Objective To design a high performance and low power consumption ECG signal acquisition system which can meet the demand for long time monitoring of the physiological status of patients.Methods The prototype system utilized low power ECG analog front end ADAS1000 and MSP430F5529 microcontroller to achieve configuration of AFE and back-reading of ECG data by SPI bus. Results This system implemented 24-hour dynamic ECG monitoring of patients in active state, and the data acquired were accurate and reliable.Conclusion The system realizes PCB integration, low power consumption, and can be used for battery powered portable application such as wearable devices.

OBJECTIVETo study on the effect of different processing methods on the contents of seven major constituents in wild and cultivated Paeonia lactiflora, gallic acid, catechin, albiflorin, paeoniflorin, pentagalloylglucose, benzoic acid and paeonol, in order to provide reference basis for different efficacy and formation mechanism of Paeonia Radix Rubra and Paeonia Radix Alba.

METHODWild and cultivated P. lactiflora were dealt with by four processing methods, direct drying, drying after boiling, drying after decorticating and boiling, and drying after boiling and decorticating. HPLC was use to simultaneously determine the contents of seven chemical constituents.

RESULTWild P. lactiflora showed notable higher content of paeoniflorin and catechin than cultivated P. lactiflora, whereas cultivated P. lactiflora showed higher content of albiflorin than wild P. lactiflora. Both of them were less affected by process methods in above three constituents. Drying after boiling, drying after decorticating and boiling, and drying after boiling and decorticating methods reduced the content of benzoic acid and paeonol to trace in both wild and cultivated P. lactiflora. Clustering analysis results showed that all processing methods assembled wild and cultivated P. lactiflora in 2 groups.

CONCLUSIONThe content differences of Paeonia Radix Rubra and Paeonia Radix Alba are mainly caused by their own differences and less affected by processing methods.

Acetophenones ; chemistry ; Benzoic Acid ; chemistry ; Chromatography, High Pressure Liquid ; Cluster Analysis ; Medicine, Chinese Traditional ; methods ; Paeonia ; chemistry

By combining with informatics theory, ta system model consisting of feature selection which is based on redundancy and correlation is presented to develop disease classification research with five gene data set (NCI, Lymphoma, Lung, Leukemia, Colon). The result indicates that this modeling method can not only reduce data management computation amount, but also help confirming amount of features, further more improve classification accuracy, and the application of this model has a bright foreground in fields of disease analysis and individual treatment project establishment.
Algorithms ; Artificial Intelligence ; Data Mining ; Gene Expression Profiling ; methods ; Informatics ; Neoplasms ; classification ; genetics

As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.
Electroencephalography ; Epilepsy/diagnosis* ; Humans ; Machine Learning ; Seizures/diagnosis* ; Signal Processing, Computer-Assisted