Key result
A multi-scale ResNet-34 model classified congestive heart failure severity from ECG segments with an average accuracy of 93.60% for two-second segments and 94.29% for five-second segments.
Why the study?
ECG signals help assess CHF severity, but subtle perturbations make it difficult for doctors to detect differences.
Does a multi-scale residual network accurately classify congestive heart failure severity into NYHA classes using ECG segments?
Does a multi-scale residual network accurately classify congestive heart failure severity into NYHA classes using ECG segments?
A novel multi-scale residual network can accurately classify congestive heart failure severity into NYHA classes using short ECG segments, potentially serving as an auxiliary diagnostic tool.
May support ECG-based AI for exploratory HF severity assessment; leaves open prospective validation before clinical use.
Congestive heart failure (CHF) poses a serious threat to human health. Once the diagnosis of CHF is established, clinical experts need to assess the severity of CHF in a timely manner. It is proved that electrocardiogram (ECG) signals are useful for assessing the severity of CHF. However, since the ECG perturbations are subtle, it is difficult for doctors to detect the differences of ECGs. In order to help doctors to make an accurate diagnosis, we proposed a novel multi-scale residual network (ResNet) to automatically classify CHF into four classifications according to the New York Heart Association (NYHA) functional classification system. Furthermore, in order to make the reported results more realistic, we used an inter-patient paradigm to divide the dataset, and segmented the ECG signals into two different intervals. The experimental results show that the proposed multi-scale ResNet-34 has achieved an average positive predictive value, sensitivity and accuracy of 93.49%, 93.44% and 93.60% respectively for two seconds of ECG segments. We have also obtained an average positive predictive value, sensitivity and accuracy of 94.16%, 93.79% and 94.29% respectively for five seconds of ECG segments. The proposed method can be used as an auxiliary tool to help doctors to classify CHF.
No takes yet. Share an insight, caveat, or question.
Li et al. (2020) studied Congestive heart failure (CHF). Multi-scale residual network (ResNet-34) was evaluated on Classification of CHF into four NYHA functional classifications. A multi-scale ResNet-34 model classified congestive heart failure severity from ECG segments with an average accuracy of 93.60% for two-second segments and 94.29% for five-second segments.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: