Deep learning assessment of left ventricular hypertrophy based on electrocardiogram

Xiaoli Zhao; Guifang Huang; Lin Wu; Min Wang; Xuemin He; Jyun-Rong Wang; Bin Zhou; Yong Liu; Yesheng Lin; Dinghui Liu; Xianguan Yu; Suzhen Liang; Borui Tian; Linxiao Liu; Yanming Chen; Shuhong Qiu; Xujing Xie; Lanqing Han; Xiaoxian Qian

doi:10.3389/fcvm.2022.952089

Deep learning assessment of left ventricular hypertrophy based on electrocardiogram

Front Cardiovasc Med. 2022 Aug 11:9:952089. doi: 10.3389/fcvm.2022.952089. eCollection 2022.

Authors

Xiaoli Zhao¹, Guifang Huang², Lin Wu¹, Min Wang¹, Xuemin He³, Jyun-Rong Wang^{4

5}, Bin Zhou¹, Yong Liu¹, Yesheng Lin¹, Dinghui Liu¹, Xianguan Yu¹, Suzhen Liang¹, Borui Tian¹, Linxiao Liu¹, Yanming Chen³, Shuhong Qiu², Xujing Xie¹, Lanqing Han⁶, Xiaoxian Qian¹

Affiliations

¹ Department of Cardiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
² China Unicom (Guangdong) Industrial Internet Ltd., Guangzhou, China.
³ Department of Endocrine and Metabolic Diseases, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
⁴ LCFC (Hefei) Electronics Technology Co., Ltd., Hefei, China.
⁵ Hefei LCFC Information Technology Co., Ltd., Hefei, China.
⁶ Center for Artificial Intelligence, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China.

Abstract

Background: Current electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) have low sensitivity. Deep learning (DL) techniques have been widely used to detect cardiac diseases due to its ability of automatic feature extraction of ECG. However, DL was rarely applied in LVH diagnosis. Our study aimed to construct a DL model for rapid and effective detection of LVH using 12-lead ECG.

Methods: We built a DL model based on convolutional neural network-long short-term memory (CNN-LSTM) to detect LVH using 12-lead ECG. The echocardiogram and ECG of 1,863 patients obtained within 1 week after hospital admission were analyzed. Patients were evenly allocated into 3 sets at 3:1:1 ratio: the training set (n = 1,120), the validation set (n = 371) and the test set 1 (n = 372). In addition, we recruited 453 hospitalized patients into the internal test set 2. Different DL model of each subgroup was developed according to gender and relative wall thickness (RWT).

Results: The LVH was predicted by the CNN-LSTM model with an area under the curve (AUC) of 0.62 (sensitivity 68%, specificity 57%) in the test set 1, which outperformed Cornell voltage criteria (AUC: 0.57, sensitivity 48%, specificity 72%) and Sokolow-Lyon voltage (AUC: 0.51, sensitivity 14%, specificity 96%). In the internal test set 2, the CNN-LSTM model had a stable performance in predicting LVH with an AUC of 0.59 (sensitivity 65%, specificity 57%). In the subgroup analysis, the CNN-LSTM model predicted LVH by 12-lead ECG with an AUC of 0.66 (sensitivity 72%, specificity 60%) for male patients, which performed better than that for female patients (AUC: 0.59, sensitivity 50%, specificity 71%).

Conclusion: Our study established a CNN-LSTM model to diagnose LVH by 12-lead ECG with higher sensitivity than current ECG diagnostic criteria. This CNN-LSTM model may be a simple and effective screening tool of LVH.

Keywords: convolutional neural network-long short-term memory; deep learning model; echocardiography; electrocardiogram; left ventricular hypertrophy.