Human Centric (HUMIC) Engineering, Telkom University, Bandung, Indonesia.
School of Computing, Telkom University, Bandung, Indonesia.
PLoS One. 2020 May 14;15(5):e0231635. doi: 10.1371/journal.pone.0231635. eCollection 2020.
Spontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding MVA prediction such as the effect of number of ECG features on a prediction remaining unclear, possibility that an alert for occurring MVA may arrive very late and uncertainty in the performance of the algorithm predicting MVA minutes before onset. To overcome the aforementioned problems, this research conducts an in-depth study on the number and types of ECG features that are implemented in a decision tree classifier. In addition, this research also investigates an algorithm's execution time before the occurrence of MVA to minimize delays in warnings for MVA. Lastly, this research aims to study both the sensitivity and specificity of an algorithm to reveal the performance of MVA prediction algorithms from time to time. To strengthen the results of analysis, several classifiers such as support vector machine and naive Bayes are also examined for the purpose of comparison study. There are three phases required to achieve the objectives. The first phase is literature review on existing relevant studies. The second phase deals with design and development of four modules for predicting MVA. Rigorous experiments are performed in the feature selection and classification modules. The results show that eight ECG features with decision tree classifier achieved good prediction performance in terms of execution time and sensitivity. In addition, the results show that the highest percentage for sensitivity and specificity is 95% and 90% respectively, in the fourth 5-minute interval (15.1 minutes-20 minutes) that preceded the onset of an arrhythmia event. Such results imply that the fourth 5-minute interval would be the best time to perform prediction.
自发性预测恶性室性心律失常 (MVA) 有助于避免抢救操作的延迟。最近，研究人员已经开发了几种使用源自心电图 (ECG) 的各种特征来预测 MVA 的算法。然而，关于 MVA 预测仍存在一些悬而未决的问题，例如 ECG 特征数量对预测的影响尚不清楚，可能会很晚才发出发生 MVA 的警报，以及预测 MVA 的算法在发作前几分钟的性能存在不确定性。为了克服上述问题，本研究深入研究了决策树分类器中实施的 ECG 特征的数量和类型。此外，本研究还研究了算法在 MVA 发生前的执行时间，以最大程度地减少 MVA 警报的延迟。最后，本研究旨在研究算法的敏感性和特异性，以揭示 MVA 预测算法的性能。为了加强分析结果，还检查了支持向量机和朴素贝叶斯等几种分类器，以进行比较研究。要实现目标需要三个阶段。第一阶段是对现有相关研究进行文献综述。第二阶段涉及设计和开发四个用于预测 MVA 的模块。在特征选择和分类模块中进行了严格的实验。结果表明，具有决策树分类器的 8 个 ECG 特征在执行时间和敏感性方面具有良好的预测性能。此外，结果表明，在心律失常事件发作前的第四个 5 分钟间隔（15.1 分钟-20 分钟）中，敏感性和特异性的最高百分比分别为 95%和 90%。这意味着第四个 5 分钟间隔将是进行预测的最佳时间。
