A new approach of QRS complex detection based on matched filtering and triangle character analysis

QRS complex detection usually provides the fundamentals to automated electrocardiogram (ECG) analysis. In this paper, a new approach of QRS complex detection without the stage of noise suppression was developed and evaluated, which was based on the combination of two techniques: matched filtering and triangle character analysis. Firstly, a template of QRS complex was selected automatically by the triangle character in ECG, and then it was time-reversed after removing its direct current component. Secondly, matched filtering was implemented at low computational cost by finite impulse response, which further enhanced QRS complex and attenuated non-QRS regions containing P-wave, T-wave and various noise components. Subsequently, triangle structure-based threshold decision was processed to detect QRS complexes. And RR intervals and triangle structures were further analyzed for the reduction of false-positive and false-negative detections. Finally, the performance of the proposed algorithm was tested on all 48 records of the MIT-BIH Arrhythmia Database. The results demonstrated that the detection rate reached 99.62 %, the sensitivity got 99.78 %, and the positive prediction was 99.85 %. In addition, the proposed method was able to identify QRS complexes reliably even under the condition of poor signal quality.