This paper describes a portable diagnostic telecardiology system, aimed to benefit the rural people of a third world country like India. The designed system consists of two major blocks; the first one is required to b...
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ISBN:
(纸本)9781424431342;0769527604
This paper describes a portable diagnostic telecardiology system, aimed to benefit the rural people of a third world country like India. The designed system consists of two major blocks; the first one is required to be carried to the patient home, named 'Portable Telecardiology Kit'. The second one, named 'Automated Cardiac Signal Processor', a PC based system, to be permanently placed at the nearest rural health care center. The 'Portable Telecardiology Kit' contains a portable ECG machine, a dedicated microcontroller-based full duplex communication system, both interfaced, in a single enclosure. This kit will convert the ECG signal along with a tag page (containing different information regarding patient's health and history of illness) to a digitized serial electromagnetic wave. The signal may be transmitted over a distance of about 7-10 km through a cordless phone under full duplex mode. The PC-based system, after receiving the transmitted signal from patient site, extract the ECG signal after proper filtering, and then pass the signal through an in-built knowledge-base. Finally, a rule-based rough set decision system is generated for the development of an inference engine for disease identification from these time-plane features. The system will generate a report indicating the preliminary level abnormalities, and the precautions that can be adopted at an early stage. The entire report is then sent to the Portable kit at the patient bed-site.
In order to help understand how the genes are affected by different disease conditions in a biological system, clustering is typically performed to analyze gene expression data. In this paper, we propose to solve the ...
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In order to help understand how the genes are affected by different disease conditions in a biological system, clustering is typically performed to analyze gene expression data. In this paper, we propose to solve the clustering problem using a graph theoretical approach, and apply a novel graph partitioning model -isoperimetric graph partitioning (IGP), to group biological samples from gene expression data. The IGP algorithm has several advantages compared to the well-established spectral graph partitioning (SGP) model. First, IGP requires a simple solution to a sparse system of linear equations instead of the eigen-problem in the SGP model. Second, IGP avoids degenerate cases produced by spectral approach to achieve a partition with higher accuracy. Moreover, we integrate unsupervised gene selection into the proposed approach through two-way ordering of gene expression data, such that we can eliminate irrelevant or redundant genes in the data and obtain an improved clustering result. We evaluate our approach on several well-known problems involving gene expression profiles of colon cancer and leukemia subtypes. Our experiment results demonstrate that IGP constantly outperforms SGP and produces a better result that is closer to the original labeling of sample sets provided by domain experts. Furthermore, the clustering accuracy is improved significantly when IGP is integrated with the unsupervised gene (feature) selection.
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