An integrated computer-based system to study neuromuscular disorders of the upper limb

A multi-channel computer-based clinical instrument was developed to simultaneously acquire, process, display, quantify and correlate electromyographic (EMG) activity, resistive torque, range of motion (ROM), and pain levels in the upper limbs of humans. Each channel consisted of a time and frequency domain block, a torque and angle measurement block, an experiment number counter block and a data storage and retrieval block. The study showed that there was increased level of EMG activity prior to pain onset (P<0.05). There was also clear evidence that elevated perception of pain and elevated levels of resistive torque (P<0.05) were positively correlated with the EMG activity in the muscles responsible for antalgic posture of the upper limb (P<0.05)

Detection and extraction of the ECG signal parameters

This work investigates a set of efficient techniques to extract important features from the ECG data applicable in automatic cardiac arrhythmia classification. The selected parameters are divided into two main categories namely morphological and statistical features. Extraction of morphological features was achieved using signal processing techniques and detection of statistical features was performed by employing mathematical methods. Each specific method was applied to a pre-selected data segment of the MIT-BIH database. The classification of different heart beats was performed based upon the extracted features. The morphological features were found as the most efficient for further ECG signal analysis. However, because of ECG signal variability in different patients, the mathematical approach is preferred for a precise and robust feature extraction. As a result of the extracted features, an efficient computer based ECG signal classifier could be developed for detection of a vast range of cardiac arrhythmias

Computer-based clinical instrumentation for processing and analysis of mechanically evoked electromyographic signals in the upper limb

A computer-based clinical instrument was developed to simultaneously acquire, process, display, quantify and correlate electromyographic (EMG) activity, resistive torque, range of motion (ROM), and pain responses evoked by mechanical stimuli (i.e. passive elbow extensions) in humans. This integrated multichannel system was designed around AMLAB® analog modules and software objects called ICAMs. Although this system was designed to specifically study the patterns and nature of evoked motor responses in Carpal Tunnel Syndrome (CTS) patients, it could equally well be modified to allow acquisition, processing and analysis of EMG signals in other studies and applications. In this paper, we describe an integrated system to simultaneously study and analyze the mechanically evoked electromyographic, torque and ROM signals and correlate various levels of pain to these signals

Computer-based clinical instrumentation for processing and analysis of electroneuromyographic signals in the upper limb

A computer-based clinical instrument was developed to simultaneously acquire, process, display, quantify and correlate electroneuromyographic (ENMG) activity in the upper limb in humans. This system was designed around AMLAB® analog modules and software objects called ICAMs. The system consists of a nerve stimulator block, a time domain, EMG block with evoked response averaging capability, a counter block and a data storage and retrieval block. This system has been designed to study the H-reflex and M-response in the upper limb of normal subjects and Carpal Tunnel Syndrome (CTS) patients. It could be easily modified to acquire, process and analyze the ENMG signals in other parts of the human body to assess the continuity and function of the sensory and motor pathways. In this paper, we present an integrated system to simultaneously measure and analyze the electroneuromyographic activities in the upper limb

Principal components of recurrence quantification analysis of EMG

A nonlinear dynamical signal analysis technique, recurrence quantification analysis (RQA), was applied to surface electromyograms (EMG) recorded during a series of isometric contractions. None of the ten RQA features calculated adequately related the EMG to the force level so principal components analysis was applied to combine these features into a lower number of variables. Linear regression of the first principal component gave similar lines for each subject. However, the error was too great for these lines to be used in predicting force from the principal component

Recurrence plot features of ECG signals

Single beats from ECG recording were used to demonstrate how the nonlinear dynamical analysis method of recurrence plots can be used to qualitatively describe data. It is concluded that, for ECG examples, the characteristics of the signals that cause particular features of the recurrence plots are easily identified. However, features in recurrence plots obtained from other signals must have similar underlying causes. Recurrence plots may therefore reveal time-domain features of a signal that make it easier to describe

Recurrence plot features: an example using ECG

Electrocardiogram (ECG) signals are analysed using the nonlinear method of recurrence plots, which reveals subtle time correlations in time-domain signals. Large-scale features in the recurrence plots, which consist entirely of single dots, line segments of different orientations and white spaces, are directly related to time-domain features in the original signals. The relationship between recurrence plot features and time-domain features is easy to see for these ECG signals, and can be used to infer time-domain features of other signals (such as other bioelectric signals) that are more difficult to interpret due to their complexity

Removing power line noise from recorded EMG

Three methods for offline removal of power line interference (hum) from electromyograms (EMGs) were compared using both simulated and recorded EMG signals. The first method was a simple recursive digital notch filter. In the second method (Regression-Subtraction), the amplitude and phase of the interference were estimated by regressing sine and cosine functions onto a 'quiet period' before the start of the muscular contraction. A sinusoid with this frequency, magnitude and phase was then subtracted from the entire length of the signal. In the third method (Spectrum Interpolation), it was assumed that the magnitude of the original component of the signal at the frequency of the interference can be approximated by interpolating between the adjacent frequency bins in the power spectrum. While Regression-Subtraction was found to give the highest SNR for the output signal under ideal conditions, Spectrum Interpolation was found to be comparable if the phase of the interference was not constant and superior if the interference contained strong harmonic components

Vital sign monitoring and cardiac triggering at 1.5 Tesla: A practical solution by an MR-ballistocardiography fiber-optic sensor

This article presents a solution for continuous monitoring of both respiratory rate (RR) and heart rate (HR) inside Magnetic Resonance Imaging (MRI) environments by a novel ballistocardiography (BCG) fiber-optic sensor. We designed and created a sensor based on the Fiber Bragg Grating (FBG) probe encapsulated inside fiberglass (fiberglass is a composite material made up of glass fiber, fabric, and cured synthetic resin). Due to this, the encapsulation sensor is characterized by very small dimensions (30 x 10 x 0.8 mm) and low weight (2 g). We present original results of real MRI measurements (conventionally most used 1.5 T MR scanner) involving ten volunteers (six men and four women) by performing conventional electrocardiography (ECG) to measure the HR and using a Pneumatic Respiratory Transducer (PRT) for RR monitoring. The acquired sensor data were compared against real measurements using the objective Bland-Altman method, and the functionality of the sensor was validated (95.36% of the sensed values were within the +/- 1.96 SD range for the RR determination and 95.13% of the values were within the +/- 1.96 SD range for the HR determination) by this means. The accuracy of this sensor was further characterized by a relative error below 5% (4.64% for RR and 4.87% for HR measurements). The tests carried out in an MRI environment demonstrated that the presence of the FBG sensor in the MRI scanner does not affect the quality of this imaging modality. The results also confirmed the possibility of using the sensor for cardiac triggering at 1.5 T (for synchronization and gating of cardiovascular magnetic resonance) and for cardiac triggering when a Diffusion Weighted Imaging (DWI) is used.Web of Science193art. no. 47

ECG noise cancellation using digital filters

A digital filter structure is proposed to maximally remove noise from the ECG signals. This structure is based on cascading a zero-phase bandpass, an adaptive filter, and multi-band-pass filter. It provides an efficient method for removing noise from the ECG signals. This filter structure has low implementation complexity and introduces little noise into a typical ECG. It can be applied to real-time applications particularly automatic cardiac arrhythmia classifiers