Multibranch 1D CNN for Detection and Localization of Myocardial Infarction from 12 Lead Electrocardiogram Signal

Choudhary, Pharvesh Salman; Dandapat, Samarendra

doi:10.1109/indicon52576.2021.9691576

Cited by 2 publications

(2 citation statements)

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“…The data were denoised using Discrete Wavelet Transform (DWT) and then fed to three CNN layers. A study by [14] also uses the PTB dataset with 12 leads and is classified into MI and healthy control classes. The ECG data were denoised using a moving wavelet filter, higher-order statistics, and morphological filtering to attend to different types of noises.…”

Section: Related Workmentioning

confidence: 99%

“…This reduces the error and the time consumed when performed manually [12]. Several researchers have used a convolutional neural network (CNN) that automatically extracts spatial features [13][14][15][16][17] and long short-term memory (LSTM) that extracts temporal features [18,19]. In addition to that, there is also research that uses a hybrid of CNN and LSTM [20][21][22] to utilize the benefits of both features.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Myocardial Infarction Using Hybrid Models of Convolutional Neural Network and Recurrent Neural Network

2023

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Myocardial Infarction (MI) is the death of the heart muscle caused by lack of oxygenated blood flow to the heart muscle. It has been the main cause of death worldwide. The fastest way to detect MI is by using an electrocardiogram (ECG) device, which generates graphs of heartbeats morphology over a certain period of time. Patients with MI need fast intervention as delay will lead to worsening heart conditions or failure. To improve MI diagnosis, much research has been carried out to come up with a fast and reliable system to aid automatic MI detection and prediction from ECG readings. Recurrent Neural Network (RNN) with memory has produced more accurate results in predicting time series problems. Convolutional neural networks have also shown good results in terms of solving prediction problems. However, CNN models do not have the capability of remembering temporal information. This research proposes hybrid models of CNN and RNN techniques to predict MI. Specifically, CNN-LSTM and CNN-BILSTM models have been developed. The PTB XL dataset is used to train the models. The models predict ECG input as representing MI symptoms, healthy heart conditions or other cardiovascular diseases. Deep learning models offer automatic feature extraction, and our models take advantage of automatic feature extraction. The other superior models used their own feature extraction algorithm. This research proposed a straightforward architecture that depends mostly on the capability of the deep learning model to learn the data. Performance evaluation of the models shows overall accuracy of 89% for CNN LSTM and 91% for the CNN BILSTM model.

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Section: Related Workmentioning

confidence: 99%