QRS complex detection plays an important role in electrocardiogram (ECG) automatic analysis. The accuracy and robustness of the detection algorithm greatly affect its practicability. However, the existing detection algorithms are greatly affected by ECG signal quality, and some detection algorithms cannot even work properly due to the poor signal quality. In this paper, a robust QRS complex detection algorithm is proposed based on Shannon energy envelope and Hilbert transform. The detection algorithm extracts the Shannon energy envelope of the preprocessed ECG signal, performs Hilbert transform on the envelope signal, then detects the suspected [Formula: see text]-peaks on the envelope by detecting the position of zero pass and screens the real [Formula: see text]-peaks by using a combination of ECG refractory period and backtracking mechanism. The proposed detection algorithm is validated using MIT-BIH Arrhythmia Database, and achieves the average detection accuracy of 99.69%, sensitivity of 99.81% and positive predictivity of 99.88%. Experimental results show that the proposed detection algorithm can still detect QRS complex correctly under complex interference, and the performance of the algorithm is hardly affected.

This study aimed to analyze the diffusion of electrical stimulation signals in human tissue and provide a theoretical basis for multi-electrode combined stimulation. The standard single-layer human head model based on electromagnetic simulation was taken as the geometric structure model. The model filler was assumed to be muscle tissue, and a finite element model with muscle characteristics was established. A 20-mA DC electrical signal was input, and the propagation mechanism of the signal in the simplified brain model was calculated and analyzed through multi-physical field simulation software. The signal was mainly concentrated around the electrode; when multi-electrode combined stimulation was used, signal superposition existed at the geometric center of the model, and the signal was enhanced. Slice interception analysis demonstrated that the signal attenuation intensity was approximately 8 dB/cm in homogeneous muscle tissue. To compare the performance of the single-layer model and multi-layer model, a semi-refined digital brain model was established, and simulated signal diffusion of the two models was analyzed. Comparative analysis found that due to the uneven distribution of tissues and the high shielding property of bone, the signal was highly scattered at the bone contact, but the superposition of signals in the brain center still existed.

Objective: The detection of QRS complexes is an important part of computer-aided analysis of electrocardiogram (ECG). However, most of the existing detection algorithms are mainly for single-lead ECG signals, which requires high quality of signal. If the signal quality decreases suddenly due to some interference, then the current algorithm is easy to cause misjudgment or missed detection. To improve the detection ability of QRS complexes under sudden interference, we study the QRS complexes information on multiple leads in-depth, and propose a two-lead joint detection algorithm of QRS complexes. Methods: Firstly, the suspected QRS complexes are screened on the main lead. For the suspected QRS complexes with low confidence and the complexes that may be missed, further accurate detection and joint judgment shall be carried out at the corresponding position of the auxiliary lead. At the same time, the adaptive threshold adjustment algorithm and backtracking mechanism are used to modify the detection results. Results: The proposed detection algorithm is validated using 48 ECG records of the MIT-BIH arrhythmia database, and achieves average detection accuracy of 99.71%, sensitivity of 99.88% and positive predictivity of 99.81%. Conclusion: The proposed algorithm has high accuracy, which can effectively deal with the sudden interference of ECG signal. Meanwhile, the algorithm requires small amount of computation, and can be embedded into hardware for real-time detection.