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Estimation and prediction of propafenone on the termination of atrial fibrillation by state-space models

机译:状态空间模型在心房纤颤终止中普罗帕酮的估计和预测

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Atrial fibrillation (AF) is the most frequent cardiac arrhythmia seen in clinical practice. Several therapeutical approaches have been developed to terminate the AF and the effects are evaluated by the reduction of the wavelet number after the treatments. In this paper, the state-space model was developed and applied to estimate the effects of pharmacological therapy on AF. Recordings (224-site bipolar recordings) of plaque electrode arrays placed on the right and left atria of pigs with sustained AF induced by rapid atrial-pacing were used to train and test the state-space models. The cardiac mapping data from five pigs treated with intravenous administration of antiarrhythmia drug, propafenone (PPF), were evaluated. The recordings of cardiac activity before the drug treatment were input to the model and the model output reported the estimated wavelet number of atria after the drug treatment. The results show that the predicting accuracy can reach 90%. It is expected that the developed state-space model can be further extended to assist the clinical staffs to estimate the effects of treatments for the AF patients in the future.
机译:心房颤动(AF)是临床实践中最常见的心律不齐。已经开发了几种治疗方法来终止AF,并且通过减少治疗后的小波数来评估效果。在本文中,开发了状态空间模型并将其用于评估药物治疗对房颤的影响。放置在猪的左右心房的斑块电极阵列的记录(224个位置的双极记录)被快速心房起搏诱导的持续性房颤用于训练和测试状态空间模型。评估了五只经静脉注射抗心律不齐药物普罗帕酮(PPF)治疗的猪的心脏图谱数据。将药物治疗前的心脏活动记录输入模型,模型输出报告药物治疗后心房的估计小波数。结果表明,该方法的预测精度可以达到90%。可以预期,可以进一步扩展已开发的状态空间模型,以帮助临床人员在将来评估房颤患者的治疗效果。

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