Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks
This work addresses a critical medical diagnosis task for healthcare, but it is incremental as it applies a known CNN architecture to a specific ECG classification problem with improved metrics over benchmarks.
This paper tackled the problem of detecting inferior myocardial infarction (IMI) from ECG signals using a shallow convolutional neural network, achieving accuracy of 84.54%, sensitivity of 85.33%, and specificity of 84.09% in a subject-oriented evaluation on the PTB database.
Myocardial Infarction is one of the leading causes of death worldwide. This paper presents a Convolutional Neural Network (CNN) architecture which takes raw Electrocardiography (ECG) signal from lead II, III and AVF and differentiates between inferior myocardial infarction (IMI) and healthy signals. The performance of the model is evaluated on IMI and healthy signals obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A subject-oriented approach is taken to comprehend the generalization capability of the model and compared with the current state of the art. In a subject-oriented approach, the network is tested on one patient and trained on rest of the patients. Our model achieved a superior metrics scores (accuracy= 84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the benchmark. We also analyzed the discriminating strength of the features extracted by the convolutional layers by means of geometric separability index and euclidean distance and compared it with the benchmark model.