Brachial to finger pulse wave distortion due to wave reflection in arteries is almost identical in all subjects and can be modelled by a single resonance. The pressure decrement due to flow in arteries is greatest for high pulse pressures superimposed on low means.
Objective To develop a computerised system that will assist the early diagnosis of fetal hypoxia and to investigate the relationship between the fetal heart rate variability and the fetal pulse oximetry recordings. Design Retrospective off-line analysis of cardiotocogram and FSpO 2 recordings.Setting The Maternity Unit of the 2nd Department of Obstetrics and Gynaecology, Aretaieion Hospital, University of Athens. Population Sixty-one women of more than 37 weeks of gestation were monitored throughout labour.Methods Multiresolution wavelet analysis was applied in each 10-minute period of second stage of labour focussing on long term variability changes in different frequency ranges and statistical analysis was performed in the associated 10-minute FSpO 2 recordings. Self-organising map neural network was used to categorise the different 10-minute fetal heart rate patterns and the associated 10-minute FSpO 2 recordings. Main outcome measures Umbilical artery pH of 7.20 and Apgar score at 5 minutes of 7 formed the inclusion criteria of the risk group. Results After using k-means clustering algorithm, the two-dimensional output layer of the self-organising map neural network was divided into three distinct clusters. All the cases that mapped in cluster 3 belonged in the risk group except one. The sensitivity of the system was 83.3% and the specificity 97.9% for the detection of risk group cases. Conclusions A relationship between the fetal heart rate variability in different frequency ranges and the time in which FSpO 2 is less than 30% was noticed. Fetal pulse oximetry seems to be an important additional source of information. Computerised analysis of the fetal heart rate monitoring and pulse oximetry recordings is a promising technique in objective intrapartum diagnosis of fetal hypoxia. Further evaluation of this technique is mandatory to evaluate its efficacy and reliability in interpreting fetal heart rate recordings.
The formulation of a Personal Area Network (PAN), consisting of a wireless infrastructure of medical sensors, attached to patient's body, and a supervising device carried by them, lays the path for continuous and real-time monitoring of vital signs without discomforting the person in question. This infrastructure enhances the context of remote healthcare services by supporting flexible acquisition of crucial vital signs, while at the same time it provides more convenience to the patient. Aiming at the exploitation of the inherent features and requirements of wireless medical sensor networks, in this paper we focus on the main design guidelines of a low power Medium Access Control (MAC) protocol, designated to support a patient PAN. The proposed protocol intends to improve energy efficiency in such applications and thus is oriented towards the prevention of main energy wastage sources, such as collision, idle listening and power outspending.
Abstract. Early detection is the key to improve breast cancer prognosis. The only proven effective method of breast cancer early detection is mammography. An early sign of 30-50% of breast cancer is the appearance of clusters of fine, granular microcalcifications and 60-80% of breast carcinomas reveal microcalcification clusters upon histological examination. The high correlation between the appearance of the microcalcification clusters and diseases, proves that computer aided diagnosis (CAD) systems for automated classification of microcalcification clusters will be very useful and helpful for breast cancer control. The fuzzy nature of microcalcification, the low contrast and the low ability of distinguishing them from their surroundings make automated characterization of them extremely difficult. In this study, we give an overview of the currently available literature on characterization of malignant and benign microcalcifications. We compare and evaluate some of the classification algorithms on microcalcifications in mammograms used in various CAD systems, which are separated into categories according to the method in use. Neural networks are used in applications where only a few decisions are required concerning an amount of data. The K-nearest neighbour classifier distinguishes unknown patterns based on the similarity to known samples and the decision tree approach is much simpler than neural networks and does not need extensive knowledge of the probability distribution of the features.
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