Open AccessHeartNetEC: a deep representation learning approach for ECG beat classification
Author(s) -
Sri Aditya Deevi,
Christina Perinbam Kaniraja,
Vani Devi Mani,
Deepak Mishra,
Shaik Ummar,
Cejoy Satheesh
Publication year - 2021
Publication title -
biomedical engineering letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.709
H-Index - 26
eISSN - 2093-985X
pISSN - 2093-9868
DOI - 10.1007/s13534-021-00184-x
Subject(s) - beat (acoustics) , categorization , artificial intelligence , pattern recognition (psychology) , computer science , block (permutation group theory) , electrocardiography , deep learning , noise reduction , cardiac arrhythmia , speech recognition , machine learning , cardiology , medicine , mathematics , atrial fibrillation , physics , geometry , acoustics
One of the most crucial and informative tools available at the disposal of a Cardiologist for examining the condition of a patient's cardiovascular system is the electrocardiogram (ECG/EKG). A major reason behind the need for accurate reconstruction of ECG comes from the fact that the shape of ECG tracing is very crucial for determining the health condition of an individual. Whether the patient is prone to or diagnosed with cardiovascular diseases (CVDs), this information can be gathered through examination of ECG signal. Among various other methods, one of the most helpful methods in identifying cardiac abnormalities is a beat-wise categorization of a patient's ECG record. In this work, a highly efficient deep representation learning approach for ECG beat classification is proposed, which can significantly reduce the burden and time spent by a Cardiologist for ECG Analysis. This work consists of two sub-systems: denoising block and beat classification block. The initial block is a denoising block that acquires the ECG signal from the patient and denoises that. The next stage is the beat classification part. This processes the input ECG signal for finding out the different classes of beats in the ECG through an efficient algorithm. In both stages, deep learning-based methods have been employed for the purpose. Our proposed approach has been tested on PhysioNet's MIT-BIH Arrhythmia Database, for beat-wise classification into ten important types of heartbeats. As per the results obtained, the proposed approach is capable of making meaningful predictions and gives superior results on relevant metrics.