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Biomedical subjects

Baoming Wu

Publications and source records attributed to Baoming Wu.

8 recordsLinked to original sources
PubMed2006

[A quality controllable algorithm for ECG compression based on wavelet transform and ROI coding].

This paper presents an ECG compression algorithm based on wavelet transform and region of interest (ROI) coding. The algorithm has realized near-lossless coding in ROI and quality controllable lossy coding outside of ROI. After mean removal of the original signal, multi-layer orthogonal discrete wavelet transform is performed. Simultaneously,feature extraction is performed on the original signal to find the position of ROI. The coefficients related to the ROI are important coefficients and kept. Otherwise, the energy loss of the transform domain is calculated according to the goal PRDBE (Percentage Root-mean-square Difference with Baseline Eliminated), and then the threshold of the coefficients outside of ROI is determined according to the loss of energy. The important coefficients, which include the coefficients of ROI and the coefficients that are larger than the threshold outside of ROI, are put into a linear quantifier. The map, which records the positions of the important coefficients in the original wavelet coefficients vector, is compressed with a run-length encoder. Huffman coding has been applied to improve the compression ratio. ECG signals taken from the MIT/BIH arrhythmia database are tested, and satisfactory results in terms of clinical information preserving, quality and compress ratio are obtained.

Algorithms↗
PubMed2005

[Application of CWT to extract characteristic monitoring parameters during spine surgery].

It is necessary to monitor intraoperative spinal function in order to prevent spinal neurological deficit during spine surgery. This study aims to extract characteristic electrophysiological monitoring parameters during surgical treatment of scoliosis. The problem, "the monitoring parameters in time domain are of great variability and are sensitive to noise", may also be solved in this study. By use of continuous wavelet transform to analyze the intraoperative cortical somatosensory evoked potential (CSEP), three new characteristic monitoring parameters in time-frequency domain (TFD) are extracted. The results indicate that the variability of CSEP characteristic parameters in TFD is lower than the variability of those in time domain. Therefore, the TFD characteristic monitoring parameters are more stable and reliable parameters of latency and amplitude in time domain. The application of TFD monitoring parameters during spine surgery may avoid spinal injury effectively.

Adolescent↗
PubMed2004

[Experimental study on brain-computer interface based on visual evoked potentials].

Brain-computer interface is a novel EEG-based communication and control system between human brain and computer or some other electric device, which has found important applications in many fields such as rehabilitation engineering. Study has been done in brain-computer interface using visual evoked potentials(VEP). Multiple stimulation patterns were produced on the computer screen through programming. Several flickering blocks were adopted for representing a number of possible selections, and when the subject was fixing his(her) eyes on an object on the screen, the very object could be distinguished by the analysis of the VEPs. Surface electrodes were placed at Oz of the inion and Cz to collect VEP. The wavelet filter and averaging method was used to extract VEP signal. Off-line experimental data analysis indicated that the proposed method may be valuable for developing real brain-computer interface with relatively high accuracy and speed, and the information transfer rates could be higher than 30 bit/min when 12 selections were on the screen.

Brain↗
PubMed2003

[The development of a pocket blood pressure holter based on handhold personal computer].

This paper introduces a kind of pocket blood pressure holter based on handhold personal computer. The measurement of blood pressure is controlled by ultralow power consumption MCU. The system configures interface circuit with handhold personal computer. The BP data can be stored, displayed, analysed and transmitted by handhold personal computer. The measurement parameters can be modified. The hardware is made up of three units: simulation circuit unit, intelligent control unit and interface circuit unit. Every unit structure and its principle are presented. The software of obtaining the BP data and communication with handhold personal computer are also illuminated. The system possesses the characters of portability, intelligence, convenience, high precision, ultralow power consumption and stable capability. It is possible to become an ideal blood pressure monitor in family.

Blood Pressure Monitoring, Ambulatory↗
PubMed2003

[Detection of surface EMG signal using active electrode].

Research of surface electromyogram(EMG) signal is important in rehabilitation medicine, sport medicine and clinical diagnosis, accurate detection of signal is the base of quantitative analysis of surface EMG signal. In this article were discussed how to reduce possible noise in the detection of surface EMG. Considerations on the design of electrode unit were presented. Instrumentation amplifier AD620 was employed to design a bipolar active electrode for use in surface EMG detection. The experiments showed that active electrode could be used to improve signal/noise ratio, reduce noise and detect surface EMG signal effectively.

Electrodes↗
PubMed2003

[Introduction to medical data mining].

Modern medicine generates a great deal of information stored in the medical database. Extracting useful knowledge and providing scientific decision-making for the diagnosis and treatment of disease from the database increasingly becomes necessary. Data mining in medicine can deal with this problem. It can also improve the management level of hospital information and promote the development of telemedicine and community medicine. Because the medical information is characteristic of redundancy, multi-attribution, incompletion and closely related with time, medical data mining differs from other one. In this paper we have discussed the key techniques of medical data mining involving pretreatment of medical data, fusion of different pattern and resource, fast and robust mining algorithms and reliability of mining results. The methods and applications of medical data mining based on computation intelligence such as artificial neural network, fuzzy system, evolutionary algorithms, rough set, and support vector machine have been introduced. The features and problems in data mining are summarized in the last section.

Algorithms↗
PubMed2003

[Advance in signal analysis of auditory evoked potential].

Signal analysis of the auditory evoked potential (AEP) has been the interesting field in biomedicine and signal processing for a long time. Recent advances in the analytic method of AEP are summed up in this paper. Seven methods, including spectral analysis, adaptive filtering, wavelet transform, chaos theory, artificial neutral network, regularization, and independent component analysis are mainly introduced. All of them are promising for extracting the signal with less stimulation and adequate quality, and for displaying their efficient application in the analysis of AEP.

Electroencephalography↗