Cardiovascular disease (CVD) is the number one cause of death in humans. Arrhythmia induced by gene mutations, heart disease, or hERG K⁺ channel inhibitors is a serious CVD that can lead to sudden death or heart failure. Conventional cardiomyocyte-based biosensors can record extracellular potentials and mechanical beating signals. However, parameter extraction and examination by the naked eye are the traditional methods for analyzing arrhythmic beats, and it is difficult to achieve automated and efficient arrhythmic recognition with these methods. In this work, we developed a unique automated template matching (ATM) cardiomyocyte beating model to achieve arrhythmic recognition at the single beat level with an interdigitated electrode impedance detection system. The ATM model was established based on a rhythmic template with a data length that was dynamically adjusted to match the data length of the target beat by spline interpolation. The performance of the ATM model under long-term astemizole, droperidol, and sertindole treatment at different doses was determined. The results indicated that the ATM model based on a random rhythmic template of a signal segment obtained after astemizole treatment presented a higher recognition accuracy (100% for astemizole treatment and 99.14% for droperidol and sertindole treatment) than the ATM model based on arrhythmic multitemplates. We believe this highly specific ATM method based on a cardiomyocyte beating model has the potential to be used for arrhythmia screening in the fields of cardiology and pharmacology.

心血管疾病 (CVD) 是导致人类死亡的第一大原因。由基因突变、心脏病或 hERG K ⁺引起的心律失常通道抑制剂是一种严重的心血管疾病，可导致猝死或心力衰竭。传统的基于心肌细胞的生物传感器可以记录细胞外电位和机械搏动信号。然而，参数提取和肉眼检查是分析心律失常的传统方法，这些方法难以实现自动化、高效的心律失常识别。在这项工作中，我们开发了一种独特的自动模板匹配 (ATM) 心肌细胞搏动模型，通过交叉电极阻抗检测系统实现单搏水平的心律失常识别。ATM模型是基于节奏模板建立的，其数据长度通过样条插值进行动态调整以匹配目标节拍的数据长度。确定了 ATM 模型在不同剂量的长期阿司咪唑、氟哌利多和舍吲哚治疗下的性能。结果表明，基于阿司咪唑治疗后获得的信号片段的随机节律模板的ATM模型比基于心律失常多模板的ATM模型具有更高的识别准确率（阿司咪唑治疗为100%，氟哌啶醇和舍吲哚治疗为99.14%）。我们相信这种基于心肌细胞搏动模型的高度特异性 ATM 方法有可能用于心脏病学和药理学领域的心律失常筛查。结果表明，基于阿司咪唑治疗后获得的信号片段的随机节律模板的ATM模型比基于心律失常多模板的ATM模型具有更高的识别准确率（阿司咪唑治疗为100%，氟哌啶醇和舍吲哚治疗为99.14%）。我们相信这种基于心肌细胞搏动模型的高度特异性 ATM 方法有可能用于心脏病学和药理学领域的心律失常筛查。结果表明，基于阿司咪唑治疗后获得的信号片段的随机节律模板的ATM模型比基于心律失常多模板的ATM模型具有更高的识别准确率（阿司咪唑治疗为100%，氟哌啶醇和舍吲哚治疗为99.14%）。我们相信这种基于心肌细胞搏动模型的高度特异性 ATM 方法有可能用于心脏病学和药理学领域的心律失常筛查。