基于PSOFS和TSK模糊系统的不平衡心电数据分类算法

doi:10.11959/j.issn.2096-0271.2022039

Abstract

Abstract:

A new classification model of electrocardiogram (ECG) signal based on particle swarm optimization feature selection (PSOFS) and TSK (Takagi-Sugeno-Kang) fuzzy system was proposed, i.e., parallel ensemble fuzzy neural network based on PSOFS and TSK (PE-PT-FN), which was used for ECG prediction.Each class sample in the training set was randomly sampled, and the samples obtained by randomly sampled were added.Then, the feature selection method PSOFS was carried out independently and parallelly.In PSOFS, particles that were random initial positions represent different feature subsets and converge to the optimal positions after many iterations.Each subset had a corresponding feature subset.Several groups of TSK fuzzy neural network (TSK-FNN) were trained by each feature subset in parallel.Medical researchers could effectively find the correlation between ECG signal data and different types of disease through the interpretability of the fuzzy system and the feature subsets by the PSOFS algorithm.Experiments prove that PE-PT-FN greatly improves the macro-R to 92.35% while retaining interpretability.

Key words: TSK fuzzy neural network, particle swarm optimization feature selection, ensemble learning, classification of ECG signal, imbalance data

CLC Number:

TP301

Xinhui LI, Qing SHEN, Xiongtao ZHANG. Classification algorithm for imbalance data of ECG based on PSOFS and TSK fuzzy system[J]. Big Data Research, 2022, 8(5): 139-152.

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模型	r	C	δ	penalty parameter	hidden layer
PE-PT-FN	50	60	2.3	—	—
RBF	—	—	3.5	—	—
ELM	—	—	—	—	1 000
ANFIS	—	—	—	—	—
GFRNN	—	—	—	—	—
SMOTE+SVM	—	—	1	1 000	—

模型	macro-P	macro-R	macro-F1
PE-PT-FN	0.877805
RBF	0.704845	0.685438	0.695006
ELM		0.860886	0.781939
ANFIS	0.481846	0.435967	0.45776
GFRNN	0.519663	0.852065	0.586005
SMOTE+SVM	0.848847	0.894739	0.871189

Classification algorithm for imbalance data of ECG based on PSOFS and TSK fuzzy system

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