Arrhythmia Classification Based on Bi-Directional Long Short-Term Memory and Multi-Task Group Method

Introduction

Cardiovascular disease (CVD) can result in arrhythmia and main causes of electrical impulses abnormalities in conductive process and abnormal heart rhythms. Many methods were used for early monitoring abnormal heart rhythms in arrhythmia that have an ideal effect. Partial arrhythmias are complicated causes that occurs suddenly and sometimes lead to morality (Wang et al., 2020). To detect human heart abnormalities, ECG signals act as a non-invasive clinical tool and patient’s heart function of detailed information is provided by ECG signal (Jha & Kolekar, 2020). Heart disease is life threatening and early diagnosis of the disease helps to provide necessary treatment to save lives. The arrhythmia automated diagnosis is based on ECG frequency content and morphological patterns (Qaisar et al., 2021). Cardiac Arrhythmias are one of the CVDs that conquered major in these deaths. ‘Arrhythmia’ is a heart rate disturbance that is caused by improper electrical conduction or formation in the heart (Ramesh et al., 2021). Recent advancements in Machine learning in bioinformatics and biomedicine have received considerable attention. Various studies of the robust automatic algorithm were presented for the identification and classification of arrhythmia (Yang et al., 2020).

Recently, end-to-end deep Neural Networks such as CNN and Recurrent Neural Networks (RNN) were widely applied for classification and automated feature selection of ECG signals (Lee & Shin, 2021). Features extracted from signals have morphological features or characteristics of ECG, vector cardiogram (VCG), wavelets coefficients, hermite polynomials, Independent Component Analysis (ICA), and heartbeat intervals. The existing features use the intervals between morphology and beats are not sufficient to distinguish beat types with high precision (Atal & Singh, 2020; Gajowniczek et al., 2020). CNN plays a significant role in the medical imaging interpretation for the classification of disease. Some research involves applying morphological analysis in physiological signals to a CNN to improve its ability to capture shift invariant mode and position (Chen et al., 2020; Ihsanto et al., 2020). The objectives and contributions of the MTGBi-LSTM are discussed as below

• 1.

  The MTGBi-LSTM model learns the ECG signal features in shared environment that helps to learn the unique features of the signal and overcome the imbalance dataset problem. The global and intra LSTM model selects the relevant features from input signal and escapes from local optima.

• 2.

  Exploration is handled by global Bi-LSTM and exploitation is handled by intra BiLSTM model to escape from local optima. The MTGBi-LSTM model is evaluated using MIT-BIH dataset and compared with existing methods for arrhythmia classification.

• 3.

  The MTGBi-LSTM model has 99.2% accuracy and existing CNN model has 95.1% accuracy in arrhythmia classification. The MTGBi-LSTM model has higher performance due to its advantage of unique feature selection in learn together of ECG signal and escape from local optima in feature selection.

This paper is formulated as follows: recent methods in arrhythmia classification is in section 2, the MTGBi-LSTM method explanation is in section 3, implementation details is in section 4, results of MTGBi-LSTM model is in section 5 and conclusion is in section 6.

Literature Survey

A cardiac arrhythmia occurs intermittently at an early stage of heart disease, and this is difficult to diagnose at an early stage. Classification of cardiac arrhythmia at an early stage helps to effectively treat the patient. Some of the recent methods in arrhythmia classifications were reviewed in this section.