Dynamic thresholding based efficient QRS complex detection with low computational overhead.

• A low computational load and dynamic thresholding based QRS complex detection method. • The dynamic threshold value is computed for both upward and downward QRS complex simultaneously. • The proposed method was applied on five different full-length databases, MITBIH AD, FTD, ESTD, NSDT, and FTDB, has given AETR of 2.35 s, 3.8 s, 3.69 s, 1.99 s, 0.53 s, respectively. • The proposed method is tested on total 195 records and 672,472 beats of five different ECG databases, which yields the 99.70 and 99.69, average Se%, and +P% respectively. QRS-complex detection is a primitive step in the detection of cardiac disorder using electrocardiogram (ECG). Abnormal and varying peaks, baseline wander and other noise are the main challenges in accurate QRS-complex detection. In this paper, A window-based FIR filter is used to eliminate the high-frequency noise. Next, the ECG is enhanced to the power third after multiplication followed by normalization and moving average process to retain dynamic QRS-complex. Then baseline and root mean square (RMS) value of first three seconds of the signal are used for initial thresholding, later dynamic thresholding process was utilized to update the threshold value after the detection of four R-peaks. The threshold value was automatically updated using the previous threshold value, R-peak amplitude, RR-interval, and RR-intervals means. Kurtosis coefficient computation is used for discarding prominent T-wave and further this technique located the QRS-complex accurately in the raw ECG signal. The proposed method was applied to Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia Database (MIT-BIH AD), Fantasia Database (FTD), European ST-T Database (ESTD), MIT-BIH Noise Stress Test Database (NSTD), and Direct Fetal ECG Database (FTD) for its evaluation and validation. The overall sensitivity rate of 99.70% and positive predictivity rate of 99.69% have been achieved. The proposed method is based on dynamic thresholding and simple decision rules, which makes this method computationally efficient. Wide validation over five different databases proves the robustness of this method. [ABSTRACT FROM AUTHOR]