Sraitih Mohamed, Jabrane Younes, Hajjam El Hassani Amir
MSC Laboratory, Cadi Ayyad University, 40000 Marrakech, Morocco.
Nanomedicine Imagery & Therapeutics Laboratory, EA4662-UBFC, UTBM, 90000 Belfort, France.
J Clin Med. 2022 Aug 23;11(17):4935. doi: 10.3390/jcm11174935.
An automatic electrocardiogram (ECG) myocardial infarction detection system needs to satisfy several requirements to be efficient in real-world practice. These requirements, such as reliability, less complexity, and high performance in decision-making, remain very important in a realistic clinical environment. In this study, we investigated an automatic ECG myocardial infarction detection system and presented a new approach to evaluate its robustness and durability performance in classifying the myocardial infarction (with no feature extraction) under different noise types. We employed three well-known supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), and tested the performance and robustness of these techniques in classifying normal (NOR) and myocardial infarction (MI) using real ECG records from the PTB database after normalization and segmentation of the data, with a suggested inter-patient paradigm separation as well as noise from the MIT-BIH noise stress test database (NSTDB). Finally, we measured four metrics: accuracy, precision, recall, and F1-score. The simulation revealed that all of the models performed well, with values of over 0.50 at lower SNR levels, in terms of all the metrics investigated against different types of noise, indicating that they are encouraging and acceptable under extreme noise situations are are thus considered sustainable and robust models for specific forms of noise. All of the methods tested could be used as ECG myocardial infarction detection tools in real-world practice under challenging circumstances.
一个自动心电图（ECG）心肌梗死检测系统要在实际应用中高效运行，需要满足若干要求。这些要求，如可靠性、较低的复杂性以及在决策方面的高性能，在现实临床环境中仍然非常重要。在本研究中，我们对一个自动ECG心肌梗死检测系统进行了研究，并提出了一种新方法，用于评估其在不同噪声类型下对心肌梗死进行分类（无需特征提取）时的鲁棒性和耐久性表现。我们采用了三种著名的监督式机器学习模型：支持向量机（SVM）、k近邻（KNN）和随机森林（RF），并在对来自PTB数据库的真实ECG记录进行归一化和分割后，使用这些技术对正常（NOR）和心肌梗死（MI）进行分类，同时采用建议的患者间范式分离以及来自MIT - BIH噪声压力测试数据库（NSTDB）的噪声，测试了这些技术的性能和鲁棒性。最后，我们测量了四个指标：准确率、精确率、召回率和F1分数。模拟结果表明，所有模型在较低信噪比水平下，针对所有研究的指标，在不同类型噪声下的表现都很好，值均超过0.50，这表明它们在极端噪声情况下令人鼓舞且可接受，因此被认为是针对特定形式噪声的可持续且鲁棒的模型。在具有挑战性的情况下，所有测试的方法都可以在实际应用中用作ECG心肌梗死检测工具。
