Key result
The proposed GFE-FINN model achieved a correct recognition rate of 87.95% in classifying 12-lead ECG signals for difficult heart diseases, outperforming comparison deep learning models.
The proposed GFE-FINN machine learning model achieved an 87.95% correct recognition rate in classifying 12-lead ECG signals for difficult heart disease diagnosis.
May enhance multi-source signal classification; leaves open clinical validation in cardiovascular monitoring.
A fuzzy radial basis inference network with grouped signal feature embedding (GFE-FINN) classification model is proposed for multi-source time-varying signal fusion analysis and feature knowledge embedding, which multi-channel signals are divided into several groups according to the sources, attribute, features and sensitivity of signals. Each pattern class of the grouped signal sample set is divided into several pattern subclasses which are more similar features according to the grouping index, and typical feature samples are extracted to implicitly express the category features knowledge of the grouped signal. A fuzzy radial basis process neuron (FRBPN) is defined, which is used as parametric membership functions, and the typical feature signal samples of the grouped pattern subclass are used as the kernel centers of FRBPN to realize the embedding of the diverse feature knowledge. Through the kernel transformation in FRBPN, the input signals of each group are fuzzified respectively. Fuzzy multiplication operation is used to realize the information synthesis based on the membership degree of grouped pattern subclasses and establish fuzzy reasoning and classification rules. The proposed method can realize the feature fusion based on fuzzy membership degree and the semantic representation based on fuzzy rules hierarchically. Through the learning of the sample set, fuzzy membership function, reasoning and classification rules are established adaptively. A comprehensive learning algorithm was given. An experiment was conducted using 4-groups 12-lead long ECG signals in diagnosis of difficult heart disease. The correct recognition rate reaches 87.95%, and the performance evaluation index and generalization ability are significantly improved.
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Zha et al. (2021) studied Heart disease (ECG classification) (n=2,269). GFE-FINN (Fuzzy Radial Basis Inference Network With Grouped Signal Feature Embedding) vs. MC-DCNN, LSTM+RF, and GRU-RNN models was evaluated on Classification accuracy. The proposed GFE-FINN model achieved a correct recognition rate of 87.95% in classifying 12-lead ECG signals for difficult heart diseases, outperforming comparison deep learning models.
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