Assessing electrocardiogram changes after ischemic stroke with artificial intelligence

• Published in: PloS one 2022-12, Vol.17 (12), p.e0279706-e0279706
• Main Authors: Zeng, Ziqiang, Wang, Qixuan, Yu, Yingjing, Zhang, Yichu, Chen, Qi, Lou, Weiming, Wang, Yuting, Yan, Lingyu, Cheng, Zujue, Xu, Lijun, Yi, Yingping, Fan, Guangqin, Deng, Libin
• Format: Article
• Language: English
• Subjects: Abnormalities; Analysis; Anomalies; Arrhythmias, Cardiac; Artificial Intelligence; Artificial neural networks; Biology and Life Sciences; Cardiac arrhythmia; Cardiac function; Cardiology; Computer and Information Sciences; Datasets; Demographics; Demography; Diagnosis; Disease; EKG; Electrocardiogram; Electrocardiography; Electronic health records; Evaluation; Heart rate; Hospitals; Humans; Ischemia; Ischemic Stroke - diagnosis; Machine learning; Mathematical models; Medical prognosis; Medical records; Medicine and Health Sciences; Modelling; Neural networks; Neural Networks, Computer; Parameter identification; Prognosis; Research and Analysis Methods; Statistical analysis; Stroke; Stroke (Disease); Support vector machines
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0279706

Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI). We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs. Next, we trained the convolutional neural network (CNN), random forest (RF) and support vector machine (SVM) models to automatically detect the changes in the ECGs; Additionally, We compared the CNN scores of good prognosis (mRS ≤ 2) and poor prognosis (mRS > 2) to assess the prognostic value of CNN model. Finally, we used gradient class activation map (Grad-CAM) to localize the key abnormalities. Among the 3506 ECGs of the IS patients, 2764 ECGs (78.84%) led to an abnormal diagnosis. Then we divided ECGs in the primary cohort into three groups, normal ECGs (N-Ns), abnormal ECGs after the first ischemic stroke (A-ISs), and normal ECGs after the first ischemic stroke (N-ISs). Basic demographic and ECG parameter analyses showed that heart rate, QT interval, and P-R interval were significantly different between 673 N-ISs and 3546 N-Ns (p < 0.05). The CNN has the best performance among the three models in distinguishing A-ISs and N-Ns (AUC: 0.88, 95%CI = 0.86-0.90). The prediction scores of the A-ISs and N-ISs obtained from the all three models are statistically different from the N-Ns (p < 0.001). Futhermore, the CNN scores of the two groups (mRS > 2 and mRS ≤ 2) were significantly different (p < 0.05). Finally, Grad-CAM revealed that the V4 lead may harbor the highest probability of abnormality. Our study showed that a high proportion of post-IS ECGs harbored abnormal changes. Our CNN model can systematically assess anomalies in and prognosticate post-IS ECGs.