Acoustic analysis is one of the important branches of biometric recognition technology widely used now. The mainly aim of the technology is to recognize the identity of person and judge the content of speech or diagnose the illness automatically according to the features extracted from the speaker's waveforms. All these features are related with the characteristics of speaker's physiological, pathological and psychological action. Speaker recognition study has its 50-year old history already, but acoustic analysis in diagnosing disease has been founded since 1970s. This paper introduces the main concept and research background of this diagnosing system generally and discusses the problems generated during processing. At last the prospect for the applications of acoustic analysis is forecasted.
Humans ; Pattern Recognition, Physiological ; Signal Detection, Psychological ; Speech ; physiology ; Speech Acoustics ; Speech Disorders ; diagnosis ; physiopathology

OBJECTIVES: Biosignal data captured by patient monitoring systems could provide key evidence for detecting or predicting critical clinical events; however, noise in these data hinders their use. Because deep learning algorithms can extract features without human annotation, this study hypothesized that they could be used to screen unacceptable electrocardiograms (ECGs) that include noise. To test that, a deep learning-based model for unacceptable ECG screening was developed, and its screening results were compared with the interpretations of a medical expert. METHODS: To develop and apply the screening model, we used a biosignal database comprising 165,142,920 ECG II (10-second lead II electrocardiogram) data gathered between August 31, 2016 and September 30, 2018 from a trauma intensive-care unit. Then, 2,700 and 300 ECGs (ratio of 9:1) were reviewed by a medical expert and used for 9-fold cross-validation (training and validation) and test datasets. A convolutional neural network-based model for unacceptable ECG screening was developed based on the training and validation datasets. The model exhibiting the lowest cross-validation loss was subsequently selected as the final model. Its performance was evaluated through comparison with a test dataset. RESULTS: When the screening results of the proposed model were compared to the test dataset, the area under the receiver operating characteristic curve and the F1-score of the model were 0.93 and 0.80 (sensitivity = 0.88, specificity = 0.89, positive predictive value = 0.74, and negative predictive value = 0.96). CONCLUSIONS: The deep learning-based model developed in this study is capable of detecting and screening unacceptable ECGs efficiently.
Dataset ; Electrocardiography ; Humans ; Learning ; Mass Screening ; Monitoring, Physiologic ; Noise ; ROC Curve ; Sensitivity and Specificity ; Signal Detection, Psychological

Vigilance is the body level of awareness for objective things. It has been used in security, medical and other fields since people used it as an objective indicator. Therefore automatical vigilance detection has become a major issue needed to be resolved as soon as possible. The methods of vigilance detection at home and abroad in recent years was analyzed in this paper, which will benefit the research and the people dedicated in vigilance detection.
Arousal ; physiology ; Awareness ; physiology ; Brain ; physiology ; Electrocardiography ; Electroencephalography ; Humans ; Neural Networks (Computer) ; Pulse ; Signal Detection, Psychological ; physiology ; Task Performance and Analysis

OBJECTIVETo observe the effects of Herba dendrobii on rats with stomach-heat syndrome and to explore the mechanisms.

METHODRats were fed with decoction of Rhizoma Zingiberis for 15 continuous days to induce the model of stomach-heat syndrome. After modeling, Herba Dendrobii (HD) decoction were given (in the doses of 1.5, 0.75 g x kg(-1) respectively) for 10 days. After treatment, amount of the daily diet, volume and absorbance of urine, pellet number and moistness of excrement, color and coating degree of tongue were recorded; the body thermal effects were detected with thermal texture maps (TTM) system; the biochemical indexes of blood reflecting the physiological function of stomach, including thromboxaneB2 (TXB2), 6-keto-prostaglandin F1alpha(6-keto-PGF1alpha), motilin (MTL), gastrin (Gas), somatostation (SS), interleukin-4 (IL-4) and interleukin-8 (IL-8) were measured by radio immunoassay; and the histological changes of gastric mucosa were observed by hematoxylin-eosin (HE) stain.

RESULTThe model rat had yellow coating and red tongues (P < 0.05). The amount of daily diet were increased (over 10%), urine volume and excrement pellet number were decreased (over 10%). The their urine color became deep (P < 0.01) and their excrement became dry. The temperatures in head, neck, left fore-armpit, chest, up-abdomen, mid-abdomen of the model rats were raised up (difference > 0.5 degrees C or difference > 1.0 degree C ). The content of 6-keto-PGF1alpha in blood of model rats decreased evidently (P < 0.01), and the contents of MTL, Gas and IL-8 increased conspicuously (P < 0.01). The histological changes of gastric mucosa in the model rats were as follows: diffuse congestion, infiltration of neutrophil, less secretion, decrease of the number of chief and parietal cells, etc (P < 0. 05 or P < 0.01). After treatment with HD, except the daily food weight, the temperatures in head, neck and chest, the content of MTL and the number of chief cells, the other indexes observed above were improved noticeably (difference > 0.5 RC or difference > 1.0 degree C, P < 0.05 or P < 0.01).

CONCLUSIONThe reason why HD relieves the general symptom and sign the gastric mucosa of rats with stomach-heat syndrome is that HD can increase 6-keto-PGF1alpha and decrease IL-8, Gas, TXB2 in their blood.

Animals ; Drugs, Chinese Herbal ; adverse effects ; therapeutic use ; Gastric Dilatation ; drug therapy ; metabolism ; Gastrins ; Interleukin-4 ; metabolism ; Interleukin-8 ; metabolism ; Male ; Motilin ; metabolism ; Prostaglandins ; metabolism ; Rats ; Rats, Sprague-Dawley ; Signal Detection, Psychological ; Signal Transduction ; drug effects ; Stomach Diseases ; drug therapy ; metabolism ; Syndrome ; Thromboxanes ; metabolism