Detection and Processing Techniques of FECG Signal for Fetal Monitoring

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system

An electrooptical muscle contraction sensor

An electrooptical sensor for the detection of muscle contraction is described. Infrared light is injected into the muscle, the backscattering is observed, and the contraction is detected by measuring the change, that occurs during muscle contraction, between the light scattered in the direction parallel and perpendicular to the muscle cells. With respect to electromyography and to optical absorption-based sensors, our device has the advantage of lower invasiveness, of lower sensitivity to electromagnetic noise and to movement artifacts, and of being able to distinguish between isometric and isotonic contractions

Radio frequency identification evolution of reader and antenna circuit design

Radio Frequency Identification (RFID) has become and will continue to be very important in the area of automatic identification. The purpose of this. article is to discuss the various circuit designs of two RFID components - the antenna and the reader - performed by various individuals and organizations. The reader circuit design is more complex than for the antenna. It is found that limited information, for both antenna and reader circuit designs, has been published since most of it is proprietary. Therefore, this article provides various crucial design concepts of antenna and reader

Comparative study of segmentation and feature extraction method on finger movement

Myoelectric control prostheses fingers are a popular developing clinical option that offers an amputee person to control their artificial fingers by recognizing the contacting muscle residual informs of electromyography (EMG) signal. Lower performance of recognition system always has been the main problem in producing the efficient prostheses finger. This is due to the inefficiency of segmentation and feature extraction in EMG recognition system. This paper aims to compare the most used overlapping segmentation scheme and time domain feature extraction method in recognition system respectively. A literature review found that a combination of Hudgins and Root Mean Square (RMS) methods is a possible way of improving feature extraction. To proof this hypothesis, an experiment was conducted by using a dataset of ten finger movements that has been pre-processed. The performance measurement considered in this study is the classification accuracy. Based on the classification accuracy results for the three common overlapping segmentation schemes, the smaller the window size with larger increment windows produce better accuracy but it will degrade the computational time. For feature extraction, the proposed Hudgins with RMS feature showed an improvement of average accuracy for ten finger movements by 0.74 and 3 per cent compared to Hudgins and RMS alone. Future study should incorporate more advance classification accuracy to improve the study

Surface EMG Fatigue Analysis by Means of Homomorphic Deconvolution

In this paper we use homomorphic deconvolution to obtain the power spectrum of the motor unit action potential (MUAP) from the surface electromyography (sEMG) signal. This spectrum is then used to extract the parameters of a time-domain model of the MUAP itself, in particular its amplitude and time scale. The analysis of the extracted parameters leads to the estimation of cadence and muscle fatigue. The methodology is tested with a sEMG signal recorded during biceps curl exercises