Skip to main content

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