Geometric detection algorithm design for ECG data analysis using wavelet

The need for clear ECG signals is increasing to reduce the probability of misdiagnosis regarding heart diseases. An algorithm was designed to diagnose patients with heart diseases using ECG signal analysis so that it can help physicians in the decision-making process. Before analyzing the ECG signal, noise of the low and high frequency components was removed through preprocessing. After preprocessing, pattern analysis detected important features on which diagnosis will be given. Then, the analysis was applied on the pure ECG signal to detect the patient's heart diseases. All feature points were extracted by using the proposed algorithm, called 'Geometric Detection (GD)'. Results showed that performance was superior to others in standard error of the sample mean and variance. Data from CSE (Common Standards for Electrocardiography) database were used to test each algorithm except for GD, because patients' ECG data was used to test the GD algorithm. Detection rate of the GD algorithm (se(%)) was 99.1% and we confirmed that the proposed algorithm is superior to the other algorithm in terms of stability and standard error of the sample mean. The result of the performance evaluation showed that the proposed algorithm produced higher accuracy and stability than the other algorithms.