Patient-Specific Deep Architectural Model for ECG Classification

Table 4

Classification metrics compared to the state-of-the-art SVEB and VEB classification (percentage, %).

Methods	SVEB				VEB
	ACC	SE	PPV	SP	ACC	SE	PPV	SP
Proposed (a)	98.8	71.4	94.4	99.8	99.1	93.3	93.3	99.5
Kiranyaz et al. [15]	96.4	64.6	62.1	98.6	98.6	95	89.5	98.1
Chazal and Reilly [11]	95.9	87.7	47.0	96.2	99.4	94.3	96.2	99.7
Jiang and Kong [8]	96.6	50.6	68.0	98.8	97.7	86.6	89.4	98.9
Ince et al. [7]	97.3	63.5	53.7	98.3	98.0	84.6	86.7	99.0
Proposed (b)	96.2	15.4	47.3	99.3	95.5	60.4	66.8	97.9
Mar et al. [12]	93.3	83.2	33.5	93.7	97.4	86.8	75.9	98.1
Alvarado et al. [10]	97.0	86.2	56.7	97.5	99.1	92.4	93.4	99.5
Ye et al. [9]	97.4	56.4	55.1	98.6	94.6	84.7	59.5	95.4
Zhang et al. [13]	93.3	79.1	36.0	93.9	98.6	85.5	92.7	99.5

Patient-specific method: require expert intervention.
(a) indicates the patient-specific heartbeat classification scenario. Classifiers were trained by using the first 300 beats of individual patient.
(b) indicates the interpatient heartbeat classification scenario.