Key result
IMNN algorithm achieves ~99% ECG classification accuracy in congenital heart disease, improving sensitivity over MNN.
Why the study?
The effect of different neural network algorithms on ECG classification of congenital heart disease patients was explored to improve diagnostic accuracy.
Does an improved multimodal neural network (IMNN) algorithm improve ECG classification accuracy and sensitivity compared to CNN, RNN, and standard MNN algorithms in patients with congenital heart disease?
Does an improved multimodal neural network (IMNN) algorithm improve ECG classification accuracy and sensitivity compared to CNN, RNN, and standard MNN algorithms in patients with congenital heart disease?
An improved multimodal neural network algorithm demonstrates high accuracy (>98.5%) and sensitivity for classifying ECG abnormalities in patients with congenital heart disease.
Answers are grounded in this paper and cite their sources.
May support multimodal AI for CHD ECG classification; leaves open prospective validation before clinical adoption.
The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted between August 2016 and August 2019 were selected for analysis to compare the classification effect and accuracy rate of IMNN, MNN, CNN ECG, and RNN ECG algorithms. It was found that the RNN ECG algorithm had higher classification sensitivity and true positive rate in terms of normal or bundle (NB) branch block beat, supraventricular abnormal (SA) rhythm, abnormal ventricular (AV) beat, and fusion beat (FB) than the CNN ECG algorithm ( P < 0.05 ), and the classification sensitivity and true positive rate of IMNN algorithm in the four aspects were significantly higher than those of MNN algorithm ( P < 0.05 ). The classification accuracy of CNN ECG algorithm and RNN ECG algorithm was above 98%, while that of MNN algorithm and IMNN algorithm was better than that of CNN ECG algorithm and RNN ECG algorithm, and the accuracy rate can reach 98.5% or more. Moreover, the accuracy rate of the IMNN algorithm can reach more than 98%. In conclusion, IMNN not only has a good classification ability in the simulated environment but also performs well in the actual environment, which is worthy of clinical promotion.
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Yuan et al. (2021) studied Congenital heart disease (CHD). Improved multimodal neural network (IMNN) ECG algorithm vs. MNN, CNN, and RNN ECG algorithms was evaluated on Classification accuracy and sensitivity (p=< 0.05). The improved multimodal neural network (IMNN) algorithm achieved an ECG classification accuracy of >98.5% in congenital heart disease patients, with higher sensitivity than the MNN algorithm.
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