A real-time QRS complex detection algorithm based on differential threshold method

Exact and fast automatic analyses of electrocardiogram (ECG) signal is critical for the computer based diagnosis of cardiac diseases, which has attracted many researches´ attentions in recent years. In the present study, a novel QRS complex detection approach based on differential threshold method was proposed, which was designed as three analysis steps of an improved differential processor, a detector of the R wave, and a detector of the Q and S waves. The QRS complex detection performance of the proposed algorithm was further evaluated with clinical ECG records in patients coming from the widely recognized Massachusetts Institute of Technology Database (MIT-BIH DB). As a result, 67,167 beats were correctly determined with the proposed approach from a total of 67,197 QRS beats (30 ECG records in variant rhythms, 30 minutes for each), while the numbers of false positives and false negatives were 251 and 208, respectively. The summarized sensitivity and positive predictivity of the proposed method were obtained as 99.69% and 99.63%, respectively. Meanwhile, the computing time of the proposed algorithm for a ECG record of 30 min is only 1.40 seconds, which shows a distinct advantage on computation requirement compared with previous works. In conclusion, the proposed straightforward algorithm for QRS complex detection would potentially be implemented easily for portable monitoring applications with both a better performance and a lower computation requirement.