基於多重神經網路之輔助心電圖分類

Auxiliary ECG Classification Based on Multiple Neural Networks

婁濟和(CHI-HO LOU)

指導教授 ： 湯松年

中原大學/電機資訊學院/資訊工程學系/碩士(2022年)

https://doi.org/10.6840/cycu202201675

摘要

心血管疾病(Cardiovascular diseases, CVDs)在人類十大死因中比例最高，為整體 16%。從 2000 年至 2019 因缺鐵性心臟病致死的人數增加 200萬人。然而一般心血管疾病不容易診斷，需使用心電圖(ECG)儀器進行時間不短的檢測。為了協助醫師能高效判別心電圖病徵，供醫師們參考的高準確度自動判別病徵數據，提出使用一維卷積神經網路(1D-CNN)、二維卷積神經網路(2D-CNN)、長短記憶神經網路(LSTM)三個神經網路模型，以整體學習(Ensemble Learning)合作判斷病徵，提升平均準確判別。。以及提出使用一維資料擴增處理、一維資料轉二維資料二值化轉換處理，彌補心電圖標籤數量不足以及優化二維資料處理速度。經實驗數據表現，在 80%訓練 20%預測資料使用方式下， 1D-CNN 為 99.2%、2D-CNN 為98.6%、LSTM 為 99.1%，整體合作判別為99.5%。經 AAMI 協會建議的標準下的數據表現，判別 VEB 病徵的準確率Acc為 99.3%、敏感度Sen為 98.7%、特異度Spe為 99.4%以及陽性預測率Ppr為 95.1%。判別 SVEB 病徵的準確率Acc為98.4%、敏感度Sen為 97.1%、特異度Spe為 98.5%以及陽性預測率Ppr為73.1%。本研究數據與其他文獻相較，顯示出以整體學習合作判斷，具有良好的成效。

關鍵字

一維卷積神經網路 ； 二維卷積神經網路 ； 長短記憶神經網路 ； 整體學習 ； 一維資料擴增 ； 一維資料轉二維資料處理

並列摘要

Cardiovascular diseases (CVDs) have the highest proportion of the top ten causes of death in humans, at 16% overall. From 2000 to 2019 the number of deaths from iron deficiency heart disease increased by 2 million. However, cardiovascular disease in general is not easy to diagnose, and electrocardiogram (ECG) instruments are required for detection for a long time. In order to help physicians efficiently determine ECG symptoms, high-accuracy automatic identification of symptom data for the reference of physicians, it is proposed to use one-dimensional convolutional neural network (1D-CNN), two-dimensional convolutional neural network (2D-CNN), Three neural network models of long and short memory neural networks (LSTM) cooperate to determine the symptoms by ensemble Learning, and improve the average accuracy of the judgment. In addition, it is proposed to use one-dimensional augmentation data processing and one-dimensional data to two-dimensional data binarization conversion processing to make up for the insufficient number of ECG labels and optimize the processing speed of two-dimensional data. According to experimental data, In 80% training 20% predictive data usage, 1DCNN is 99.2% and 2D-CNN is 98.6%. The LSTM is 99.1%, and the overall cooperation judgment is 99.5%. According to the criteria recommended by the AAMI Association, for symptoms VEB, Acc(Accuracy) 99.3%, Sen(Sensitivity) 98.7%, Spe(Specificity) 99.4% and Ppr(Positive predictive rate) 95.1%, respectively. For symptoms SVEB Acc 98.4%, Sen 97.1%, Spe 98.5% and Ppr 73.1%, respectively. Compared with other literature, the data in this study showed good results based on cooperation with Ensemble Learning.

並列關鍵字

1D-CNN ； 2D-CNN ； LSTM ； Ensemble Learning ； One-dimensional data augmentation ； One-dimensional data to two-dimensional data processing

參考文獻

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