Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:832-836. doi: 10.1109/EMBC48229.2022.9871777.
PPG (Photoplethysmography) and ECG (Electro-cardiogram) physiological signals have been known to have certain indicators for establishing blood pressure (BP) levels. Continuous monitoring of blood pressure (BP) is highly valuable for cardiovascular patients; however the existing non-invasive cuff-based blood pressure monitoring system is discreet and applies artificial pressure on patients' arms that is uncomfortable. The other invasive method is highly interventional in nature and is highly disturbing when the patient is recuperating in the hospital wards or elsewhere. A non-invasive cuff-less, non-disturbing, and continuous BP measurement system targeted toward surgical, clinical, and domestic usage are proposed in this work. A convolutional neural network (CNN) followed by a long short-term memory layer (LSTM) was designed and applied to ECG and PPG signals to present accurate systolic blood pressure (SBP), and diastolic blood pressure (DBP). For developing the CNN-LSTM layers, a novel and open-source dataset was compiled that consisted of PPG and ECG signals from 30 healthy participants and is made publicly available for further usage to the research community. The novel CNN-LSTM based cuff-less blood pressure evaluation system presented a mean absolute error (MAE) of 2.57 mmHg and 3.44 mmHg for SBP and DBP respectively with similar standard-deviation (SD) metrics. The characterized error metrics of the proposed system are the lowest to date when compared to other prior work. Clinical Relevance- A cuff-less BP estimation system allows patients to have easy access to blood pressure evaluation as well as aid in determining unsafe health ailments like hypertension. Ready access to such system will not only allow practitioners to continuously monitor BP in hospitals but also help patients to regularly monitor BP more frequently at their convenience.
PPG（光电容积脉搏波）和 ECG（心电图）生理信号已被证明具有确定血压（BP）水平的某些指标。连续监测血压（BP）对心血管病患者非常有价值；然而，现有的非侵入性袖带式血压监测系统不够隐蔽，会对患者手臂施加不舒适的人工压力。另一种侵入性方法本质上具有高度的介入性，当患者在医院病房或其他地方康复时会非常令人不安。本工作提出了一种针对手术、临床和家庭使用的非侵入性、无袖带、无干扰和连续的血压测量系统。设计并应用卷积神经网络（CNN）和长短期记忆层（LSTM）来对 ECG 和 PPG 信号进行分析，以准确呈现收缩压（SBP）和舒张压（DBP）。为了开发 CNN-LSTM 层，我们编译了一个新颖的开源数据集，其中包含 30 名健康参与者的 PPG 和 ECG 信号，并公开发布，以供研究社区进一步使用。基于新颖的 CNN-LSTM 的无袖带血压评估系统的收缩压和舒张压的平均绝对误差（MAE）分别为 2.57mmHg 和 3.44mmHg，具有相似的标准偏差（SD）指标。与其他先前的工作相比，所提出系统的特征误差指标是最低的。临床相关性-无袖带 BP 估计系统允许患者轻松进行血压评估，并有助于确定高血压等不安全的健康疾病。这种系统的便捷获取不仅可以让医生在医院中持续监测 BP，还可以帮助患者更频繁地在自己方便的时候定期监测 BP。
