Real-Time Arrhythmia Classification Algorithm Using Time-Domain ECG Feature Based on FFNN and CNN

Figure 3

Schematic diagram of generating the ECG_200 ms_POS_72 and ECG_RRR_TYPE_360 datasets from the MIT-BIH database. The ECG_200 ms_POS_72 dataset is used to train Classifier 1 for QRS complex position prediction. Each of these samples contains an ECG segment (ECG_200 ms) and the corresponding QRS complex position label. The ECG_200 ms is a 200 ms ECG-MLII lead ECG segment with a length of 72 (when the sampling rate is 360 Hz) in mV. The label p is the center position of the QRS complex (an integer in [0, 71], where 0 means no QRS complex). The ECG_RRR_TYPE_360 dataset is used to train Classifier 2 for arrhythmia classification. Each of these samples contains an ECG_RRR feature and a corresponding arrhythmia type (TYPE). An ECG_RRR ECG segment is the MLII lead ECG data between every three QRS complexes resampled to a fixed length of 360 in mV. The data label TYPE is the arrhythmia type corresponding to the middle QRS complex (an integer in [0, 12], which represents 13 classes of arrhythmia).