It is a well-established fact that the electrical bioimpedance of a part of the human body can provide valuable information regarding physiological parameters of the human body, if the signal is correctly detected and interpreted. Accordingly, an efficient low-cost bio-electrical impedance measuring instrument was developed, implemented, and tested in this study. Primarily, it is based upon the low-cost component-level approach so that it can be easily used by researchers and investigators in the specific domain. The measurement setup of instrument was tested on adult human subjects to obtain the impedance signal of the forearm which is under investigation in this case. However, depending on the illness or activity under examination, the instrument can be used on any other part of the body. The current injected by the instrument is within the safe limits and the gain of the biomedical instrumentation amplifier is highly reasonable. The technique is easy and user-friendly, and it does not necessitate any special training, therefore it can be effectively used to collect bio-impedance data and interpret the findings for medical diagnostics. Moreover, in this paper, several existing methods and associated approaches have been extensively explored, with in-depth coverage of their working principles, implementations, merits, and disadvantages, as well as focused on other technical aspects. Lastly, the paper also deliberates upon the present status, future challenges and scope of various other possible bio-impedance methods and techniques.
Electrocardiogram (ECG) is a documentation of the electrical activities of the heart. It is used to identify a number of cardiac faults such as arrhythmias, AF etc. Quite often the ECG gets corrupted by various kinds of artifacts, thus in order to gain correct information from them, they must first be denoised. This paper presents a novel approach for the filtering of low frequency artifacts of ECG signals by using Complete Ensemble Empirical Mode Decomposition (CEED) and Neural Networks, which removes most of the constituent noise while assuring no loss of information in terms of the morphology of the ECG signal. The contribution of the method lies in the fact that it combines the advantages of both EEMD and ANN. The use of CEEMD ensures that the Neural Network does not get over fitted. It also significantly helps in building better predictors at individual frequency levels. The proposed method is compared with other state-of-the-art methods in terms of Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Correlation Coefficient. The results show that the proposed method has better performance as compared to other state-of-the-art methods for low frequency artifacts removal from EEG.
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