Focused on the issue of decoding visual input from neuron response signal, a method to reconstruct visual input using neurons action potential (Spike) signal was proposed. Firstly, the Spike signal from the pigeon Optic Tectum (OT) neurons was recorded and the firing rate characteristics of Spike were extracted. Then, a linear inverse filter reconstruction model and a convolution neural network reconstruction model were constructed to realize the reconstruction of the visual input. Finally, the number of channels, time bin, data time length and delay time were optimized. Under the same parameter condition, the cross correlation coefficient of image reconstruction using linear inverse filter reconstruction model reached 0.910 7±0.021 9, and the cross correlation coefficient of image reconstruction using convolution neural network reconstruction model reached 0.927 1±0.017 6. The results show that the visual input can be reconstructed effectively by extracting firing rate characteristics of neuron Spike and using linear inverse filter reconstruction model and convolution neural network reconstruction model.

In view of the problem that the existing QRS complex detection algorithms of electrocardiograph are still not ideal for the detection of some signal abnormalities, a QRS complex detection method combining Shannon energy with adaptive threshold was proposed to solve the problem of low accuracy of QRS complex detection. Firstly, the Shannon energy envelope was extracted from the pre-processed signal. Then, the QRS complex was detected by the improved adaptive threshold method. Finally, the location of the detected QRS complex was located according to the enhanced signal of the detected QRS complex. The MIT-BIH arrhythmia database was employed to evaluate the performance of the proposed algorithm. Results show that the algorithm can accurately detect the location of the QRS complex even when high P wave, T wave, irregular rhythm and serious noise interference exist in the signal, and has the sensitivity, positive and accuracy of the overall data detection reached 99.88%, 99.85% and 99.73% respectively, meanwhile the proposed algorithm can quickly complete the QRS complex detection task with the accuracy guaranteed.