Table 2.

Accuracy of Classification Models for Regions of Interest in AF

Classification	Kappa†	Sensitivity	Specificity	PPV	NPV	Accuracy
Method		%	%	%	%	%
Supervised Convolutional Neural Network
CNN,	0.982	99.6	98.5	98.7	99.5	99.1
Train		(99.5–99.7)	(98.4–98.6)	(98.6–98.8)	(99.5–99.6)	(99.0–99.2)
CNN,	0.966	96.7	99.4	98.8	98.4	98.5
Validation		(96.3–97.1)	(99.3–99.5)	(98.5–99.0)	(98.2–98.6)	(98.3–98.7)
CNN,	0.900	97.0	93.0	93.1	97	95
Test		(96.8–97.1)	(92.7–93.3)	(92.7–93.4)	(96.8–97.2)	(94.8–95.2)
Unsupervised Cluster Analysis
k-means, k=2	0.530	69.1	92.0	94.9	58.3	76.4
All data		(68.8–69.3)	(91.8–92.3)	(94.7–95.0)	(58.0–58.6)	(76.2–76.6)
k-means, k=12	0.589	77.0	82.3	83.5	75.5	79.4
All data		(76.7–77.2)	(82.0–82.6)	(83.2–83.7)	(75.2–75.8)	(79.3–79.6)
Supervised Classical Machine Learning Methods
LDA,	0.617	85.8	76	77.9	84.4	80.9
Train		(85.0–86.1)	(75.6–76.3)	(77.6–78.2)	(84.1–84.7)	(80.6–81.1)
LDA,	0.595	85.0	74.6	76.4	83.7	79.7
Test		(84.6–85.5)	(74.1–75.1)	(75.9–76.9)	(83.3–84.2)	(79.4–80.1)
k-NN, k=109	0.647	77.0	88.2	87.9	77.4	82.3
Train		(76.7–77.4)	(87.9–88.4)	(87.6–88.2)	(77.0–77.7)	(82.0–82.5)
k-NN, k=109	0.615	83.4	86	94.9	62.4	84
Validation		(82.8–83.9)	(85.1–86.9)	(94.6–95.2)	(61.3–63.4)	(83.5–84.5)
k-NN, k=109	0.576	75.3	84.0	87.1	70.3	78.9
Test		(74.8–75.7)	(83.5–84.5)	(86.7–87.5)	(69.8–70.9)	(78.5–79.2)
SVM,	0.601	85.8	74.3	79.4	81.5	80.3
Train		(85.1–85.8)	(73.9–74.7)	(79.1–79.7)	(81.1–81.9)	(80.0–80.5)
SVM,	0.638	74.7	88.7	76.8	87.5	84
Validation		(73.8–75.7)	(88.2–89.1)	(75.9–77.7)	(86.9–87.9)	(83.5–84.4)
SVM,	0.595	82.9	76.7	77.4	82.3	79.7
Test		(82.4–83.4)	(76.1–77.2)	(76.9–77.9)	(81.8–82.8)	(79.4–80.1)

^*Values are percent (95% confidence intervals)

^†p-value for all <0.001