Analyzing the cardiovascular system condition via Electrocardiography (ECG) is a common and highly effective approach, and it has been practiced and perfected over many decades. ECG sensing is non-invasive and relatively easy to acquire, and yet it is still cumbersome for holter monitoring tests that may span over hours and even days. A possible alternative in this context is Photoplethysmography (PPG): An optically-based signal that measures blood volume fluctuations, as typically sensed by conventional ``wearable devices''. While PPG presents clear advantages in acquisition, convenience, and cost-effectiveness, ECG provides more comprehensive information, allowing for a more precise detection of heart conditions. This implies that a conversion from PPG to ECG, as recently discussed in the literature, inherently involves an unavoidable level of uncertainty. In this paper we introduce a novel methodology for addressing the PPG-2-ECG conversion, and offer an enhanced classification of cardiovascular conditions using the given PPG, all while taking into account the uncertainties arising from the conversion process. We provide a mathematical justification for our proposed computational approach, and present empirical studies demonstrating its superior performance compared to state-of-the-art baseline methods.

翻译：通过心电图（ECG）分析心血管系统状况是一种常见且高效的方法，该方法历经数十年实践已臻完善。ECG传感具有无创性且相对易于采集，但对于可能持续数小时甚至数日的动态心电图监测而言，其操作仍显繁琐。在此背景下，光电容积脉搏波（PPG）成为一种可能的替代方案：这是一种基于光学原理、测量血容量波动的信号，通常由常规“可穿戴设备”采集。尽管PPG在采集便捷性与成本效益方面具有明显优势，但ECG能提供更全面的信息，可实现更精确的心脏疾病检测。这意味着如近期文献所探讨的PPG到ECG的转换过程，本质上存在不可避免的不确定性。本文提出一种处理PPG-2-ECG转换的新方法，在充分考虑转换过程所产生不确定性的前提下，利用给定PPG信号实现心血管疾病分类的增强。我们为所提出的计算方法提供了数学论证，并通过实证研究证明，相较于现有基线方法，本方法具有更优越的性能。