Liu Xin, Qu Hongyi, Huang Chuangxin, Meng Lingwei, Chen Qi, Wang Qiuliang
Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou, Jiangxi Province, 341000, China.
Department of Automation, University of Science and Technology of China, Hefei 230026, China.
Heliyon. 2024 Feb 8;10(4):e25992. doi: 10.1016/j.heliyon.2024.e25992. eCollection 2024 Feb 29.
Centrifugal blood pumps are important devices used to treat heart failure. However, they are prone to high-risk suction events that pose a threat to human health when operating at high speeds. To address these issues, a normal suction detection method and a suction suppression method based on the FFT-GAPSO-LSTM model and speed modulation were proposed. The innovation of this suction detection method lies in the application of the genetic particle swarm optimisation (GAPSO) and the fast Fourier transform (FFT) feature extraction method to the long-term and short-term memory (LSTM) model, thereby improving the accuracy of suction detection. After detecting signs of suction, the suction suppression method designed in this study based on variable-speed modulation immediately takes effect, enabling the centrifugal blood pump to quickly return to its normal state by controlling the speed. The suction detection method was divided into four steps. First, a mathematical model of the coupling of the cardiovascular system and the centrifugal blood pump was established, and a real-time blood flow curve was obtained through model simulation. Second, the signal was preprocessed by adding Gaussian white noise and low-pass filtering to make the blood flow signal close to actual working conditions while retaining the original characteristics. Subsequently, through fast Fourier transform (FFT) analysis of the processed curve, the spectral characteristics that can characterise the working state of the centrifugal blood pump were extracted. Finally, the parameters of the LSTM model were optimised using the GAPSO, and the improved LSTM model was used to train and test the blood flow spectrum feature set. The results show that the suction detection method of the FFT-GAPSO-LSTM model can effectively detect whether centrifugal blood pump suction occurs and has certain advantages over other methods. In addition, the simulation results of the suction suppression were excellent and could effectively suppress the occurrence of suction. These results provide a reference for the design of centrifugal blood pump control systems.
离心式血泵是用于治疗心力衰竭的重要设备。然而,它们在高速运行时容易发生高风险的抽吸事件,对人体健康构成威胁。为了解决这些问题,提出了一种基于快速傅里叶变换-遗传粒子群优化-长短期记忆(FFT-GAPSO-LSTM)模型和速度调制的正常抽吸检测方法和抽吸抑制方法。这种抽吸检测方法的创新之处在于将遗传粒子群优化算法(GAPSO)和快速傅里叶变换(FFT)特征提取方法应用于长短期记忆(LSTM)模型,从而提高了抽吸检测的准确性。在检测到抽吸迹象后,本研究设计的基于变速调制的抽吸抑制方法立即生效,通过控制速度使离心式血泵迅速恢复到正常状态。抽吸检测方法分为四个步骤。首先,建立心血管系统与离心式血泵耦合的数学模型,并通过模型仿真获得实时血流曲线。其次,通过添加高斯白噪声和低通滤波对信号进行预处理,使血流信号接近实际工作条件,同时保留原始特征。随后,对处理后的曲线进行快速傅里叶变换(FFT)分析,提取能够表征离心式血泵工作状态的频谱特征。最后,使用GAPSO对LSTM模型的参数进行优化,并使用改进后的LSTM模型对血流频谱特征集进行训练和测试。结果表明,FFT-GAPSO-LSTM模型的抽吸检测方法能够有效检测离心式血泵是否发生抽吸,与其他方法相比具有一定优势。此外,抽吸抑制的仿真结果良好,能够有效抑制抽吸的发生。这些结果为离心式血泵控制系统的设计提供了参考。