Abstract: The diagnosing of the heart abnormality can benefit from the availability of analyzing techniques for the electrocardiogram (ECG) signal. A system which is acquire ECG signal and extract the heart activity features has been designed and implemented in this paper. The implemented system includes four stages; signal acquiring stage, signal conditioning stage, transmission channel stage, and diagnosing stage. Three electrodes are placed on the left and right arm and left leg of the patient for acquiring ECG signal. The second stage is designed for the purpose of amplifying and filtering the acquired weak signal which is corrupted by noise from muscles and electrical source. The extracted analog ECG signal is converted to the digital signal in this stage also. In order to realize the functionality of this stage, instrumentation amplifier, high pass filter, notch filter, and low pass filter have been implemented to extracting analog ECG. Arduino is configured in this stage for converting the analog ECG signal into digital signal, it is also acts as a gateway to transfer the digital ECG to the transmission channel. Wireless transmission channel has been implemented based on Xbee located at both channel sides. Finally, the core of the diagnosing stage has been designed using Language VHDL language and implemented using Spartan 3AN XC3S700 FPGA Starter Kit board. The algorithm implemented in the diagnosing stage concerned with the detecting of R- peaks in ECG signal. According to a deviation in duration or amplitude of these peaks from the standard values, the diagnosing stage indicates one type of the heart abnormality which is appear at the LCD of FPGA. Experimental tests have been performed for different cases and show promising results toward precise diagnosis of heart irregular activities.
Keywords: ECG Circuit; Arduino; ZigBee; UART; VHDL; FPGA.
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Abstract: The present study mainly investigates the synthesis and growth of some novel bioactive Ni(II), Cu(II) and Zn(II) compounds (1–3) derived from 2–aminobenzamide (o–aminobenzamide: 2AB: L) and L–asparagine (asn: B). The synthesised compounds were structurally characterized by various physico–chemical and spectral (FTIR, UV–vis., TGA/DTA, XRD and SEM) studies. The micro elemental (C, H and N) analysis suggests that stoichiometry of the metal(II) compounds to be 1:1:1 (Metal: 2AB: asn). The observed low molar conductance values reveal their non–electrolytic nature and the observed electronic spectra coupled with magnetic moment values clearly indicate that the ligands 2AB (L) and asn (B) coordinate with metal(II) ions in tetradentate manner through amino–N & amido–O of (L) and deprotonated carboxylato–O & amino–N atoms of (B) to form a stable 6, 5 membered chelate ring. Powder X–ray diffractogram and SEM pictograph implies that all the compounds have well–defined nanocrystallinity with homogeneous morphology. All the derived compounds (1–3) show significant in vitro biological and antioxidant activities than the 2AB(L) and asn(B) in their free state and the activities go behind the order as Control >> CuLB >> NiLB ≈ ZnLB > 2AB(L) > asn(B).
Keywords: 2–Aminobenzamide, L–Asparagine, Spectral, XRD, SEM, Pharmacological study.Abbreviations:
2AB (L) = 2–Aminobenzamide or o–Aminobenzamide;
Asn (B) = L–Asparagine, AA = Ascorbic acid,
DPPH = 2, 2–Diphenyl–1–picrylhydrazyl,
SEM = Scanning Electron Micrography
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Abstract: Several techniques of anonymity, such as generalization and disruption, have been designed to protect privacy from the publication of micro-data. Recent work has shown that generalization loses much information, especially for high dimensional data. Bucketization, on the other hand, does not prevent the disclosure of membership and does not apply to data that do not have a clear separation between quasi-identifiable attributes and sensitive attributes. In this paper, we present a new technique called overlapping slice, which divides the data horizontally and vertically. We show that the overlap section preserves a better data utility than generalization and can be used for the protection of belonging to belonging. Another important advantage of the overlap slice is that it can handle large data storage.
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