Key result
The Inception-ResNet-v2 deep learning model achieved an accuracy of 0.97 in classifying 27 heart abnormalities using 12-lead ECG signals, outperforming other evaluated methods.
Why the study?
Manual 12-lead ECG interpretation requires expertise and time, creating interest in computer-aided diagnosis to categorize ECG abnormalities.
Does an Inception-ResNet-v2 based deep learning model accurately classify 27 heart abnormalities using 12-lead ECG signals?
Does an Inception-ResNet-v2 based deep learning model accurately classify 27 heart abnormalities using 12-lead ECG signals?
Absolute Event Rate: 0.97% vs 0.96%
A novel end-to-end deep learning model based on Inception-ResNet-v2 can accurately classify 27 different heart abnormalities from 12-lead ECG signals with 97% accuracy.
Supports automated multi-abnormality ECG analysis; extends deep learning methods but leaves clinical adoption open pending validation.
An electrocardiogram (ECG) machine with a standard 12-lead configuration is the primary clinical technique for diagnosing abnormalities in heart function. Automated 12-lead ECG machines have the capacity to screen the general population and provide second opinions for physicians. However, expertise and time are required for manual ECG interpretation. Therefore, computer-aided diagnoses are of interest to the medical community. Hence, this study aims to build a deep learning (DL) model with an end-to-end structure that can categorize 12-lead ECG results into 27 different disorders. We use multivariate time-series data to construct a novel end-to-end DL model (based on combined convolutional neural networks (CNNs), long short-term memory, gated recurrent units, and a deep residual network structure) for feature representations and determining spatial relations among deep features. In addition, a dataset of 43,101 classified standard ECG recordings was collected from six different sources to guarantee the model’s ability to generalize and alleviate data divergence. As a result, the residual network-based model obtained promising outcomes and an accuracy of 0.97. According to the experimental data, it outperforms other methods.
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Rawi et al. (2023) studied Heart abnormalities (ECG classification) (n=43,101). Inception-ResNet-v2 deep learning model vs. Other deep learning models (LSTM, CNN-LSTM, LSTM-GRU, CNN-GRU) was evaluated on Classification accuracy. The Inception-ResNet-v2 deep learning model achieved an accuracy of 0.97 in classifying 27 heart abnormalities using 12-lead ECG signals, outperforming other evaluated methods.
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