Realtime Evaluation of Cardiac Activities Using Multi Features with KNN Classifier - A Pilot Study

Abstract

Abstract Monitoring of cardiac episodes is essential for patients with suspected cardiac arrhythmias. The quantitative evaluation of cardiac activities during continuous unattended cardiac monitoring will support clinician to understand the nature of the physical activities. This pilot study is proposed to assess the cardiac activities using multi features with KNN classifier. ECG was collected from healthy volunteers under rest and during exercise. The time and frequency domain features such as BPM, QRS duration, RR interval, SD, CV, RMSRR, NN50, SDNN, Shannon entropy, log entropy, and sample entropy were extracted. Further, multi-classification was employed using KNN classifier. The performance of KNN classifier is tested in terms of classification accuracy and elapsed time and it was found that Euclidean distance for k=2 was showing better performance. Statistical analysis was performed to confirm the suitability of the features using Pearson’s correlation coefficient and box plot. Among the features BPM, QRS, RR, sample and log entropies were found to be significant. Specifically, sample entropy provided to be the best candidate with p<0.01. The study reveals an overall classification accuracy of 90% was obtained.