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Table 1 Summary of related works on sleep stage classifications using ECG

From: Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

Algorithm [Ref.]

Dataset

Number of classes

Methods

Accuracy

SVM [20]

MITBPD

2

HRV, DFA, windowed DFA

79.31 ± 4.52% for 12 features

79.99 ± 4.64% for 10 features

RF [21]

SHRSV

3

HRV

88.67% for subject specific classifier

72.58% for subject independent classifier

ELM [22]

MITBPD

2

HRV and DFA

78.33% for both HRV and DFA; 76.29% for HRV; 73.48% for DFA

BPNN [22]

MITBPD

2

HRV and DFA

76.74% for both HRV and DFA; 73.81% for HRV; 71.07% for DFA

SVM [22]

MITBPD

2

HRV and DFA

78.12% for both HRV and DFA; 76.03% for HRV; 73.21% for DFA

SVM with ECOC extension and RBF [25]

MITBPD

2 and 3

HRV and EDR

81.76% for 2 classes

76% for 3 classes

LSTM network [23]

Siesta

4

HRV

77.00 ± 8.90%

Combination of SVM and PSO [24]

MITBPD

2

HRV

78.41% for RBF kernel

77.08% for linear kernel

Morphological [29]

MITBPD

3

HRV

77.02%

Deep Neural Network [30]

MITBPD

3

HRV

77%

Combination of ELM and PSO (Proposed Method)

MITBPD

2

HRV

81.96% for 2 classes

76.59% for 3 classes

71.44% for 4 classes

63.18% for 6 classes