Table 5.

Comparison results of different research work on the CCDD.

Literature	ECG categories	Classifier	Performance
[32]	2	CBRNN	Spe = 76.32% Se = 75.52% Acc = 87.69%
[44]	2	Ensemble deep learning	Spe = 86.86 ± 3.51% Se = 80.23 ± 4.49% Acc = 84.84 ± 1.82%
[45]	2	LCNN	Spe = 83.84% Se = 83.43% Acc = 83.66%
[46]	2	Heart rate and LCNN fuse	Spe = 84.45% Se = 85.19% Acc = 84.77%
[47]	2	ResNet50	Spe = 91.63% Se = 87.73% Acc = 89.43%
[24]	7 multilabel	Ensemble multilabel classification model	Se (Rec) = 71.6% Acc = 75.2% Pre = 80.8% F1 = 75.2%
This work	9 multilabel	CSA-MResNet	Spe = 98.7% Se (Rec) = 85.9% Acc = 97.1% Pre = 90.6% F1 = 88.2%