Table 2. Overview of ML algorithms used to assess ECG analysis.

Authors	ML algorithm	Aim	Performance
Isin, et al.[30]	Deep neural network	To detect automatically arrhythmia on ECG	Correct recognition rate: 98.5%
Isin, et al.[30]	Deep neural network	To detect automatically arrhythmia on ECG	Accuracy: 92%
Attia, et al.[31]	Convolutional neural network	To identify asymptomatic left ventricular systolic dysfunction	AUC: 0.93
			Sensitivity: 86.3%
			Specificity: 85.7%
			Accuracy: 85.7%
Galloway, et al.[32]	Convolutional neural network	Screening of hyperkalemia in patients with chronic kidney disease	AUC: 0.853–0.883

AUC: area under the curve; ECG: electrocardiography; ML: machine learning.