treatment for 12 wks post-surgery (OVX+E2). At weeks 12 or 24, anesthetized rats were instrumented for invasive hemodynamics monitoring via a pressure transducer inserted through the carotid artery. AP signals of length 300 seconds were downsampled to 50Hz, sliced to 1000 data points per bin (16.7s), and Fourier-transformed. Fourier plots were cropped to remove graph labels/axes and were then fed into the CNN. Our model consisted of 5 convolutional layers, separated by batch normalization, ReLU activation, and max pooling. The outcome of the 5th convolutional layer had 40% of data dropped out and was delivered to a fully connected layer and then to a softmax function. The model utilized cross entropy for a loss function and ADAM for an optimization function. 80% of the data was randomly allocated for training and 10% each for validation and testing. Test accuracy (TA), AUROC, and AUPRC were used to evaluate the model. Our CNN successfully classified rats based on diet (HC vs. control, test accuracy: 97.66%, AUROC: 0.9975, AUPRC: 0.9975), sex (TA: 96.63%, AUROC: 0.9921, AUPRC: 0.9922), surgical outcome (sham vs. OVX vs. OVX+E2, TA: 96.55%, AUROC: 0.9950, AUPRC: 0.9904), and age (16 vs. 28 weeks, TA: 96.65%, AUROC: 0.9802, AUPRC: 0.9757). Upon challenging the model with a 4-way classification task on age and diet (16 wks + control diet, 16 wks + HC, 28 wks + control, and 28 wks + HC), it was able to classify the above with TA: 92.79%, AUROC: 0.9764, and AUPRC: 0.9603. Application of a supervised ML model provides the opportunity of capturing several undefined features of hemodynamic control. Utility of a CNN overcomes the shortcomings associated with the use of other parameters of hemodynamic fluctuations which measure a single characteristic, by probing and compiling additional layers of differences and synthesizing a global appraisal of CV signals." @default.
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• W4377029449 date "2023-05-18" @default.
• W4377029449 modified "2023-09-29" @default.
• W4377029449 title "A Convolutional Neural Network Model classifies Beat-to-Beat Arterial Pressure Time-Series: Sex Stratification and Cardiovascular Risk Prediction" @default.
• W4377029449 doi "https://doi.org/10.1124/jpet.122.203240" @default.
• W4377029449 hasPublicationYear "2023" @default.
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