Isolation of Fetal ECG Signals from Abdominal ECG Using Wavelet Analysis

ObjectiveMonitoring the heartbeat of the fetus during pregnancy is a vital part in determining their health. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The demand for a reliable method of non-invasive fetal heart monitoring is of high importance.MethodElectrocardiogram (ECG) is a method of monitoring the electrical activity produced by the heart. The extraction of the fetal ECG (FECG) from the abdominal ECG (AECG) is challenging since both ECGs of the mother and the baby share similar frequency components, adding to the fact that the signals are corrupted by white noise. This paper presents a method of FECG extraction by eliminating all other signals using AECG. The algorithm is based on attenuating the maternal ECG (MECG) by filtering and wavelet analysis to find the locations of the FECG, and thus isolating them based on their locations. Two signals of AECG collected at different locations on the abdomens are used. The ECG data used contains MECG of a power of five to ten times that of the FECG.ResultsThe FECG signals were successfully isolated from the AECG using the proposed method through which the QRS complex of the heartbeat was conserved, and heart rate was calculated. The fetal heart rate was 135 bpm and the instantaneous heart rate was 131.58 bpm. The heart rate of the mother was at 90 bpm with an instantaneous heart rate of 81.9 bpm.ConclusionThe proposed method is promising for FECG extraction since it relies on filtering and wavelet analysis of two abdominal signals for the algorithm. The method implemented is easily adjusted based on the power levels of signals, giving it great ease of adaptation to changing signals in different biosignals applications.     

Tutorial. Surface EMG detection,conditioning and pre-processing: Best practices

This tutorial is aimed primarily to non-engineers, using or planning to use surface electromyography (sEMG) as an assessment tool for muscle evaluation in the prevention, monitoring, assessment and rehabilitation fields. The main purpose is to explain basic concepts related to: (a) signal detection (electrodes, electrode–skin interface, noise, ECG and power line interference), (b) basic signal properties, such as amplitude and bandwidth, (c) parameters of the front-end amplifier (input impedance, noise, CMRR, bandwidth, etc.), (d) techniques for interference and artifact reduction, (e) signal filtering, (f) sampling and (g) A/D conversion, These concepts are addressed and discussed, with examples.The second purpose is to outline best practices and provide general guidelines for proper signal detection, conditioning and A/D conversion, aimed to clinical operators and biomedical engineers. Issues related to the sEMG origin and to electrode size, interelectrode distance and location, have been discussed in a previous tutorial. Issues related to signal processing for information extraction will be discussed in a subsequent tutorial.     

心电、呼吸信号采集分析系统的研制

设计一种用于移动监护系统的生理信息采集及预处理装置。该装置以ARM为核心,包括低功耗的双路心电信号放大、滤波、抗基线漂移电路。实现了心电信号的采集、预处理、简单分析及从心电信号中提取呼吸信号等功能。     

Adaptive filtering for ECG rejection from surface EMG recordings.

Surface electromyograms (EMG) of back muscles are often corrupted by electrocardiogram (ECG) signals. This noise in the EMG signals does not allow to appreciate correctly the spectral content of the EMG signals and to follow its evolution during, for example, a fatigue process. Several methods have been proposed to reject the ECG noise from EMG recordings, but seldom taking into account the eventual changes in ECG characteristics during the experiment. In this paper we propose an adaptive filtering algorithm specifically developed for the rejection of the electrocardiogram corrupting surface electromyograms (SEMG). The first step of the study was to choose the ECG electrode position in order to record the ECG with a shape similar to that found in the noised SEMGs. Then, the efficiency of different algorithms were tested on 28 erector spinae SEMG recordings. The best algorithm belongs to the fast recursive least square family (FRLS). More precisely, the best results were obtained with the simplified formulation of a FRLS algorithm. As an application of the adaptive filtering, the paper compares the evolutions of spectral parameters of noised or denoised (after adaptive filtering) surface EMGs recorded on erector spinae muscles during a trunk extension. The fatigue test was analyzed on 16 EMG recordings. After adaptive filtering, mean initial values of energy and of mean power frequency (MPF) were significantly lower and higher respectively. The differences corresponded to the removal of the ECG components. Furthermore, classical fatigue criteria (increase in energy and decrease in MPF values over time during the fatigue test) were better observed on the denoised EMGs. The mean values of the slopes of the energy-time and MPF-time linear relationships differed significantly when established before and after adaptive filtering. These results account for the efficacy of the adaptive filtering method proposed here to denoise electrophysiological signals.     

Real time microprocessor-based 50 Hz notch filter for ECG

High performance analogue notch filters are difficult to realize in practice. Their real time digital counterparts, when implemented on an inexpensive microprocessor with no additional hardware, also have limitations of their own. To overcome these limitations, we have developed a new type of 50 Hz notch filter with its poles close to the zeros of the transfer function 1 + Z^−N. This new type of digital notch filter can be used for suppression of 50 Hz noise in the ECG. The filter is simple to design and easy to implement on most 8-bit microprocessors. It has a high execution speed, low analogue to digital noise, low recursive noise and good frequency response with no overshoot or ringing. It is capable of suppressing 50 Hz noise by at least 40 db. Its finite bandwidth of 4 Hz causes about 2% attenuation on the QRS peak, which is acceptable for almost all practical applications. One possible drawback is that multiple notches occur at higher frequencies. However, this has hardly any effect on the ECG because of the limited notch bandwidth.     

Empirical mode decomposition based filtering techniques for power line interference reduction in electrocardiogram using various adaptive structures and subtraction methods

Electrocardiogram (ECG) is a vital sign monitoring measurement of the cardiac activity. One of the main problems in biomedical signals like electrocardiogram is the separation of the desired signal from noises caused by power line interference, muscle artifacts, baseline wandering and electrode artifacts. Different types of digital filters are used to separate signal components from unwanted frequency ranges. Adaptive filter is one of the primary methods to filter, because it does not need the signal statistic characteristics. In contrast with Fourier analysis and wavelet methods, a new technique called EMD, a fully data-driven technique is used. It is an adaptive method well suited to analyze biomedical signals. This paper foregrounds an empirical mode decomposition based two-weight adaptive filter structure to eliminate the power line interference in ECG signals. This paper proposes four possible methods and each have less computational complexity compared to other methods. These methods of filtering are fully a signal-dependent approach with adaptive nature, and hence it is best suited for denoising applications. Compared to other proposed methods, EMD based direct subtraction method gives better SNR irrespective of the level of noises.     

A hybrid on-line ECG segmenting system for long-term monitoring.

This paper introduces a new hybrid ECG beat segmenting system, which can be applied in the processing unit of single-channel, long-term ECG monitors for the on-line segmentation of the ECG signal. Numerous ECG segmentation techniques are already existing and applied, however sufficiently robust and reliable methods currently require more than one ECG signal channel and quite complex computations, which are practically not feasible in stand-alone, low-cost monitors. Our new system approach presents a time domain segmentation technique based on a priori physiological and morphological information of the ECG beat. The segmentation is carried out after classifying the ECG beat, using the linear approximation of the filtered ECG signal and considering the pathophysiological properties as well. The proposed algorithms require moderate computational power, allowing the practical realization in battery powered stand-alone long-term cardiac monitors or small-sized cardiac defibrillators. The prototype version of the system was implemented in Matlab. The test and evaluation of the system was carried out with the help of reference signal databases.     

Simulink-based HW/SW codesign of embedded neuro-fuzzy systems

We propose a semi-automatic HW/SW codesign flow for low-power and low-cost Neuro-Fuzzy embedded systems. Applications range from fast prototyping of embedded systems to high-speed simulation of Simulink models and rapid design of Neuro-Fuzzy devices. The proposed codesign flow works with different technologies and architectures (namely, software, digital and analog). We have used The Mathworks' Simulink environment for functional specification and for analysis of performance criteria such as timing (latency and throughput), power dissipation, size and cost. The proposed flow can exploit trade-offs between SW and HW as well as between digital and analog implementations, and it can generate, respectively, the C, VHDL and SKILL codes of the selected architectures.     

High avidity cytokine autoantibodies in health and disease: Pathogenesis and mechanisms

Numerous reports have documented the presence of autoantibodies working against naturally occurring cytokines in humans in health and disease. In most instances, their physiological and pathophysiological significance remains unknown. However, recent advances in the methodologies for detecting cytokine autoantibodies and their application in research focused on specific disorders have shown that some cytokine autoantibodies play an important role in the pathogenesis of disease. Additionally, levels of cytokine autoantibodies may also correlate with disease severity and progression in certain infectious and autoimmune diseases but not in others. This suggests that cytokine-specific pathogenic differences exist. While multiple lines of evidence support the notion that high avidity cytokine autoantibodies are present and likely to be ubiquitous in healthy individuals, their potential physiological role, if any, is less clear. It is believed that they may function by scavenging pro-inflammatory cytokines and thereby inhibiting deleterious ‘endocrine’ effects, or by serving as carrier proteins, providing a ‘reservoir’ of inactive cytokines and thus modulating cytokine bioactivity. A central hypothesis is that sustained or repeated high-level exposure to cytokines triggers defects in T-cell tolerance, resulting in the expansion of existing cytokine autoantibody-producing B cells.     

MultiSig: a new high-precision approach to the analysis of complex biomolecular systems

MultiSig is a newly developed mode of analysis of sedimentation equilibrium (SE) experiments in the analytical ultracentrifuge, having the capability of taking advantage of the remarkable precision (~0.1 % of signal) of the principal optical (fringe) system employed, thus supplanting existing methods of analysis through reducing the ‘noise’ level of certain important parameter estimates by up to orders of magnitude. Long-known limitations of the SE method, arising from lack of knowledge of the true fringe number in fringe optics and from the use of unstable numerical algorithms such as numerical differentiation, have been transcended. An approach to data analysis, akin to ‘spatial filtering’, has been developed, and shown by both simulation and practical application to be a powerful aid to the precision with which near-monodisperse systems can be analysed, potentially yielding information on protein-solvent interaction. For oligo- and poly-disperse systems the information returned includes precise average mass distributions over both cell radial and concentration ranges and mass-frequency histograms at fixed radial positions. The application of MultiSig analysis to various complex heterogenous systems and potentially multiply-interacting carbohydrate oligomers is described.     

Frequency analysis of atrial fibrillation from the surface electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy.     

Evaluation of some nonrecursive digital filters for signals of auditory evoked responses

Auditory brain stem evoked responses are routinely used in audiology and otoneurology. Because recordings include more or less noise, the signals of evoked responses need digital filtering to suppress the noise. Nonrecursive digital filters are often the best since they can be organized to have no phase shift, which is essential in order not to distort sensitive parameters, as latency, in evoked responses. We have studied effects of some nonrecursive digital filters on the latency parameters of evoked responses. It turned out that digital filtering can have considerable influence on latencies, and thus the choice of appropriate filters is crucial.     

The Sensorium: Psychophysiological Evaluation of Responses to a Multimodal Neurofeedback Environment

The Sensorium is a multimodal neurofeedback environment that reflects a person’s physiological state by presenting physiological signals via orchestral sounds from a speaker and multi-coloured lights projected onto a white surface. The software manages acquisition, real-time processing, storage, and sonification of various physiological signals such as the electroencephalogram (EEG) or electrocardiogram (ECG). Each of the 36 participants completed 6 interventional conditions consisting of three different Sensorium-phases with EEG and ECG feedback, a mindfulness meditation, a guided body scan exercise, and a Pseudo-Sensorium using pre-recorded data that did not reflect the subject’s own physiology. During all phases EEG, ECG, skin conductance, and respiration were recorded. A feedback questionnaire assessed the participants’ subjective reports of changes in well-being, perception, and life-spirit. The results indicate that the Sensorium sessions were not statistically inferior compared to their corresponding active control conditions with respect to improvements in subjective reports concerning well-being and perception. Additionally, the Sensorium was rated as being a more extraordinary experience, as compared to meditation. During the Sensorium conditions the EEG showed lower levels of theta2 (7–8.5 Hz), alpha (9–12 Hz) and beta (12.5–25 Hz) activity. Since participants reported benefit from the Sensorium experience regardless of any prior experience with meditation, we propose this novel method of meditative and extraordinary self-experience to be utilized as a modern alternative to more traditional forms of meditation.     

Processing of multi-dimensional NMR data with the new software PROSA

Summary The new program PROSA is an efficient implementation of the common data-processing steps for multi-dimensional NMR spectra. PROSA performs linear prediction, digital filtering, Fourier transformation, automatic phase correction, and baseline correction. High efficiency is achieved by avoiding disk storage of intermediate data and by the absence of any graphics display, which enables calculation in the batch mode and facilitates porting PROSA on a variety of different computer systems; including supercomputers. Furthermore, all time-consuming routines are completely vectorized. The elimination of a graphics display was made possible by the use of a new, reliable automatic phase-correction routine. CPU times for complete processing of a typical heteronuclear three-dimensional NMR data set of a protein vary between less than 1 min on a NEC SX3 supercomputer and 40 min on a Sun-4 computer system.     

便携式心电监护中实时信号处理方法研究

针对心电信号处理过程中的心电信号数字滤波、心电波形的动态显示、心电数据存储等问题,阐述了3个可用于心电信号实时处理的方法：一是运用滤波器频谱的周期性减少了滤波器系数个数,提高了运算速度,并根据卷积公式特点实现了数字滤波的实时性;二是运用基于内存虚拟屏幕技术实现心电波形动态显示,解决了屏幕闪烁和绘图不连续问题;三是采用嵌入式数据库SQLITE实现了心电数据存储。所有方法均考虑实时性要求,并已成功用于课题组开发的便携式心电监护仪,效果较为理想,具有很强的实用价值。     

On models and muddles of heritability

One reason for the astonishing persistence of the IQ myth in the face of overwhelming prior and posterior odds against it may be the unbroken chain of excessive heritability claims for ‘intelligence’, which IQ tests are supposed to ‘measure’. However, if, as some critics insist, ‘intelligence’ is undefined, and Spearman's g is beset with numerous problems, not the least of which is universal rejection of Spearman's model by the data, then how can the heritability of ‘intelligence’ exceed that of milk production of cows and egg production of hens? The thesis of the present review paper is that the answer to this riddle has two parts: (a) the technical basis of heritability claims for human behavior is just as shaky as that of Spearman's g. For example, a once widely used ‘heritability estimate’ turns out to be mathematically invalid, while another such estimate, though mathematically valid, never fits any data; and (b) valid technical criticisms of flawed heritability claims typically are met with stubborn editorial resistance in the main stream journals, which tends to calcify such misinformation.     

Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks

BackgroundElectrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG recordings is not practical for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to develop computer-aided-diagnosis (CAD) systems to automatically identify arrhythmias.MethodsThis paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The proposed approach identifies arrhythmia classes using Convolutional Neural Network (CNN) trained by two-dimensional (2D) ECG beat images. Firstly, ECG signals, which consist of 5 different arrhythmias, are segmented into heartbeats which are transformed into 2D grayscale images. Afterward, the images are used as input for training a new CNN architecture to classify heartbeats.ResultsThe experimental results show that the classification performance of the proposed approach reaches an overall accuracy of 99.7%, sensitivity of 99.7%, and specificity of 99.22% in the classification of five different ECG arrhythmias. Further, the proposed CNN architecture is compared to other popular CNN architectures such as LeNet and ResNet-50 to evaluate the performance of the study.ConclusionsTest results demonstrate that the deep network trained by ECG images provides outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Moreover, the proposed method has lower computational costs compared to existing methods and is more suitable for mobile device-based diagnosis systems as it does not involve any complex preprocessing process. Hence, the proposed approach provides a simple and robust automatic cardiac arrhythmia detection scheme for the classification of ECG arrhythmias.     

Real-time software methodologies: Are they suitable for developing Manufacturing control software?

Computer-Integrated Manufacturing (CIM) systems may be classified as real-time systems. Hence, the applicability of methodologies that are developed for specifying, designing, implementing, testing, and evolving real-time software is investigated in this article. The paper highlights the activities of the software development process. Among these activities, a great emphasis is placed on automating the software requirements specification activity, and a set of formal models and languages for specifying these requirements is presented. Moreover, a synopsis of the real-time software methodologies that have been implemented by the academic and industrial communities is presented together with a critique of the strengths and weaknesses of these methodologies. The possible use of the real-time methodologies in developing the control software of efficient and dependable manufacturing systems is explored. In these systems, efficiency is achieved by increasing the level of concurrency of the operations of a plan, and by scheduling the execution of these operations with the intent of maximizing the utilization of the devices of their systems. On the other hand, dependability requires monitoring the operations of these systems. This monitoring activity facilitates the detection of faults that may occur when executing the scheduled operations of a plan, recovering from these faults, and, whenever feasible, resuming the original schedule of the system. The paper concludes that the set of surveyed methodologies may be used to develop the real-time control software of efficient and dependable manufacturing systems. However, an integrated approach to planning, scheduling, and monitoring the operations of these systems will significantly enhance their utility, and no such approach is supported by any of these methodologies.     

Biosupramolecular networks: Taking inspiration from nature to create powerful synthetic platforms

Nature is predicated on the ability to process large number of parallel signals to produce specific downstream outputs. Biosupramolecular networks are beginning to allow such processing power in synthetic systems, particularly through harnessing the recognition power of biomolecules. Such systems can be summarised through the reductionist view of containing inputs, circuitry motifs and functional outputs, with each of these elements able to be readily combined in a modular approach. Through the inherent ‘plug and play’ nature of these systems the field continues to rapidly expand, providing a wealth of new smart diagnostic and therapeutic systems.     

Acute Stress Modulates Feedback Processing in Men and Women: Differential Effects on the Feedback-Related Negativity and Theta and Beta Power

Sex-specific prevalence rates in mental and physical disorders may be partly explained by sex differences in physiological stress responses. Neural networks that might be involved are those underlying feedback processing. Aim of the present EEG study was to investigate whether acute stress alters feedback processing, and whether stress effects differ between men and women. Male and female participants performed a gambling task, in a control and a stress condition. Stress was induced by exposing participants to a noise stressor. Brain activity was analyzed using both event-related potential and time-frequency analyses, measuring the feedback-related negativity (FRN) and feedback-related changes in theta and beta oscillatory power, respectively. While the FRN and feedback-related theta power were similarly affected by stress induction in both sexes, feedback-related beta power depended on the combination of stress induction condition and sex. FRN amplitude and theta power increases were smaller in the stress relative to the control condition in both sexes, demonstrating that acute noise stress impairs performance monitoring irrespective of sex. However, in the stress but not in the control condition, early lower beta-band power increases were larger for men than women, indicating that stress effects on feedback processing are partly sex-dependent. Our findings suggest that sex-specific effects on feedback processing may comprise a factor underlying sex-specific stress responses.