Abstract
Background
Brugada syndrome is a major cause of sudden cardiac death in young people and has distinctive electrocardiographic (ECG) features. We aimed to develop a deep learning–enabled ECG model for automatic screening for Brugada syndrome to identify these patients at an early point in time, thus allowing for life-saving therapy.
Methods
A total of 276 ECGs with a type 1 Brugada ECG pattern (276 type 1 Brugada ECGs and another randomly retrieved 276 non-Brugada type ECGs for 1:1 allocation) were extracted from the hospital-based ECG database for a 2-stage analysis with a deep learning model. After trained network for identifying right bundle branch block pattern, we transferred the first-stage learning to the second task to diagnose the type 1 Brugada ECG pattern. The diagnostic performance of the deep learning model was compared with that of board-certified practicing cardiologists. The model was further validated in an independent ECG data set collected from hospitals in Taiwan and Japan.
Results
The diagnoses by the deep learning model (area under the receiver operating characteristic curve [AUC] 0.96, sensitivity 88.4%, specificity 89.1%) were highly consistent with the standard diagnoses (kappa coefficient 0.78). However, the diagnoses by the cardiologists were significantly different from the standard diagnoses, with only moderate consistency (kappa coefficient 0.63). In the independent ECG cohort, the deep learning model still reached a satisfactory diagnostic performance (AUC 0.89, sensitivity 86.0%, specificity 90.0%).
Conclusions
We present the first deep learning–enabled ECG model for diagnosing Brugada syndrome, which appears to be a robust screening tool with a diagnostic potential rivalling trained physicians.
Brugada syndrome is a major cause of sudden cardiac death in young people and has distinctive electrocardiographic (ECG) features. We aimed to develop a deep learning–enabled ECG model for automatic screening for Brugada syndrome to identify these patients at an early point in time, thus allowing for life-saving therapy.
Methods
A total of 276 ECGs with a type 1 Brugada ECG pattern (276 type 1 Brugada ECGs and another randomly retrieved 276 non-Brugada type ECGs for 1:1 allocation) were extracted from the hospital-based ECG database for a 2-stage analysis with a deep learning model. After trained network for identifying right bundle branch block pattern, we transferred the first-stage learning to the second task to diagnose the type 1 Brugada ECG pattern. The diagnostic performance of the deep learning model was compared with that of board-certified practicing cardiologists. The model was further validated in an independent ECG data set collected from hospitals in Taiwan and Japan.
Results
The diagnoses by the deep learning model (area under the receiver operating characteristic curve [AUC] 0.96, sensitivity 88.4%, specificity 89.1%) were highly consistent with the standard diagnoses (kappa coefficient 0.78). However, the diagnoses by the cardiologists were significantly different from the standard diagnoses, with only moderate consistency (kappa coefficient 0.63). In the independent ECG cohort, the deep learning model still reached a satisfactory diagnostic performance (AUC 0.89, sensitivity 86.0%, specificity 90.0%).
Conclusions
We present the first deep learning–enabled ECG model for diagnosing Brugada syndrome, which appears to be a robust screening tool with a diagnostic potential rivalling trained physicians.
| Original language | American English |
|---|---|
| Pages (from-to) | 152-159 |
| Number of pages | 8 |
| Journal | Canadian Journal of Cardiology |
| Volume | 38 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2022 |