A Novel QT Interval Analysis Method Based On Continuous Wavelet Transform and Philips Algorithm

QT surveillance is the most vital appliance to detect the possibility of sudden death sourced by using pro-arrhythmic drugs treating abnormal conditions in the heart. The repolarization of ventricles makes QT interval surveillance difficult since noisy conditions and individual cardiac situations. Besides, an automated QT algorithm is crucial due to a manual QT measurement with some disadvantages such as fatigue condition in reading long records. In this study, a fully novel automated method combining Continuous Wavelet Transform and Philips method was established to perform QT interval analysis. ECG recordings were obtained from PhyisoNet database marked by manual and standard automated methods. The proposed algorithm had scores of 15.46 and 11.87 millisecond mean error with 11.85 and 9.91 millisecond standard deviation in terms of gold and silver standards, respectively. Also, the entire QT database was utilized in order to test the algorithm performance with the score of 12.89 and 9.76 millisecond mean and standard deviation errors, respectively. The present algorithm performance had scores of -0.21±7.81 at golden standard, and -4.10±18.21 millisecond error for the whole QT database tests, respectively. The proposed algorithm is attained to more stable and robust results with a higher performance than the previous comparable studies.

Application of an Automatic Noise or Signal Removal Algorithm Based on Synchrosqueezed Continuous Wavelet Transform of Passive Surface Wave Imaging: A Case Study in Sichuan, China

Passive surface wave imaging based on noise cross-correlation has been a research hotspot in recent years. However, because randomness of noise is difficult to achieve in reality, prominent noise sources will inevitably affect the dispersion measurement. Additionally, in order to recover high-fidelity surface waves, the time series input during cross-correlation calculation is usually very long, which greatly limits the efficiency of passive surface wave imaging. With an automatic noise or signal removal algorithm based on synchrosqueezed continuous wavelet transform (SS-CWT), these problems can be alleviated. We applied this method to 1-h passive datasets acquired in Sichuan province, China; separated the prominent noise events in the raw field data, and enhanced the cross-correlation reconstructed surface waves, effectively improving the accuracy of the dispersion measurement. Then, using the conventional surface wave inversion method, the shear wave velocity profile of the underground structure in this area was obtained.

Ground Penetrating Radar for Belowground Phenotyping of High-biomass Grasses for Soil Carbon Sequestration

AimsMany governments and companies have committed to moving to net-zero emissions by 2030 or 2050 to tackle climate change, which require the development of new carbon capture and sequestration/storage (CCS) techniques. A proposed method of sequestration is to deposit carbon in soils as plant matter including root mass and root exudates. Adding perennial traits such as rhizomes to crops as part of a sequestration strategy would result in annual crop regrowth from rhizome meristems rather than requiring replanting from seeds which would in turn encourage no-till agricultural practices. Integrating these traits into productive agriculture requires a belowground phenotyping method compatible with high throughput breeding and selection methods (i.e., is rapid, inexpensive, reliable, and non-invasive), however none currently exist. MethodsGround penetrating radar (GPR) is a non-invasive subsurface sensing technology that shows potential as a phenotyping technique. In this study, a prototype GPR antenna array was used to scan roots of the perennial sorghum hybrid, PSH09TX15. A-scan level time-domain analyses and B-scan level time/frequency analyses using the continuous wavelet transform were utilized to extract features of interest from the acquired radargrams. ResultsOf six A-scan diagnostic indices examined, the standard deviation of signal amplitude correlated most significantly with belowground biomass. Time frequency analysis using the continuous wavelet transform yielded high correlations of B-scan features with belowground biomass. ConclusionThese results demonstrate that continued refinement of GPR data analysis workflows should yield a highly applicable phenotyping tool for breeding efforts in environments where selection is otherwise impractical on a large scale.

Brain Activity Measurement during a Mental Arithmetic Task in fNIRS Signal Using Continuous Wavelet Transform

Purpose: Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive imaging technology with widespread use in cognitive sciences and clinical studies. It indirectly measures neural activation by measuring alterations of oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) in tissues. This study used mental arithmetic task for analyzing the activation of the frontal cortex. Materials and methods: The fNIRS instrument was used for measuring the alterations of HbO2 and Hb in healthy subjects during the task. Then the recorded signals were filtered in the frequency range of 3 to 80 mHz. The Continuous Wavelet Transform (CWT) of each of the HbO2 and Hb signals in each channel was calculated in the intended frequency range, followed by the calculation of the energy of obtained coefficients. Finally, for the performed tasks, the average energy of each channel in each region was obtained. Then the energies of spatially symmetric channel pairs in the two hemispheres were compared using the t-test. Results: Results demonstrated that the average energy of HbO2 signal for corresponding channels in the temporal, Medial Prefrontal Cortex (MPFC), and Dorsolateral Prefrontal Cortex (DLPFC) regions had significant differences (P<0.05). Also, a significant difference was observed in the temporal, medial prefrontal, and Ventrolateral Prefrontal Cortex (VLPFC) regions for Hb signal. Conclusion: The obtained results indicate activation in both HbO2 and Hb signals in the DLPFC, temporal, and MPFC regions, considering the performance of memory and the frontal cortex under mental arithmetic tasks. Therefore, it can be concluded that this technique is effective and appropriate for analyzing alterations of brain oxygen levels during cognitive activity.