Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients’ health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation. Specifically, the proposed method utilizes a 2-layer bidirectional long short-term memory network as the sharing layer, followed by three identical architectures of 2-layer fully connected networks for task-specific blood pressure estimation. To learn the importance of task-specific losses automatically, an adaptive weight learning scheme based on the trend of validation loss is proposed. Extensive experiment results on Physionet Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II waveform database demonstrate that the proposed method using electrocardiogram signals obtains estimating performance of $0.12\pm 10.83$ mmHg, $0.13\pm 5.90$ mmHg, and $0.08\pm 6.47$ mmHg for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. It can meet the requirements of the British Hypertension Society standard and US Association of Advancement of Medical Instrumentation standard with a considerable margin. Combined with a wearable/portal electrocardiogram device, the proposed model can be deployed to a healthcare system to provide a long-term continuous blood pressure monitoring service, which would help to reduce the incidence of malignant complications to hypertension.

中文翻译：

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n基于自适应权重学习的多任务深度网络，用于使用心电图信号进行连续血压估计

通过与心电图和光体积描记器信号的组合分析来估计血压，对持续监测患者的健康状况引起了越来越多的兴趣。但是，由于能源成本，设备重量和尺寸等方面的考虑，大多数可穿戴/门户监控设备通常仅获取一种生理信号。在本研究中，基于单线索的新型自适应基于权重学习的多任务深度学习框架建议使用心电图信号进行连续血压估计。具体而言，所提出的方法利用2层双向长期短期存储网络作为共享层，然后采用3个完全相同的2层全连接网络体系结构进行特定任务的血压估计。为了自动学习特定任务损失的重要性，提出了一种基于验证损失趋势的自适应权重学习方案。在Physionet重症监护多参数智能监控（MIMIC）II波形数据库上进行的大量实验结果表明，所提出的使用心电图信号的方法能够获得心律图的估计性能。$0.12\pm 10.83$ 毫米汞柱 $0.13\pm 5.90$ 毫米汞柱，和 $0.08\pm 6.47$mmHg分别代表收缩压，舒张压和平均动脉压。它可以相当大程度地满足英国高血压学会标准和美国医疗器械进步协会标准的要求。结合可穿戴式/门式心电图设备，可以将所提出的模型部署到医疗保健系统中，以提供长期的连续血压监测服务，这将有助于减少高血压导致的恶性并发症的发生率。