  In this article, we present a resource-efficient approach for
electrocardiogram (ECG) based heartbeat classification using multi-feature
fusion and bidirectional long short-term memory (Bi-LSTM). The dataset
comprises five original classes from the MIT-BIH Arrhythmia Database: Normal
(N), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB),
Premature Ventricular Contraction (PVC), and Paced Beat (PB). Preprocessing
methods including the discrete wavelet transform and dual moving average
windows are used to reduce noise and artifacts in the raw ECG signal, and
extract the main points (PQRST) of the ECG waveform. Multi-feature fusion is
achieved by utilizing time intervals and the proposed under-the-curve areas,
which are inherently robust against noise, as input features. Simulations
demonstrated that incorporating under-the-curve area features improved the
classification accuracy for the challenging RBBB and LBBB classes from 31.4\%
to 84.3\% for RBBB, and from 69.6\% to 87.0\% for LBBB. Using a Bi-LSTM
network, rather than a conventional LSTM network, resulted in higher accuracy
(33.8\% vs 21.8\%) with a 28\% reduction in required network parameters for the
RBBB class. Multiple neural network models with varying parameter sizes,
including tiny (84k), small (150k), medium (478k), and large (1.25M) models,
are developed to achieve high accuracy \textit{across all classes}, a more
crucial and challenging goal than overall classification accuracy.
