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Abstract：
Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating photoplethysmogram (PPG) and electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls, our approach focuses on continuous, non-invasive monitoring. Key features, including the QRS interval, RR interval, augmentation index, heart rate, systolic pressure, diastolic pressure, and peak-to-peak amplitude, were carefully selected for their clinical relevance and ability to capture cardiovascular dynamics. This feature selection not only highlighted important physiological indicators but also helped reduce computational complexity and the risk of overfitting in machine learning models. The use of these features in training machine learning algorithms led to a model with impressive accuracy (98%), sensitivity (97.60%), specificity (96.90%), and precision (97.20%). Our integrated approach, combining PPG and ECG signals, demonstrates superior performance compared to single-signal strategies, emphasizing its potential in early and precise heart failure diagnosis. The study also highlights the importance of continuous monitoring with wearable technology, suggesting a significant stride forward in non-invasive cardiovascular health assessment. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.
摘要：
心力衰竭是一种普遍存在的心血管疾病，对健康有重大影响。需要有效的诊断策略进行及时干预。这项研究探讨了连续监测非侵入性信号的潜力，特别是整合光血管容积图（PPG）和心电图（ECG），用于增强心力衰竭的早期发现和诊断。利用来自MIMIC-III数据库的数据集，包括682名心力衰竭患者和954名对照，我们的方法侧重于连续，非侵入性监测。主要特点,包括QRS间期，RR间隔,增强指数，心率,收缩压,舒张压,和峰峰值幅度，因其临床相关性和捕获心血管动力学的能力而被仔细选择。这种特征选择不仅突出了重要的生理指标，还有助于降低计算复杂性和机器学习模型中过度拟合的风险。在训练机器学习算法中使用这些功能导致了一个具有令人印象深刻的准确性（98％）的模型，灵敏度（97.60%），特异性（96.90%），和精度（97.20%）。我们的综合方法，结合PPG和ECG信号，与单信号策略相比，表现出卓越的性能，强调其在早期和精确的心力衰竭诊断中的潜力。该研究还强调了使用可穿戴技术进行连续监测的重要性，表明在非侵入性心血管健康评估方面取得了重大进展。所提出的方法有望在硬件系统中实施，以实现连续监控，帮助早期发现和预防严重的健康状况。
