Table 2.

Performance characteristics of 3 models in the cardioversion patient test set

Algorithm type	AUC	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)
Conventional heart rate variability∗ (model 1)	0.717	74.1	58.4	80.8	48.8
Machine learning fed heart rate only data† (model 2)	0.954	81.0	92.1	96.0	67.1
Machine learning fed raw waveform data‡ (model 3)	0.983	98.5	88.0	95.1	96.2

Model evaluation indices are given for each of the 3 models applied to the test set of patients undergoing cardioversion.

AUC = area under the receiver operating characteristic curve; NPV = negative predictive value; PPV = positive predictive value.

^∗Using the root mean square of the successive interval differences.

^†Using a long short-term memory algorithm.

^‡Using a deep convolutional-recurrent neural network algorithm.