Review of Computational Techniques for the Analysis of Abnormal Patterns of ECG Signal Provoked by Cardiac Disease

Abstract

The 12-lead ECG aids in the diagnosis of myocardial infarction and is helpful in the prediction of cardiovascular disease complications. It does, though, have certain drawbacks. For other electrocardiographic anomalies such as Left Bundle Branch Block and Left Ventricular Hypertrophy syndrome, the ECG signal with Myocardial Infarction is difficult to interpret. These diseases cause variations in the ST portion of the ECG signal. It reduces the clarity of ECG signals, making it more difficult to diagnose these diseases. As a result, the specialist is misled into making an erroneous diagnosis by using the incorrect therapeutic technique. Based on these concepts, this article reviews the different procedures involved in ECG signal pre-processing, feature extraction, feature selection, and classification techniques to diagnose heart disorders such as Left Ventricular Hypertrophy, Bundle Branch Block, and Myocardial Infarction. It reveals the flaws and benefits in each segment, as well as recommendations for developing more advanced and robust methods for diagnosing these diseases, which will increase the system's accuracy. The current issues and prospective research directions are also addressed.

Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics

“…Other applications of deep learning include a real-time maskless-face detector using deep residual networks [356], topology optimization with embedded physical law and physical constraints [357], prediction of stress-strain relations in granular materials from triaxial test results [358], surrogate model for flight-load analysis [359], classification of domestic refuse in medical institutions based on transfer learning and convolutional neural network [360], convolutional neural network for arrhythmia diagnosis [361], e-commerce dynamic pricing by deep reinforcement learning [362], network intrusion detection [363], road pavement distress detection for smart maintenance [364], traffic flow statistics [365], multi-view gait recognition using deep CNN and channel attention mechanism [366], mortality risk assessment of ICU patients [367], stereo matching method based on space-aware network model to reduce the limitation of GPU RAM [368], air quality forecasting in Internet of Things [369], analysis of cardiac disease abnormal ECG signals [370], detection of mechanical parts (nuts, bolts, gaskets, etc.) by machine vision [371], asphalt road crack detection [372], steel commondity selection using bidirectional encoder representations from transformers (BERT) [373], short-term traffic flow prediction using LSTM-XGBoost combination model [374], emotion analysis based on multi-channel CNN in social networks [375].…”

Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review

“…When confronted with faulty ECG tracings, traditional models can easily lose their robustness (41). • Fourth, the ECG characteristics of MI, such as ST-segment deviation, are often inadequate to detect MI since they may be seen in other cardiac conditions, such as left ventricular hypertrophy and left bundle-branch block (44). Moreover, ECG abnormalities are also common in patients who have myocarditis or Takotsubo syndrome (TTS) (45,46) Therefore, the low accuracy of the conventional ML methods keeps the manual-crafted feature extraction as a central task, which may be attributed to the traditional imperfect ECG classification criteria.…”

“…There exist other six related works that focus on automatic ECG analysis for the prediction of structural cardiac pathologies, including two systematic reviews (52,53), one meta-analysis (54), and three comprehensive reviews (44,51,55)…”

Attention-Based Residual Dense Shrinkage Network for ECG Denoising

“…ECG signal plays an important role in clinical analysis, which can reflect the heart's electrical activity. However, ECG signals received from instruments and equipment may be polluted by various noises, which will lead to the problem of abnormal detection [1]. It will significantly affect the accuracy of diagnosis and mislead the subsequent medical pathological analysis.…”