Key result
MDD-Net algorithm detects cardiovascular diseases with ~97% inter-patient classification accuracy.
Why the study?
Existing heartbeat classification methods suffer from category imbalance and rely heavily on preprocessing steps such as noise removal and R-peak location.
Does the MDD-Net system accurately classify cardiovascular diseases from raw ECG signals?
Does the MDD-Net system accurately classify cardiovascular diseases from raw ECG signals?
A novel deep learning model (MDD-Net) demonstrates high accuracy in classifying cardiovascular diseases directly from raw ECG signals, overcoming class imbalance and noise issues.
Cardiovascular disease is the leading cause of death worldwide. Immediate and accurate diagnoses of cardiovascular disease are essential for saving lives. Although most of the previously reported works have tried to classify heartbeats accurately based on the intra-patient paradigm, they suffer from category imbalance issues since abnormal heartbeats appear much less regularly than normal heartbeats. Furthermore, most existing methods rely on data preprocessing steps, such as noise removal and R-peak location. In this study, we present a robust classification system using a multilevel discrete wavelet transform densely network (MDD-Net) for the accurate detection of normal, coronary artery disease (CAD), myocardial infarction (MI) and congestive heart failure (CHF). First, the raw ECG signals from different databases are divided into same-size segments using an original adaptive sample frequency segmentation algorithm (ASFS). Then, the fusion features are extracted from the MDD-Net to achieve great classification performance. We evaluated the proposed method considering the intra-patient and inter-patient paradigms. The average accuracy, positive predictive value, sensitivity and specificity were 99.74%, 99.09%, 98.67% and 99.83%, respectively, under the intra-patient paradigm, and 96.92%, 92.17%, 89.18% and 97.77%, respectively, under the inter-patient paradigm. Moreover, the experimental results demonstrate that our model is robust to noise and class imbalance issues.
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Zhang et al. (2020) studied Cardiovascular disease (Normal, CAD, MI, CHF) (n=222). Multilevel discrete wavelet transform densely network (MDD-Net) vs. Conventional deep learning models (e.g., DenseNet, VGG-16, ResNet) was evaluated on Classification accuracy for 4-class cardiovascular disease detection. The MDD-Net algorithm achieved an average classification accuracy of 99.74% under the intra-patient paradigm and 96.92% under the inter-patient paradigm for detecting cardiovascular diseases.
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