Table 10 Others
Publication year | Author | Publication title | Population/dataset | Purpose of study | Sensors | Framework | Diseases | Performance |
|---|---|---|---|---|---|---|---|---|
2019 [217] | Fan et al. | BMC Medical Informatics and Decision Making | Elderly nursing home | Two DL models predict the health status of older adults one day in the future using a single-lead ECG | TeleMedCare (single lead) | LSTM,BiLSTM | health status | BiLSTM/LSTM: AUROC = 0.9312/0.9065 |
2019 [219] | Attia et al. | Circulation: Arrhythmia and Electrophysiology | Mayo Clinic digital data | A CNN-based model for gender and ECG-age diagnosis via 12-lead ECG | 12 leads ECG、CXR | CNN | Gender、ECG-age | ECG-age:MAE = 6.9 ± 5.6 year ECG-Gender:AUC = 0.97,Acc = 90.4% |
2021 [225] | Mori et al. | Pediatric Cardiology | Proprietary DB | A CNN-LSTM model diagnoses ASD with a 12-lead ECG | 12 leads ECG | CNN-LSTM | ASD | AUC = 0.95 Acc = 0.89 |
2021 [221] | Lima et al. | Nature Communications | CODE、ELSA-Brasil DB、SaMi-Trop DB | A DNN-based model diagnoses ECG-age with a 12-lead ECG | 12 leads ECG、CXR | DNN | ECG-age | CODE/ELSA-Brasil /SaMi-Trop MAE = 8.38/8.44/10.04 SD = 7.00/6.19/7.76 |
2021 [218] | Butt et al. | Information | Proprietary DB | The CNN-based model identifies falls and activity classifications by ECG | ECG | CNN | Fall、Daily activities | Acc = 98.02% |
2022 [226] | Lou et al. | Journal of Personalized Medicine | Proprietary DB | A CNN-based model was developed to diagnose LAE with a 12-lead ECG | 12 leads ECG | CNN | LAE | AUC of Internal/external validation sets Moderate LAE = 0.8587/0.8688 Severe LAE = 0.8899/0.8990 |
2022 [227] | Liu et al. | Canadian Journal of Cardiology | Proprietary DB | The DL model was used to diagnose aortic dissection by 12-lead ECG and CXR | 12 leads ECG、CXR | CNN,DenseNet | AD | AUC = 0.943 |
2022 [228] | Liu et al. | Journal of Personalized Medicine | Proprietary DB | A DL model for diagnosis of acute pericarditis by 12-lead ECG | 12 leads ECG、CXR | CNN | Acute pericarditis | AUC = 0.954 |
2022 [223] | Chang et al. | Frontiers in Cardiovascular Medicine | Proprietary DB,SaMi-Trop CODE-15% | An MXNet-based model predicts the relationship between human biological age and morbidity and mortality from ECG | 12 leads | MXNet | Heart Age、Prognosis | All-cause mortality HR = 1.61 CV-cause mortality HR = 3.49 |
2023 [222] | Zhang et al. | Frontiers in Cardiovascular Medicine | UK Biobank DB | A CNN-based model is used to diagnose ECG age with a 12-lead ECG | 12 leads ECG、CXR | DNN | ECG age | MAE = 9.1 ± 6.6 |
2023 [229] | Gomes et al. | Medical & Biological Engineering & Computing | Proprietary DB | The DL model uses a 12-lead ECG to determine the effects of COVID-19 on the heart in the ECG | 12 leads ECG | VGG 16 | COVID-19 | AUC = 1 Acc = 0.94 |
2023 [230] | Hassan et al. | Signal, Image and Video Processing | Proprietary DB | The DL model diagnoses Covid-19 in patients with heart disease by 12-lead ECG | 12 leads ECG、CXR | VGG-19,AlexNet,ResNet-101 | COVID-19 | Acc = 99.1% |
2023 [224] | Iakunchykova et al. | European Journal of Neurology | Tromsø 7 | A CNN-based model for the diagnosis of heart-related age via 12-lead ECG | 12 leads ECG、CXR | CNN | HDA | MSD = − 4.63 |
2023 [231] | Gupta et al. | IEEE Transactions on Instrumentation and Measurement | f-ECG ARRDB | A DCM-based model identifies fetal arrhythmia by fetal electrocardiogram | Fetal-electrocardiogram | DCM | Fetal arrhythmia | AUC = 99.57% Acc = 94.18% F1 = 94.90% |
2023 [220] | Baek et al. | Frontiers in Cardiovascular Medicine | Proprietary DB | A BI-LSTM-based model estimates biological heart age by 12-lead ECG | 12 leads ECG | Bi-LSTM | Biological heart age | MAE = 5.8 ± 3.9 |
2023 [172] | Zvuloni et al. | IEEE Transactions on Biomedical Engineering | TNMG DB | A DNN-based model for arrhythmia diagnosis, atrial fibrillation risk prediction, and age estimation via ECG | 12 leads ECG | DNN | Arrhythmia diagnosis, AF risk prediction, age estimation | MAE = 6.26 ± 5.35 |