Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020 1.0.2
(723 bytes)
E09402 12 500 5000
E09402.mat 16x1+24 1000.0(0)/mV 16 0 -136 -31882 0 I
E09402.mat 16x1+24 1000.0(0)/mV 16 0 87 -1978 0 II
E09402.mat 16x1+24 1000.0(0)/mV 16 0 224 30250 0 III
E09402.mat 16x1+24 1000.0(0)/mV 16 0 24 16511 0 aVR
E09402.mat 16x1+24 1000.0(0)/mV 16 0 -180 1644 0 aVL
E09402.mat 16x1+24 1000.0(0)/mV 16 0 156 -18718 0 aVF
E09402.mat 16x1+24 1000.0(0)/mV 16 0 -361 -8058 0 V1
E09402.mat 16x1+24 1000.0(0)/mV 16 0 29 20775 0 V2
E09402.mat 16x1+24 1000.0(0)/mV 16 0 370 8047 0 V3
E09402.mat 16x1+24 1000.0(0)/mV 16 0 107 -23522 0 V4
E09402.mat 16x1+24 1000.0(0)/mV 16 0 -263 4786 0 V5
E09402.mat 16x1+24 1000.0(0)/mV 16 0 -175 15296 0 V6
# Age: 61
# Sex: Male
# Dx: 428750005,59931005
# Rx: Unknown
# Hx: Unknown