Abstract
Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG diagnosis depends on the personal judgment of medical staff, which leads to heavy burden and low efficiency of medical staff. Automatic ECG analysis technology will help the work of relevant medical staff. In this paper, we use the MIT-BIH ECG database to extract the QRS features of ECG signals by using the Pan-Tompkins algorithm. After extraction of the samples, K-means clustering is used to screen the samples, and then, RBF neural network is used to analyze the ECG information. The classifier trains the electrical signal features, and the classification accuracy of the final classification model can reach 98.9%. Our experiments show that this method can effectively detect the abnormality of ECG signal and implement it for the diagnosis of heart disease.
Highlights
Cardiac diseases are likely to occur in all age groups [1]
Based on the above problems, this paper proposes an automatic ECG analysis technology, which uses Pan-Tompkins algorithm, uses ECG data in MIT-BIH ECG database to extract the features of ECG QRS complex, and uses K -means clustering to filter the data after cutting the samples
The experimental data in this paper are from the MIT-BIH ECG database [18], which is provided by MIT and is often used to study arrhythmia and other heart symptoms
Summary
Cardiac diseases are likely to occur in all age groups [1]. Surgery for cardiac diseases is difficult and risky. Conservative treatment is often used for nonessential cardiac diseases, which results in high recurrence probability and high diagnostic efficiency. Electrocardiogram (ECG) detection is currently the most effective and direct way to detect ECG signals [2]. The diagnosis of cardiac diseases is mainly determined by medical doctors and clinicians through manual detection and ECG analysis. The related ECG automatic analysis technology can provide effective help for the diagnosis of medical workers, which can help to improve the efficiency of diagnosis
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