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Biomedical subjects

Xingming Guo

Publications and source records attributed to Xingming Guo.

7 recordsLinked to original sources
PubMed2006

Characteristics of inhalable particulate matter concentration and size distribution from power plants in China.

In this investigation, the collection efficiency of particulate emission control devices (PECDs), particulate matter (PM) emissions, and PM size distribution were determined experimentally at the inlet and outlet of PECDs at five coal-fired power plants. Different boilers, coals, and PECDs are used in these power plants. Measurement in situ was performed by an electrical low-pressure impactor with a sampling system, which consisted of an isokinetic sampler probe, precut cyclone, and two-stage dilution system with a sample line to the instruments. The size distribution was measured over a range from 0.03 to 10 microm. Before and after all of the PECDs, the particle number size distributions display a bimodal distribution. The PM2.5 fraction emitted to atmosphere includes a significant amount of the mass from the coarse particle mode. The controlled and uncontrolled emission factors of total PM, inhalable PM (PM10), and fine PM P(M2.5) were obtained. Electrostatic precipitator (ESP) and baghouse total collection efficiencies are 96.38-99.89% and 99.94%, respectively. The minimum collection efficiency of the ESP and the baghouse both appear in the particle size range of 0.1-1 microm. In this size range, ESP and baghouse collection efficiencies are 85.79-98.6% and 99.54%. Real-time measurement shows that the mass and number concentration of PM10 will be greatly affected by the operating conditions of the PECDs. The number of emitted particles increases with increasing boiler load level because of higher combustion temperature. During test run periods, the data reproducibility is satisfactory.

Air Pollutants↗
PubMed2006

[Heart sound recognition algorithm based on PNN for evaluating cardiac contractility change trend].

This paper discusses the recognition of heart sound for evaluating the cardiac contractility change trend, which includes heart sound samples recorded at different exercise condition. Especially, focused on the recognition of heart sound recorded after high intensity exercise workload. The algorithm proposed consisted of two correlative methods. The first was to recognize heart sound recorded at rest and after low intensity exercise workloads by probabilistic neural network and the second was to recognize heart sound recorded after high intensity exercise workloads based on the characteristic of heart sound. Both methods have two consecutive phases. Firstly, all peaks, including the peaks of both heart sounds and noise, are marked by a repetitive threshold detecting algorithm. Secondly, probabilistic neural network is employed to classify the peaks detected in the first phase into Si, S2, and noise. Finally, the performance of the algorithm was evaluated using 45 digital heart sound recordings including normal and abnormal heart sound, which were recorded at rest and after low intensity exercise workloads, and 28 digital heart sound recordings recorded after high intensity exercise workloads. The results showed that over 94% of heart sound samples were classified and recognized correctly. Moreover, the reasons for the wrong classification, of which omitting and misdetection are two main problems, are also discussed and solutions are proposed. So this method can be improved and refined in following studies. In conclusion, this algorithm is a reliable approach to detect and classify heart sounds, providing a solid basis for further heart sound analysis.

Algorithms↗
PubMed2005

[Design of multifocal visual electrophysiology examining system based on fast m-transform].

The multifocal visual electrophysiology is an objective and non-invasive method to examine visual function. It can examine many tiny areas of retina at the same time and have a particular advantage in diagnosing the early diseases of ocular fundus. This paper introduces the design of multifocal visual electrophysiology examining system. The system uses multidisplay technology exclusively owned by Windows 98 or above operating system to control graphics stimulator which generates specified hexagonal array. The white or black transition of each hexagon controlled by a binary m-sequence stimulates corresponding area of retina. The specified Burian-Allen electrode is used to extract the mixed signal originated from many areas of retina. The signal is amplified and A/D transformed. The computer separates the mixed signal through fast m-transform and gets the response of each area of retina. The fast m-transform is a fast computation of cross-correlations with binary m-sequences and can reduce cross-correlation computation to a single Fast Walsh Transform. So a lot of time can be saved.

Electrophysiology↗
PubMed2004

[Heart sound recognition algorithm based on mathematical morphology].

In this paper, a new method was put forward for automatic recognition of the first heart sounds (S1) and the second heart sounds (S2). After the original heart sound signal was preprocessed, the heart sound envelope was extracted by using the mathematical morphology. Then on the heart sound envelope, S1 and S2 were recognized. Eighty heart sound samples collected were used for testing the algorithm. The accuracy of recognition was 86%, and was 100% for the normal heart sound. The result showed that the algorithm proposed in this paper had high performance, which could be used as a basis for further analysis of heart sound.

Algorithms↗
PubMed2002

A relative value method for measuring and evaluating cardiac reserve.

BACKGROUND: Although a very close relationship between the amplitude of the first heart sound (S1) and the cardiac contractility have been proven by previous studies, the absolute value of S1 can not be applied for evaluating cardiac contractility. However, we were able to devise some indicators with relative values for evaluating cardiac function. METHODS: Tests were carried out on a varied group of volunteers. Four indicators were devised: (1) the increase of the amplitude of the first heart sound after accomplishing different exercise workloads, with respect to the amplitude of the first heart sound (S1)recorded at rest was defined as cardiac contractility change trend (CCCT). When the subjects completed the entire designed exercise workload (7000 J), the resulting CCCT was defined as CCCT(1); when only 1/4 of the designed exercise workload was completed, the result was defined as CCCT(1/4). (2) The ratio of S1 amplitude to S2 amplitude (S1/S2). (3) The ratio of S1 amplitude at tricuspid valve auscultation area to that at mitral auscultation area T1/M1 (4) the ratio of diastolic to systolic duration (D/S). Data were expressed as mean +/- SD. RESULTS: CCCT(1/4) was 6.36 +/- 3.01 (n = 67), CCCT(1) was 10.36 +/- 4.2 (n = 33), S1/S2 was 1.89 +/- 0.94 (n = 140), T1/M1 was 1.44 +/- 0.99 (n = 144), and D/S was 1.68 +/- 0.27 (n = 172). CONCLUSIONS: Using indicators CCCT(1/4) and CCCT(1) may be beneficial for evaluating cardiac contractility and cardiac reserve mobilization level, S1/S2 for considering the factor for hypotension, T1/M1 for evaluating the right heart load, and D/S for evaluating diastolic cardiac blood perfusion time.

Adolescent↗