Naseri Arman, Tax David M J, Reinders Marcel, van der Bilt Ivo
Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands.
Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands.
Heliyon. 2024 Oct 30;10(21):e39927. doi: 10.1016/j.heliyon.2024.e39927. eCollection 2024 Nov 15.
Cardiovascular disease (CVD) is the most important cause of morbidity and mortality worldwide. Early detection, prevention or even prediction is of pivotal importance to reduce the burden of cardiovascular disease and its associated costs. Low cost, consumer-grade smartwatches have the potential to revolutionize cardiovascular medicine by enabling continuous monitoring of heart rate and activity. When combined with machine learning(ML), the resulting large amounts of time series data hold the potential of detection, or exclusion of CVD. However, analyzing such large datasets is challenging due to the sparse presence of informative segments. Efficient selection of these segments is essential for developing predictive models for clinical deployment. The objective of this paper was to investigate the potential of an acceleration-deceleration curvebased ML model as a novel clinical indicator for the detection of cardiovascular diseases. We used data from the ME-TIME study; 42 participants from which 21 have a cardiovascular disease and 21 are health controls. Data from each subject was normalized to decrease inter-subject variability. A neural network model aggregated predictions per week. We showed that per-subject normalization by the peak value of curves during inactivity, aggregation of model predictions over a week, and using a contrastive loss, resulted in a predictive model with 99 % ± 3 % specificity and 40 % ± 49 % sensitivity on the development set, and 100 % specificity with 67 % ± 47 % sensitivity on the test set. Acceleration-deceleration curves are effective patterns for ruling out the presence of cardiovascular disease, but caution must be taken to properly pre-process the curves and carefully choosing a model that reduces the variability in the extracted curves.
心血管疾病（CVD）是全球发病和死亡的最重要原因。早期检测、预防甚至预测对于减轻心血管疾病负担及其相关成本至关重要。低成本的消费级智能手表有潜力通过实现对心率和活动的持续监测来彻底改变心血管医学。当与机器学习（ML）相结合时，由此产生的大量时间序列数据具有检测或排除心血管疾病的潜力。然而，由于信息片段的稀疏存在，分析如此大的数据集具有挑战性。有效选择这些片段对于开发用于临床部署的预测模型至关重要。本文的目的是研究基于加速度-减速曲线的机器学习模型作为检测心血管疾病的新型临床指标的潜力。我们使用了ME-TIME研究的数据；42名参与者，其中21人患有心血管疾病，21人作为健康对照。对每个受试者的数据进行归一化处理以减少个体间差异。一个神经网络模型汇总每周的预测结果。我们表明，通过非活动期间曲线的峰值进行个体归一化、在一周内汇总模型预测结果以及使用对比损失，在开发集上得到了一个预测模型，其特异性为99%±3%，敏感性为40%±49%，在测试集上特异性为100%，敏感性为67%±47%。加速度-减速曲线是排除心血管疾病存在的有效模式，但必须谨慎地对曲线进行适当预处理，并仔细选择一个能减少提取曲线变异性的模型。
