Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network
Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network
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DOI：
10.1016/j.bspc.2019.02.028
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Md. Sayed Tanveer;Md. Kamrul Hasan
中科院分区：
文献类型：
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作者：
Md. Sayed Tanveer;Md. Kamrul Hasan
虽然光电体积描记图（PPG）和心电图（ECG）信号可以通过提取各种特征来估计血压（BP），但是由于循环系统的各种疾病以及其他生理系统的相互作用而导致PPG和ECG信号的形态轮廓发生变化，使得这些特征的提取非常困难。在这项工作中，我们提出了一种基于波形的分层人工神经网络——长短期记忆（ANN-LSTM）模型，用于血压估计。该模型由两个层级组成，其中较低层级使用 ANN 从 ECG 和 PPG 波形中提取必要的形态特征，而较高层级使用 LSTM 层来解释较低层级提取的特征的时域变化。使用医疗仪器促进协会 (AAMI) 标准和英国高血压协会 (BHS) 标准对 39 名受试者对所提出的模型进行了评估。该方法满足收缩压（SBP）和舒张压（DBP）的估算标准。对于所提出的网络，SBP 估计的平均绝对误差 (MAE) 和均方根误差 (RMSE) 分别为 1.10 和 1.56mmHg，DBP 估计的平均绝对误差 (MAE) 和均方根误差 (RMSE) 分别为 0.58 和 0.85mmHg。我们发现所提出的分层 ANN-LSTM 模型的性能优于其他基于特征工程的网络。结果表明，所提出的模型能够自动提取必要的特征及其时域变化，以非侵入性连续方式可靠地估计血压。该方法有望极大地促进目前可用的移动医疗设备的无袖连续血压测量。
Although photoplethysmogram (PPG) and electrocardiogram (ECG) signals can be used to estimate blood pressure (BP) by extracting various features, the changes in morphological contours of both PPG and ECG signals due to various diseases of circulatory system and interaction of other physiological systems make the extraction of such features very difficult. In this work, we propose a waveform-based hierarchical Artificial Neural Network – Long Short Term Memory (ANN-LSTM) model for BP estimation. The model consists of two hierarchy levels, where the lower hierarchy level uses ANNs to extract necessary morphological features from ECG and PPG waveforms and the upper hierarchy level uses LSTM layers to account for the time domain variation of the features extracted by the lower hierarchy level. The proposed model is evaluated on 39 subjects using the Association for the Advancement of Medical Instrumentations (AAMI) standard and the British Hypertension Society (BHS) standard. The method satisfies both the standards in the estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP). For the proposed network, the mean absolute error (MAE) and the root mean square error (RMSE) for SBP estimation are 1.10 and 1.56 mmHg, respectively, and for DBP estimation are 0.58 and 0.85 mmHg, respectively. The performance of the proposed hierarchical ANN-LSTM model is found to be better than the other feature engineering-based networks. It is shown that the proposed model is able to automatically extract the necessary features and their time domain variations to estimate BP reliably in a noninvasive continuous manner. The method is expected to greatly facilitate the presently available mobile health-care gadgets in cuffless continuous BP estimation.