Dynamics of gas exchange and heart rate signal entropy in standard cardiopulmonary exercise testing during critical periods of growth and development

Physiological Reports · 2024 · 2 citations

• Medicine
• Cardiology
• Internal medicine

CPET phases by 14.10% and 23.79%, respectively, p < 0.01. In females, late-pubertal had 17.6% lower HR SampEn compared to early-pubertal participants (p < 0.05). Breath-by-breath gas exchange and HR data from CPET are amenable to SampEn analysis that leads to novel insight into physiological responses to work intensity, and sex and maturational effects.

Signal Variability Comparative Analysis of Healthy Early- and Late-Pubertal Children during Cardiopulmonary Exercise Testing

Medicine & Science in Sports & Exercise · 2023 · 5 citations

• Medicine
• Cardiology
• Internal medicine

PURPOSE: The kinetics of physiological responses to exercise have traditionally been characterized by estimating exponential equation parameters using iterative best-fit techniques of heart rate (HR) and gas exchange (respiratory rate, oxygen uptake (V̇O 2 ), carbon dioxide output, and ventilation). In this study, we present a novel approach to characterizing the maturation of physiological responses to exercise in children by accounting for response uncertainty and variability. METHODS: Thirty-seven early-pubertal (17 females, 20 males) and 44 late-pubertal (25 females, 19 males) participants performed three multiple brief exercise bouts (MBEB). MBEB consisted of ten 2-min bouts of cycle ergometry at constant work rate interspersed by 1-min rest. Exercise intensity was categorized as low, moderate, or high, corresponding to 40%, 60%, and 80% of peak work rate, and performed in random order on 3 separate days. We evaluated sample entropy (SampEn), approximate entropy, detrended fluctuation analysis, and average absolute local variability of HR and gas exchange. RESULTS: SampEn of HR and gas-exchange responses to MBEB was greater in early- compared with late-pubertal participants (e.g., V̇O 2 early-pubertal vs late-pubertal, 1.70 ± 0.023 vs 1.41 ± 0.027; P = 2.97 × 10 -14 ), and decreased as MBEB intensity increased (e.g., 0.37 ± 0.01 HR for low-intensity compared with 0.21 ± 0.014 for high intensity, P = 3.56 × 10 -17 ). Females tended to have higher SampEn than males (e.g., 1.61 ± 0.025 V̇O 2 for females vs 1.46 ± 0.031 for males, P = 1.28 × 10 -4 ). Average absolute local variability was higher in younger participants for both gas exchange and HR (e.g., early-pubertal vs late-pubertal V̇O 2 , 17.48 % ± 0.56% vs 10.24 % ± 0.34%; P = 1.18 × 10 -21 ). CONCLUSIONS: The greater entropy in signal response to a known, quantifiable exercise perturbation in the younger children might represent maturation-dependent, enhanced competition among physiological controlling mechanisms that originate at the autonomic, subconscious, and cognitive levels.

Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology

American Journal of Preventive Cardiology · 2022 · 95 citations

• Computer Science
• Artificial Intelligence
• Machine Learning

Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.

Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification

arXiv (Cornell University) · 2021 · 51 citations

• Computer Science
• Computer Science
• Artificial Intelligence

In recent years, the availability of digitized Whole Slide Images (WSIs) has enabled the use of deep learning-based computer vision techniques for automated disease diagnosis. However, WSIs present unique computational and algorithmic challenges. WSIs are gigapixel-sized ($\sim$100K pixels), making them infeasible to be used directly for training deep neural networks. Also, often only slide-level labels are available for training as detailed annotations are tedious and can be time-consuming for experts. Approaches using multiple-instance learning (MIL) frameworks have been shown to overcome these challenges. Current state-of-the-art approaches divide the learning framework into two decoupled parts: a convolutional neural network (CNN) for encoding the patches followed by an independent aggregation approach for slide-level prediction. In this approach, the aggregation step has no bearing on the representations learned by the CNN encoder. We have proposed an end-to-end framework that clusters the patches from a WSI into ${k}$-groups, samples ${k}'$ patches from each group for training, and uses an adaptive attention mechanism for slide level prediction; Cluster-to-Conquer (C2C). We have demonstrated that dividing a WSI into clusters can improve the model training by exposing it to diverse discriminative features extracted from the patches. We regularized the clustering mechanism by introducing a KL-divergence loss between the attention weights of patches in a cluster and the uniform distribution. The framework is optimized end-to-end on slide-level cross-entropy, patch-level cross-entropy, and KL-divergence loss (Implementation: https://github.com/YashSharma/C2C).