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A Novel Short-Term Blood Pressure Prediction Model Based on LSTM

机译：基于LSTM的新型短期血压预测模型

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摘要

Blood pressure (BP) can reflect many physiological characteristics, and timely monitoring of it can somehow prevent hypertension, asphyxia and other diseases. With the development of clinical medical technology, the accuracy requirements of the intelligent physiological monitoring equipment for predicting the physiological characteristics of BP are gradually increasing. This paper proposes a BP prediction model based on Long Short Term Memory Networks (LSTM-NN), which makes full use of the efficient processing characteristics of LSTM for time series information and accurately predicts the systolic BP and diastolic BP. The primitive photoplethysmographic pulse wave (PPW) signal and actual BP data in different time periods and different states were collected on 6 adult goats simultaneously. The blood flow changes were stimulated by injection of adrenaline to obtain a wide range of raw data to improve the generalization ability of the model. The clinical features of each PPW cycle were introduced into the LSTM model for training and prediction to resolve actual systolic BP and diastolic BP. Comparing the model with the prediction effect of BP neural network (BP-NN) model, the result shows that the prediction accuracy of LSTM model is high and the robustness is strong. The maximum error values for systolic and diastolic pressure prediction are 1.05mmHg and 1.8mmHg, respectively.
机译：血压（BP）可以反映许多生理特性，并及时监测它可以以某种方式预防高血压，窒息和其他疾病。随着临床医疗技术的发展，智能生理监测设备的准确性要求预测BP的生理特性逐渐增加。本文提出了一种基于长短短期内存网络（LSTM-NN）的BP预测模型，其充分利用LSTM的时间序列信息的高效处理特性，并准确地预测收缩式BP和舒张压BP。在不同的时间段和不同状态下的光体积描记图元脉波（PPW）信号和实际BP数据同时被收集在6个成年羊。通过注射肾上腺素来刺激血流变化，以获得广泛的原始数据，以改善模型的泛化能力。将每个PPW循环的临床特征引入LSTM模型中，用于训练和预测，以解决实际的收缩性BP和舒张压BP。将模型与BP神经网络（BP-NN）模型的预测效果进行比较，结果表明，LSTM模型的预测精度高，鲁棒性强。收缩镜和舒张压预测的最大误差值分别为1.05mmHg和1.8mmHg。

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