Detection of Angina Pectoris Using ECG Signals

Abstract

Angina pectoris due to ischemia is very crucial to detect because, if it can be detected earlier, doctor can provide the patient proper medication to cure. Sometimes from a long ECG data it is tiresome to calculate and differentiate the normal and angina pectoris affected ECG peak to decide about the condition of the patient. In addition, the remote areas of lower and midlower income countries often face lack of experienced doctors or highly cost devices like ecocardiogram, MRI to detect angina. In these regions, automatic identification of ECG features can be a fruitful solution. Therefore, only way is computerized and efficient ECG analyzing algorithm development that can be able to detect angina pectoris. In this work, an efficient algorithm is developed to detect angina pectoris from ECG signal. This algorithm consists of several steps to take decision on ECG signal. First of all this algorithm removes baseline wandering from ECG signal by baseline wandering path finding algorithm. After that it removes other noises from ECG signal by Gaussian weighted moving average window method. In this consequence, QRS complex was detected by very well-known method First and Second Derivative (FS2) algorithm and gradually other important points like S, J, K, and T were detected by possible range maxima-minima criterion. Besides, the isoelectric line of ECG signal is estimated and eventually the statistical features of J-K points of normal and abnormal ECG peaks are compared with that isoelectric line by the algorithm, Finally, this algorithm takes the decision whether the patient is suffering from angina or not. This algorithm is applied on MIT arrhythmia Database to detect angina pectoris. From the result provided by this algorithm, we have found 94% (average) accuracy which is noticeable. In addition with that the sensitivity and specificity of our proposed algorithm have also been calculated which are found 91% and 89%, respectively. Since the previous work is based on the single feature, it may prove inappropriate for all the time. Therefore with the help of multiple features machine learning based approach k-nearest neighbor (kNN) method has been deployed in this research work to make it more accurate and acceptable. Although kNN based prediction method also provide almost similar results found by the previous methodologies. Therefore our proposed approach for angina pectoris detection has been testified by both statistical and kNN based method. It is expected that the proposed algorithm will be helpful for computerized angina pectoris detection from ECG signals.