Objective To investigate the effect of new type ultrafiltration technique in preventing and relieving the main organ injury that may follow open heart surgery with cardiopulmonary bypass (CPB),and improve the operative effects and decrease the postoperative complications. Methods Thirty patients with congenital heart diseases were randomly divided into two groups. Modified ultrafiltration group: modified ultrafiltration was used after CPB; new type ultrafiltration group: new type ultrafiltration technique was used throughout CPB. The serum concentrations of nflammatory mediators,hematocrit,serum albumin concentrations, pulmonary function, operative duration time and main organ function parameters were measured in both groups. Results Ultrafiltration time after CPB in new type ultrafiltration group was significantly shorted as compared with modified ultrafiltration group（6.35±1.28 min vs. 12.45±4.52 min，P=0.000）; serum concentrations of interleukin6(IL-6)and tumor necrosis factor α(TNF-α) after CPB were significantly decreased as compared with modified ultrafiltration group（292.84±58.23 μg/L vs. 383.79±66.24 μg/L,P=0.000; 13.32±2.31 μg/L vs. 16.41±2.65 μg/L,P=0.000）; the hematocrit and serum albumin concentrations at the ten minutes after CPB were increased as compared with modified ultrafiltration group (0.39±0.04 vs. 0.35±0.03,P=0.003; 38.32±4.26 g/L vs. 34.04±2.83 g/L, P=0.003); the mechanical ventilation support time and ICU time after operation was shorted as compared with modified ultrafiltration group (Plt;0.05); main organ function was improved as compared with the modified ultrafiltration group. Conclusion The clinical application of new type ultrafiltration throughout CPB can effectively exclude some harmful inflammatory mediators, concentrate blood,short operation time,attenuate the main organ edema and injury.
The extraction of pulse rate variability(PRV) in daily life is often affected by exercise and blood perfusion. Therefore, this paper proposes a method of detecting pulse signal and extracting PRV in post-ear, which could improve the accuracy and stability of PRV in daily life. First, the post-ear pulse signal detection system suitable for daily use was developed, which can transmit data to an Android phone by Bluetooth for daily PRV extraction. Then, according to the state of daily life, nine experiments were designed under the situation of static, motion, chewing, and talking states, respectively. Based on the results of these experiments, synchronous data acquisition of the single-lead electrocardiogram (ECG) signal and the pulse signal collected by the commercial pulse sensor on the finger were compared with the post-auricular pulse signal. According to the results of signal wave, amplitude and frequency-amplitude characteristic, the post-ear pulse signal was significantly steady and had more information than finger pulse signal in the traditional way. The PRV extracted from post-ear pulse signal has high accuracy, and the accuracy of the nine experiments is higher than 98.000%. The method of PRV extraction from post-ear has the characteristics of high accuracy, good stability and easy use in daily life, which can provide new ideas and ways for accurate extraction of PRV under unsupervised conditions.
In order to quantitatively analyze the morphology and period of pulse signals, a time-space analytical modeling and quantitative analysis method for pulse signals were proposed. Firstly, according to the production mechanism of the pulse signal, the pulse space-time analytical model was built after integrating the period and baseline of pulse signal into the analytical model, and the model mathematical expression and its 12 parameters were obtained for pulse wave quantification. Then, the model parameters estimation process based on the actual pulse signal was presented, and the optimization method, constraints and boundary conditions in parameter estimation were given. The spatial-temporal analytical modeling method was applied to the pulse waves of healthy subjects from the international standard physiological signal sub-database Fantasia of the PhysioNet in open-source, and we derived some changes in heartbeat rhythm and hemodynamic generated by aging and gender difference from the analytical models. The model parameters were employed as the input of some machine learning methods, e.g. random forest and probabilistic neural network, to classify the pulse waves by age and gender, and the results showed that random forest has the best classification performance with Kappa coefficients over 98%. Therefore, the space-time analytical modeling method proposed in this study can effectively quantify and analyze the pulse signal, which provides a theoretical basis and technical framework for some related applications based on pulse signals.
Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.