Ensembled Data Augmentation Model for Simplified Cardiac Arrhythmia Detection Under Limited Minority Class Data

Automatic cardiac arrhythmia detection (CAD) from electrocardiogram (ECG) is a challenging topic for biomedical research community. In recent times, machine learning (ML) and deep learning (DL) models have been widely adopted to achieve good prediction accuracy. However, the primary obstacles toward their standalone implementation often have high complexity and memory requirement, preventing low resource embedding. The other one is low prediction accuracy toward minority classes. This work presents a new data augmentation model, a combination of borderline majority class undersampling (BMU) and chaos-based minority class oversampling (CMO). In BMU, the undersampling was done with priority to the majority class instances which are far away from the boundary. In CMO, the beat synthesis was done by adaptive chaos addition to the parent beats of minority classes so that they remain clinically acceptable. A simple classification flow using an autoencoder with random forest (RF) was used with the synthesized data for multiclass CAD. For performance evaluation three benchmark datasets, viz., MITDB, INCART, and SVDB datasets were used. It was found that the$F1$score of the minority classes have increased, with reduction in false positive and negative rates as compared to few published works employing complex architectures. Also, the simple CAD model could achieve equally good accuracy with much lower (70%) latency, and lower (90%) memory requirement than few reported works using MITDB dataset in a standalone hardware implementation (ARM v6 controller). This research shows great promise toward developing low-complexity wearable CAD monitors.