Instituto de Computação, Universidade Federal do Amazonas (UFAM), Av. Rodrigo Otávio, n° 6200, Manaus 69077-000, AM, Brazil.
Departamento de Computação, Universidade Federal do Piauí (UFPI), R. Dirce Oliveira, n° 1805, Teresina 64049-550, PI, Brazil.
Sensors (Basel). 2024 Sep 19;24(18):6046. doi: 10.3390/s24186046.
There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases.
心电图 (ECG) 和光体积描记图 (PPG)。这些方法中使用的传感器在可穿戴设备中非常受欢迎，它们将心脏监测扩展到了医院环境之外。然而，通过移动设备连续监测 ECG 信号具有挑战性，因为它要求用户在数据采集期间将手指按在设备上，这在长期内是不可行的。另一方面，PPG 没有这个限制。然而，从这个信号诊断这些异常的医学知识受到需要熟悉程度的限制，因为 ECG 作为金标准在文献中进行了研究和使用。为了最小化这个问题，这项工作提出了一种方法，PPG2ECG，它使用 PPG 和 ECG 信号域之间的相关性，从 PPG 信号推断 ECG 信号的波形。PPG2ECG 通过应用一组卷积滤波器在域之间进行映射，使用 U-net inception 神经网络架构学习将 PPG 输入信号转换为 ECG 输出信号。我们使用基于个性化和广义模型的两种评估策略来评估我们提出的方法，分别实现了 0.015 和 0.026 的平均误差值。我们的方法通过提供一种通过 PPG 信号准确且可行的连续监测 ECG 信号的方法，克服了以前方法的局限性。推断出的 ECG 与原始 ECG 之间的短距离证明了我们的方法具有可行性和潜力，可以帮助早期识别心脏病。
