An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and
Ahmed Faeq Hussein1, Shaiful Jahari Hashim2, Fakhrul Zaman Rokhani2
1Biomedical Engineering Department, Faculty of Engineering, Al-Nahrain University, Baghdad 10072, Iraq.
Insights
A new method using multi-lead electrocardiograms (ECG) accurately detects myocardial ischemia. This advanced ECG analysis offers high accuracy for identifying heart conditions, aiding cardiologists and enabling home screening.
Area of Science:
- Cardiology and Biomedical Signal Processing
Background:
- Cardiovascular Disease (CVD) is a leading cause of mortality, with myocardial ischemia requiring timely detection to prevent myocardial infarction.
- Electrocardiogram (ECG) interpretation for conditions like ischemia often necessitates expert cardiologist analysis.
- Existing algorithms for ECG analysis face challenges in accuracy and reliability for ischemia detection.
Purpose of the Study:
- To propose a novel scheme for enhanced myocardial ischemia detection using multi-lead, long-interval ECG.
- To improve the accuracy and reliability of identifying ischemic events through advanced signal processing and classification.
Main Methods:
- Utilized multi-lead long-interval ECG data, focusing on ST and PR segments.
- Employed Choi-Williams time-frequency distribution for extracting ischemic-related ECG features.
- Implemented a multi-class Support Vector Machine (SVM) classifier trained on data from 92 normal and 266 patient records across four databases.
Main Results:
- Achieved an overall accuracy of 99.09% in detecting myocardial ischemia.
- Demonstrated high sensitivity (99.49%) and specificity (98.44%) in classification.
- Validated the scheme's flexibility, validity, and reliability across diverse and unknown datasets.
Conclusions:
- The proposed multi-lead ECG analysis scheme provides a robust and precise method for myocardial ischemia detection.
- This approach can significantly assist cardiologists in diagnosing cardiac abnormalities.
- The scheme holds potential for integration into home screening systems for rapid emergency evaluations.
Abstract:
Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases.
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