In intensive care units (ICU), the occurrence of acute hypotensive episodes (AHE) is the key problem for the clinical research and it is meaningful for clinical care if we can use appropriate computational technologies to predict the AHE. In this study, based on the records of patients in ICU from the MIMICⅡclinical data, the chaos signal analysis method was applied to the time series of mean artery pressure, and then the patient's Lyapunov exponent curve was drawn ultimately. The research showed that a curve mutation appeared before AHE symptoms took place. This is powerful and clear basis for AHE determination. It is also expected that this study may offer a reference to research of AHE theory and clinical application.
In order to evaluate the ability of human standing balance scientifically, we in this study proposed a new evaluation method based on the chaos nonlinear analysis theory. In this method, a sinusoidal acceleration stimulus in forward/backward direction was forced under the subjects' feet, which was supplied by a motion platform. In addition, three acceleration sensors, which were fixed to the shoulder, hip and knee of each subject, were applied to capture the balance adjustment dynamic data. Through reconstructing the system phase space, we calculated the largest Lyapunov exponent (LLE) of the dynamic data of subjects' different segments, then used the sum of the squares of the difference between each LLE (SSDLLE) as the balance capabilities evaluation index. Finally, 20 subjects' indexes were calculated, and compared with evaluation results of existing methods. The results showed that the SSDLLE were more in line with the subjects' performance during the experiment, and it could measure the body's balance ability to some extent. Moreover, the results also illustrated that balance level was determined by the coordinate ability of various joints, and there might be more balance control strategy in the process of maintaining balance.
In order to improve the reliability of cardiac pacemaker contact-less power supply technology, this paper proposes a novel application of wireless feedback voltage stabilizing technology to adjust heart disease patients with inner power supply filter circuit output voltage and current control method, to keep the output voltage stability, and to ensure that the super capacitor and cardiac pacemaker to get a stable power supply. To implement the real-time accurate voltage control with considering the primary and secondary side inductance coupling coefficient changes, the change of the external power supply voltage and load, it is necessary to test thee real-time and accurate output voltage and current value after rectifying filtering. Therefore, based on the chaotic control theory, we adopted method of phase diagram on the basis of the quick observation after rectifying filtering, so that the method of voltage and current could improve the detection time of the circuit. The phase diagram of proposed control method can be divided into 8 segments, and we got 7 zero-extreme points. When these zero-extreme points are detected, according to extreme points of the zero instantaneous values, the corresponding average values of voltage and current were obtained. Simulation and experimental results showed that using the above method can shorten the response time to less than switch devices 1/2 switching cycles, thus validating the effectiveness and feasibility of the proposed detection algorithm.
Tinnitus is a common clinical symptom and its occurrence rate is high. It seriously affects life quality of the patients. Scientific researches show that listening some similar and none-repetitive music can relieve tinnitus to some extent. The overall music accorded with self-similarity character by the direct mapping method based on chaos. However, there were often the same tones continuous repeating a few times and tone mutations. To solve the problem, this paper proposes a new method for tinnitus rehabilitation sound synthesis based on pentatonic scale, chaos and musical instrument digital interface (MIDI). Experimental results showed that the tinnitus rehabilitation sounds were not only self-similar and incompletely reduplicate, but also no sudden changes. Thus, it has a referential significance for tinnitus treatment.
To distinguish the randomness and chaos characteristics of physiological signals and to keep its performance independent of the signal length and parameters are the key judgement of performance of a complexity algorithm. We proposed an encoding Lempel-Ziv (LZ) complexity algorithm to try to explicitly discern between the randomness and chaos characteristics of signals. Our study also compared the effects of length of time series, the sensitivity to dynamical properties change of time series and quantifying the complexity between gauss noise and 1/f pink noise ELZ with those from classic LZ (CLZ), multi-state LZ (MLZ), sample entropy (SampEn) and permutation entropy (PE). The experimental results showed ELZ could not only distinguish the randomness and chaos characteristics of time series on all time length (i.e. 100, 500, 5 000), but also reflected exactly that the complexity of gauss noise was lower than that of pink noise, and responded change of dynamic characteristics of time series in time. The congestive heart failure (CHF) RR Interval database and the normal sinus rhythm (NSR) RR Interval database created by Massachusetts Institute of Technology (MIT) and Boston Beth Israel Hospital(BIH)were used as real data in our study. The results revealed that the ELZ could show the complexity of congestive heart failure which was lower than that of normal sinus rhythm during all lengths of time series (P<0.01), and the ELZ algorithm had better generalization ability and was independent of length of time series.