A new arterial mechanical property indicator reflecting differences in invasive stimulus intensity induced by alteration of remifentanil concentration during laryngoscopy.

BACKGROUND: Reliable analgesia monitoring is not available for general anaesthesia cases. In 2003, we introduced a method to characterise arterial mechanical properties, which we termed arterial stiffness (K). However, it is unclear whether differences in K actually indicate changes in the intensity of a noxious stimulus. Thus, we examined the relationship between stress intensity and the value of K. METHODS: Thirty patients under general anesthesia were randomly divided into two remifentanil concentration groups (2 and 6 ng/mL). After a steady concentration of remifentanil was achieved for at least 3 minutes, laryngoscopy was performed. After completion of laryngoscopy, once the K value returned to near-baseline, laryngoscopy with endotracheal intubation was performed, and the value of K after the procedure was recorded and analyzed. RESULTS: In total, data were obtained for 28 of 30 patients. The values of K before the laryngoscopy were not significantly different between the groups (2 ng/mL group: 13.1 [8.5-33.1] mmHg/%; 6 ng/mL group: 11.6 [4.3-31.4] mmHg/%; P=0.53). After laryngoscopy, K was approximately 2 times greater in the 2 ng/mL group than in the 6 ng/mL group (39.0 [13.6-115.9] mmHg/% vs. 19.0 [5.5-85.1] mmHg/%, P=0.02). After intubation also, K was approximately 2 times greater in the 2 ng/mL group (52.0 [27.7-122.0] mmHg/% vs. 24.3 [7.2-94.9] mmHg/%, P=0.04). CONCLUSIONS: The value for arterial stiffness (K) non-proportionally changes in response to stimulus intensity; therefore, it has the potential to be used as an indicator of nociceptive stimulation intensity.

Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality.

Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

Xinjiang Uygur Medicine Image Feature Extraction and Discriminant Analysis Based on Color and Textural Features / 中国中医药信息杂志

Objective To extract Xinjiang Uyghur medicine image features and analyze the features; To investigate the image classification effect of the researched features; To find the suitable features for Xinjiang Uyghur medicine image classification; To lay the foundation for content-based medical image retrieval system of Xinjiang Uyghur medicine images.Methods The flowers and leaves of Xinjiang Uyghur medicine were treated as the research objects. First, images were under preprocessing. Then color and textural features were extracted as original features and statistics method was used to analyze the features. Maximum classification distance was used to analyze the main features obtained from image classification. At last, the classification ability of features was evaluated by Bayes discriminant analysis.Results Color and textural features were selected and classified. The correct classification rate of flower images was 85% and the correct classification rate of leaf images was 62%. The classification effect of flower images used by selected features was better than classification effect of original feature.Conclusion Compared with the classification of original features, the classification accuracy of flower medicine is higher through selected features. This research can lay a certain foundation for the further researches on Xinjiang Uyghur medicine images and the improvement of feature extraction methods.

[Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.

A log-linearized arterial viscoelastic model for evaluation of the carotid artery.

This paper proposes a method for qualitatively estimating the mechanical properties of arterial walls on a beat-to-beat basis through noninvasive measurement of continuous arterial pressure and arterial diameter using an ultrasonic device. First, in order to describe the nonlinear relationships linking arterial pressure waveforms and arterial diameter waveforms as well as the viscoelastic characteristics of arteries, we developed a second-order nonlinear model (called the log-linearized arterial viscoelastic model) to allow estimation of arterial wall viscoelasticity. Next, to verify the validity of the proposed method, the viscoelastic indices of the carotid artery were estimated. The results showed that the proposed model can be used to accurately approximate the mechanical properties of arterial walls. It was therefore deemed suitable for qualitative evaluation of arterial viscoelastic properties based on noninvasive measurement of arterial pressure and arterial diameter.

A novel online method to monitor autonomic nervous activity based on arterial wall impedance and heart rate variability.

This paper proposes a new method of evaluating autonomic nervous activity using the mechanical impedance of arterial walls and heart rate variability. The cardiovascular system is indispensable to life maintenance functions, and homeostasis is maintained by the autonomic nervous system. Accordingly, it is very important to be able to make diagnosis based on autonomic nervous activity within the body's circulation. The proposed method was evaluated in surgical operations; the mechanical impedance of the arterial wall was estimated from arterial blood pressure and a photoplethysmogram, and heart rate variability was estimated using electrocardiogram R-R interval spectral analysis. In this paper, we monitored autonomic nervous system activity using the proposed system during endoscopic transthoracic sympathetic block surgery in eight patients with hyperhidrosis. The experimental results indicated that the proposed system can be used to estimate autonomic nervous activity in response to events during operations.

Arterial mechanical impedance is a sensitive stress response monitor during general anesthesia.

Arterial mechanical impedance is a characteristic of arterial dynamics that is determined by photoplethysmographic amplitude and direct arterial pressure, while mechanical impedance is indicated by stiffness, viscosity, and inertia. We compared the parameters of mechanical impedance and photoplethysmographic amplitude to estimate the magnitude of stress response in patients undergoing general anesthesia by measuring direct arterial pressure. After sedating each patient with propofol, photoplethysmographic amplitude and mechanical impedance were measured as baseline control values, then 3 min after fentanyl administration (2 microg kg(-1)), the parameters were measured again as post-fentanyl values. Thereafter, a direct laryngoscopy procedure was performed and post-laryngoscopy values for the parameters were determined. The magnitudes of response to each event were compared by using the ratio of the measurements from the preceding event. Then, comparisons of the magnitudes were performed after coordinating each response in the same direction. Our results showed that both stiffness and viscosity of mechanical impedance had greater variations than photoplethysmographic amplitude. In conclusion, we propose stiffness and viscosity derived from arterial mechanical impedance as sensitive parameters to monitor stress responses during general anesthesia.