Advances in Digital Processing of Low-Amplitude Components of Electrocardiosignals

This manual has been published within the framework of the BME-ENA project under the responsibility of National Technical University of Ukraine. The BME-ENA “Biomedical Engineering Education Tempus Initiative in Eastern Neighbouring Area”, Project Number: 543904-TEMPUS-1-2013-1-GR-TEMPUS-JPCR is a Joint Project within the TEMPUS IV program. This project has been funded with support from the European Commission.Навчальний посібник присвячено розробці методів та засобів для неінвазивного виявлення та дослідження тонких проявів електричної активності серця. Особлива увага приділяється вдосконаленню інформаційного та алгоритмічного забезпечення систем електрокардіографії високого розрізнення для ранньої діагностики електричної нестабільності міокарда, а також для оцінки функціонального стану плоду під час вагітності. Теоретичні основи супроводжуються прикладами реалізації алгоритмів за допомогою системи MATLAB. Навчальний посібник призначений для студентів, аспірантів, а також фахівців у галузі біомедичної електроніки та медичних працівників.The teaching book is devoted to development and research of methods and tools for non-invasive detection of subtle manifistations of heart electrical activity. Particular attention is paid to the improvement of information and algorithmic support of high resolution electrocardiography for early diagnosis of myocardial electrical instability, as well as for the evaluation of the functional state of the fetus during pregnancy examination. The theoretical basis accompanied by the examples of implementation of the discussed algorithms with the help of MATLAB. The teaching book is intended for students, graduate students, as well as specialists in the field of biomedical electronics and medical professionals

Combined effects of prozac and hypothalmic mediated response on masseter muscle activity in the cat

The present study tested the hypothesis that infusion of Prozac would serve to suppress defensive rage elicited from the medial hypothalamus of the cat. Cats are known to exhibit certain kind of behavior, known as the defensive rage response such as unsheathing of the claws, retraction of the ear and vocalization (hissing). Three adult cats (2 males and 1 female) weighing (2.8 - 3.4 kg) were utilized during the experiments. Cannula-electrodes were implanted into the medial hypothalamus for elicitation of defensive rage behavior. EMG activity was recorded with a bipolar electrode attached to the masseter muscle to establish baseline before the infusion of the drug. Mean frequency values of early and late stimulation were calculated using the continuous wavelet transform. The effects of early stimulation of the medial hypothalamus upon response latencies were compared with those of the late stimulation. The results reveal an inhibitory effect that may be related to fatigue or other inhibitory structures in the brain. The mean frequency values of the early stimulation on average were significantly higher than the mean frequency values of the late stimulation (p 0.03). While baseline (preinjection) mean values among the three subjects during early and late stimulation were highly significant (p 0.003), there were no significant differences among post-injection mean values (p \u3e; 0.05). The findings suggest that infusion of the drug has some inhibitory influence on the masseteric EMG activity

Automatic signal and image-based assessments of spinal cord injury and treatments.

Spinal cord injury (SCI) is one of the most common sources of motor disabilities in humans that often deeply impact the quality of life in individuals with severe and chronic SCI. In this dissertation, we have developed advanced engineering tools to address three distinct problems that researchers, clinicians and patients are facing in SCI research. Particularly, we have proposed a fully automated stochastic framework to quantify the effects of SCI on muscle size and adipose tissue distribution in skeletal muscles by volumetric segmentation of 3-D MRI scans in individuals with chronic SCI as well as non-disabled individuals. We also developed a novel framework for robust and automatic activation detection, feature extraction and visualization of the spinal cord epidural stimulation (scES) effects across a high number of scES parameters to build individualized-maps of muscle recruitment patterns of scES. Finally, in the last part of this dissertation, we introduced an EMG time-frequency analysis framework that implements EMG spectral analysis and machine learning tools to characterize EMG patterns resulting in independent or assisted standing enabled by scES, and identify the stimulation parameters that promote muscle activation patterns more effective for standing. The neurotechnological advancements proposed in this dissertation have greatly benefited SCI research by accelerating the efforts to quantify the effects of SCI on muscle size and functionality, expanding the knowledge regarding the neurophysiological mechanisms involved in re-enabling motor function with epidural stimulation and the selection of stimulation parameters and helping the patients with complete paralysis to achieve faster motor recovery

Masseter muscle activity resulting from stimulation of hypothalamic behavioral sites : wavelet analysis

Patterns of electromyographic (EMG) activity can give an insight into muscle activity associated with a given behavioral state. The masseter muscle is positioned closely to the temporomandibular joint and controls the position and movement of the jaw. The hypothalamus is the region of the brain associated with emotional behavior. In an effort to further understand the muscle activity underlying emotional display, the hypothalamus in two cats was stimulated to evoke a stereotyped emotional response, known as the rage response. Unsheathing of the claws, retraction of the ears, significant pupillary dilation and vocalization (hissing) characterize this behavior. EMG data obtained at the masseter muscle during this emotional state were compared to EMG activity recorded during mastication (eating), the simulated voluntary behavior for this study. The results of this study indicate that the emotional state significantly influences the EMG activity in the masseter muscle. This is evidenced statistically by a larger high frequency component in the EMG data. It is also evidenced by the ratio of stimulation to mastication power levels at different frequencies, which increases as frequency increases. The frequency range between 5-30 Hz has been utilized in the past in studies assessing fatigue. However, the results of this research indicate that the interpretation of the data in this frequency band must be different in studies of emotionally elicited muscle response. Recordings obtained during voluntary muscular activity reflected the typical fatigue response, and appropriate elevations in the power in the 5-30 Hz frequency range occurred, in agreement with previous findings. Recordings obtained during stimulation indicate that the highest power in this frequency band is achieved at the onset of hypothalamic stimulation, rather than at the point in time when fatigue typically occurs, in contrast to previous findings

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

A Review of EMG Techniques for Detection of Gait Disorders

Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices

Spike Sorting of Muscle Spindle Afferent Nerve Activity Recorded with Thin-Film Intrafascicular Electrodes

Afferent muscle spindle activity in response to passive muscle stretch was recorded in vivo using thin-film longitudinal intrafascicular electrodes. A neural spike detection and classification scheme was developed for the purpose of separating activity of primary and secondary muscle spindle afferents. The algorithm is based on the multiscale continuous wavelet transform using complex wavelets. The detection scheme outperforms the commonly used threshold detection, especially with recordings having low signal-to-noise ratio. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards online model-based estimation of muscle length that can be used in a closed-loop functional electrical stimulation system with natural sensory feedback

Wavelet entropy as a measure of ventricular beat suppression from the electrocardiogram in atrial fibrillation

A novel method of quantifying the effectiveness of the suppression of ventricular activity from electrocardiograms (ECGs) in atrial fibrillation is proposed. The temporal distribution of the energy of wavelet coefficients is quantified by wavelet entropy at each ventricular beat. More effective ventricular activity suppression yields increased entropies at scales dominated by the ventricular and atrial components of the ECG. Two studies are undertaken to demonstrate the efficacy of the method: first, using synthesised ECGs with controlled levels of residual ventricular activity, and second, using patient recordings with ventricular activity suppressed by an average beat template subtraction algorithm. In both cases wavelet entropy is shown to be a good measure of the effectiveness of ventricular beat suppression