To investigate the effect of stepwise paced breathing (PB) on pulse transit time (PTT), we collected physiological signals of electrocardiogram (ECG), respiration and arterial pulse wave during a procedure of stepwise PB, which consists of 6 different breathing rates changing in a protocol of 14.0-12.5-11.0-9.5-8.0-7.0 breath per minute (BPM), with each breathing rate lasting 3 minutes. Twenty two healthy adults involved in this experiment and the change of PTT was analyzed during the stepwise PB procedure. In our study, the PTT was measured by calculating the time interval from the R-spike of the ECG to the peaks of the second derivative of the arterial pulse wave. Ensemble empirical mode decomposition (EEMD) was applied to PTT to decompose the signal into different intrinsic mode function, and respiratory oscillation and trend component (baseline) in PTT were further extracted. It was found that the respiratory oscillations in the PTT increased with decreasing of the PB rate, and many of the subjects (14 out of 22) showed the phenomena of PTT baseline increasing during the stepwise PB procedure. The results indicated that the stepwise PB procedure induced a high level of cardiovascular oscillation and produced an accumulative effect of PTT baseline increase. As PTT is capable of predicting changes in BP over a short period of time, increase of PTT baseline indicates the decrease of blood pressure. The experiments showed that the stepwise PB procedure could reduce blood pressure for most subjects. For future work, it is necessary to develop certain indices differentiating the effectiveness of the stepwise PB procedure on the PTT baseline change, and to test the effectiveness of this stepwise PB procedure on blood pressure reduction for patients with essential hypertension.
Fetal heart rate (FHR) baseline estimation is of significance for the computerized analysis of fetal heart rate and the assessment of fetal state. In our work, a fetal heart rate baseline correction algorithm was presented to make the existing baseline more accurate and fit to the tracings. Firstly, the deviation of the existing FHR baseline was found and corrected. And then a new baseline was obtained finally after treatment with some smoothing methods. To assess the performance of FHR baseline correction algorithm, a new FHR baseline estimation algorithm that combined baseline estimation algorithm and the baseline correction algorithm was compared with two existing FHR baseline estimation algorithms. The results showed that the new FHR baseline estimation algorithm did well in both accuracy and efficiency. And the results also proved the effectiveness of the FHR baseline correction algorithm.
The selection of fiducial points has an important effect on electrocardiogram (ECG) denoise with cubic spline interpolation. An improved cubic spline interpolation algorithm for suppressing ECG baseline drift is presented in this paper. Firstly the first order derivative of original ECG signal is calculated, and the maximum and minimum points of each beat are obtained, which are treated as the position of fiducial points. And then the original ECG is fed into a high pass filter with 1.5 Hz cutoff frequency. The difference between the original and the filtered ECG at the fiducial points is taken as the amplitude of the fiducial points. Then cubic spline interpolation curve fitting is used to the fiducial points, and the fitting curve is the baseline drift curve. For the two simulated case test, the correlation coefficients between the fitting curve by the presented algorithm and the simulated curve were increased by 0.242 and 0.13 compared with that from traditional cubic spline interpolation algorithm. And for the case of clinical baseline drift data, the average correlation coefficient from the presented algorithm achieved 0.972.
Identification of real-time uterine contraction status is very significant to labor analgesia, but the traditional uterine contraction analysis algorithms and systems cannot meet the requirement. According to the situations mentioned above, this paper designs a set of algorithms for the real-time analysis of uterine contraction status. The algorithms include uterine contraction signal preprocessing, uterine contraction baseline extraction based on histogram and linear iteration and an algorithm for the real-time analysis of uterine contraction status based on finite state machines theory. It uses the last uterine status and a series of state transfer conditions to identify the current uterine contraction status, as well as a buffer mechanism to avoid false status transitions. To evaluate the performance of the algorithm, we compare it with an existing uterine contraction analysis algorithm used in the electronic fetal monitor. The experiments show that our algorithm can analyze the uterine contraction status while monitoring the uterine contraction signal in a real-time. Its sensitivity reaches 0.939 9 and its positive predictive value is 0.869 3, suggesting that the algorithm has high accuracy and meets the need of clinical monitoring.
Objective To improve the sensitivity and broaden the applicability of N-of-1 trials in traditional Chinese medicine (TCM), the clinical application and methodology of single-case experimental designs (N-of-1trials, multiple-baseline designs; MBDs) were expounded, compared, and discussed. Methods This paper introduced the current utility of N-of-1 trials in TCM research, introduced MBDs, and compared the methodologies of N-of-1 trials, MBDs and crossover design. Finally, two design schemes to improve the sensitivity and applicability of N-of-1 trials were illustrated. Results N-of-1 trials conformed to the TCM concept of treatment based on syndrome differentiation; however, due to the complex composition of TCM, the results were easily affected by carryover effect. In MBDs, the intervention was introduced in a staggered way, no washout period was needed, and the required sample size was small. MBDs were generally used to preliminarily indicate the effect of intervention; however, the statistical analysis was relatively complicated, and there were few MBDs used in clinical trials of TCM at present. Compared with crossover trials, single-case experimental designs had advantages and disadvantages. N-of-1 trials might best reflect the individualized treatment of TCM and a suitable statistical model (e.g., hierarchical Bayesian statistical method) was expected to improve the sensitivity and applicability of N-of-1 trials in TCM. Combining clinical trial designs (e.g., the combination of N-of-1 trials and MBDs) would complement the limitations of N-of-1 trials, and expand the scope of conditions applicable for study. Conclusion N-of-1 trials have both advantages and disadvantages in TCM research. Improved statistical models or combined study designs will improve the sensitivity and broaden the applicability of N-of-1 trials in TCM.
How to improve the performance of circulating tumor DNA (ctDNA) signal acquisition and the accuracy to authenticate ultra low-frequency mutation are major challenges of minimal residual disease (MRD) detection in solid tumors. In this study, we developed a new MRD bioinformatics algorithm, namely multi-variant joint confidence analysis (MinerVa), and tested this algorithm both in contrived ctDNA standards and plasma DNA samples of patients with early non-small cell lung cancer (NSCLC). Our results showed that the specificity of multi-variant tracking of MinerVa algorithm ranged from 99.62% to 99.70%, and when tracking 30 variants, variant signals could be detected as low as 6.3 × 10−5 variant abundance. Furthermore, in a cohort of 27 NSCLC patients, the specificity of ctDNA-MRD for recurrence monitoring was 100%, and the sensitivity was 78.6%. These findings indicate that the MinerVa algorithm can efficiently capture ctDNA signals in blood samples and exhibit high accuracy in MRD detection.