Denoising and automated R peak detection in the ECG using Discrete Wavelet Transform
This work presents a novel approach to ECG R peak detection based on the Discrete Wavelet Transform. 18,647 beats were analysed from thirty AF patients who underwent DC cardioversion at Royal Victoria Hospital, Belfast. The efficacy of the R peak detection algorithm for both normal sinus rhythm and atrial fibrillation beats was assessed using three performance parameters: Sensitivity, Positive Predictivity and Accuracy. The preliminary results acquired using the proposed R peak detection approach provided results of 99.61% 99.88% and 99.50% respectively, indicating that the utilization of DWT to assist peak detection is a viable method. The second phase of the study assessed how effectively the algorithm could discriminate between segments presenting normal sinus rhythm and those presenting atrial fibrillation based on RR interval data derived from the R peak detection method. Fifty segments of normal sinus rhythm and AF ECG were tested, and 100% successful classification was achieved. This highlights that the DWT R peak detection method can be utilized in a practical application to differentiate between patients in normal sinus rhythm and those in AF.
2016 Computing in Cardiology Conference (CinC)