Compressing the ECG signal is considered a feasible solution for supporting a system to manipulate the package size, a major factor leading to congestion in an ECG wireless network. Hence, this paper proposes a compression algorithm, called the advanced two-state algorithm, which achieves three necessary characteristics: a) flexibility towards all ECG signal conditions, b) the ability to adapt to each requirement of the package size and c) be simple enough. In this algorithm, the ECG pattern is divided into two categories: "complex" durations such as QRS complexes, are labeled as low-state durations, and "plain" durations such P or T waves, are labeled as high-state durations. Each duration type can be compressed at different compression ratios, and Piecewise Cubic Spline can be used for reconstructing the signal. For evaluation, the algorithm was applied to 48 records of the MIT-BIH arrhythmia database (clear PQRST complexes) and 9 records of the CU ventricular tachyarrhythmia database (unclear PQRST complexes). Parameters including Compression Ratio (CR), Percentage Root mean square Difference (PRD), Percentage Root mean square Difference, Normalized (PRDN), root mean square (RMS), Signal-to-noise Ratio (SNR) and a new proposed index called Peak Maximum Absolute Error (PMAE) were used to comprehensively evaluate the performance of the algorithm. Eventually, the results obtained were positive with low PRD, PRDN and PMAE at different compression ratios compared to many other loss-type compressing methods, proving the high efficiency of the proposed algorithm. All in all, with its extremely low-cost computation, versatility and good-quality reconstruction, this algorithm could be applied to a number of wireless applications to control package size and overcome congested situations.
One of the best challenges in healthy field in 21th century is diabetes because the number of patients is rising faster and faster. Prevention of complications of diabetes, we have to continuously monitor blood glucose level. In this paper, we introduce our research about non-invasive blood glucose measuring device which used infrared light 1550nm. Our goals are developing the non-invasive glucose measuring device, it is more convenient in measuring and following glucose index in patient's body. We can follow this index continuously, without pain and slow down problems about infectious diseases. Beside it, when using our device, the price is decreased because it does not use consumable supplies. But the accuracy of the measuring is still not high. We tested this device in living body of 2 volunteers in 10 days then compared the results of our device with those of invasive device used in the market, our device gives correlation coefficient from 0.870 to 0.995.
This article presents the research and application of android smart phones to support tele-monitoring cardiovascular disease. The smart phones are more flexible and easier to carry than laptops. According to a report from Newzoo, by November 2017, 75% of smartphones in the worldwide installed with Android operating system. In this paper, the authors designed a system that combined hardware with applications android smart phones software for measuring Electrocardiogram (ECG) signal. The results shown that the convenience and the effect of the application of android smart phones in monitoring cardiovascular disease in particular and healthcare in general, as android smartphone are widely used throughout the world.
Electrocardiographic (ECG) signals in measurements are often contaminated with different types of noises in which include baseline noise. In case of the frequency of baseline noise is greater or smaller than frequency of the ECG signal, it is easy to filter the baseline noise from ECG signal by using filtered methods in frequency domain. In contrast, if frequency of baseline noise and frequency of ECG signal are coincident, it is difficult to apply the frequency domain filters for baseline noise removal. In this paper, we introduce an approach to remove of baseline noise from ECG signal in time domain and evaluation the efficacy of the method based on Mean Square Error criteria. We have performed experiments with simulated ECG signal which including white noise, random and sinusoidal baseline noises. Throughout the experiment, we found that the errors of time domain filters depend on the amplitude of the base line noises.
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