Francisco Assis de Oliveira Nascimento

Compression of EMG signals with wavelet transform and artificial neural networks

This paper presents a hybrid adaptive algorithm for the compression of surface electromyographic (S-EMG) signals recorded during isometric and/or isotonic contractions. This technique is useful for minimizing data storage and transmission requirements for applications where multiple channels with high bandwidth data are digitized, such as telemedicine applications. The compression algorithm proposed in this work uses a discrete wavelet transform for spectral decomposition and an intelligent dynamic bit allocation scheme implemented by an approach using the Kohonen layer, which improves the bit allocation for sections of the S-EMG with different characteristics. Finally, data and overhead information are packed by entropy coding. The results for the compression of isometric EMG signals showed that this algorithm has a better performance than standard wavelet compression algorithms presented in the literature (presenting a decrease of at least 5% in per cent residual difference (PRD) for the same compression ratio), and a performance that is comparable with the performance of algorithms based on an embedded zero-tree wavelet. For isotonic EMG signals, its performance is better than the performance of the algorithms based on embedded zero-tree wavelets (presenting a decrease in PRD of about 3.6% for the same compression ratios, in the useful compression range).

A tool for time-frequency analysis of heart rate variability

The analysis of heart rate variability (HRV) signals is an important tool for studying the autonomic nervous system, as it allows the evaluation of the balance between the sympathetic and parasympathetic influences on heart rhythm. Time-frequency analysis of HRV makes it easier to evaluate how this balance varies with time. This work presents a tool for time-frequency analysis of heart rate variability (HRV) developed in Matlab 6.5. Three techniques are available: Short-Time Fourier Transform, Continuous Wavelet Transform Scalogram and Time-Variant Auto-Regressive Modeling.

Study on the optimal order for the auto-regressive time-frequency analysis of heart rate variability

Time-frequency analysis of heart rate variability (HRV) makes it easier to evaluate how the balance between the sympathetic and parasympathetic influences on heart rhythm varies with time. The auto-regressive model can be used to calculate the power spectrum density of HRV and to create an auto-regressive spectrogram. This work presents these techniques and describes a series of tests performed with the goal of determining the AR order that is more adequate for the calculation of the AR spectrogram. As a result, ranges of optimal orders for different interpolation rates of the HRV signal are presented.

Semi-automatic detection of the left ventricular border

Two semi-automatic methods for the detection of the left ventricular border in two-dimensional short axis echocardiographic images are presented and compared. In these methods, the left ventricular area variation curve is calculated during a complete cardiac cycle after the segmentation of several frames. This allows the evaluation of the cardiovascular dynamics and the identification of important clinical parameters. The algorithms are proposed as several independent modules. The results are validated through the comparison between the semi-automatic continuous boundaries and manuals boundaries sketched by a medical specialist.

Compression of electromyographic signals using image compression techniques

Despite the growing interest in the transmission and storage of electromyographic signals for long periods of time, few studies have addressed the compression of such signals. In this article we present an algorithm for compression of electromyographic signals based on the JPEG2000 coding system. Although the JPEG2000 codec was originally designed for compression of still images, we show that it can also be used to compress EMG signals for both isotonic and isometric contractions. For EMG signals acquired during isometric contractions, the proposed algorithm provided compression factors ranging from 75 to 90%, with an average PRD ranging from 3.75% to 13.7%. For isotonic EMG signals, the algorithm provided compression factors ranging from 75 to 90%, with an average PRD ranging from 3.4% to 7%. The compression results using the JPEG2000 algorithm were compared to those using other algorithms based on the wavelet transform.

S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation

BackgroundSurface electromyographic (S-EMG) signal processing has been emerging in the past few years due to its non-invasive assessment of muscle function and structure and because of the fast growing rate of digital technology which brings about new solutions and applications. Factors such as sampling rate, quantization word length, number of channels and experiment duration can lead to a potentially large volume of data. Efficient transmission and/or storage of S-EMG signals are actually a research issue. That is the aim of this work.MethodsThis paper presents an algorithm for the data compression of surface electromyographic (S-EMG) signals recorded during isometric contractions protocol and during dynamic experimental protocols such as the cycling activity. The proposed algorithm is based on discrete wavelet transform to proceed spectral decomposition and de-correlation, on a dynamic bit allocation procedure to code the wavelets transformed coefficients, and on an entropy coding to minimize the remaining redundancy and to pack all data. The bit allocation scheme is based on mathematical decreasing spectral shape models, which indicates a shorter digital word length to code high frequency wavelets transformed coefficients. Four bit allocation spectral shape methods were implemented and compared: decreasing exponential spectral shape, decreasing linear spectral shape, decreasing square-root spectral shape and rotated hyperbolic tangent spectral shape.ResultsThe proposed method is demonstrated and evaluated for an isometric protocol and for a dynamic protocol using a real S-EMG signal data bank. Objective performance evaluations metrics are presented. In addition, comparisons with other encoders proposed in scientific literature are shown.ConclusionsThe decreasing bit allocation shape applied to the quantized wavelet coefficients combined with arithmetic coding results is an efficient procedure. The performance comparisons of the proposed S-EMG data compression algorithm with the established techniques found in scientific literature have shown promising results.

Two-dimensional compression of surface electromyographic signals using column-correlation sorting and image encoders

We present a new preprocessing technique for two-dimensional compression of surface electromyographic (S-EMG) signals, based on correlation sorting. We show that the JPEG2000 coding system (originally designed for compression of still images) and the H.264/AVC encoder (video compression algorithm operating in intraframe mode) can be used for compression of S-EMG signals. We compare the performance of these two off-the-shelf image compression algorithms for S-EMG compression, with and without the proposed preprocessing step. Compression of both isotonic and isometric contraction S-EMG signals is evaluated. The proposed methods were compared with other S-EMG compression algorithms from the literature.

Measurement of ejection fraction with standard thermodilution catheters

Right ventricle ejection fraction (RVEF) is clinically used to evaluate right ventricular function. The thermodilution method can be modified to estimate the RVEF. However, this method requires a thermistor with a fast time response in order to yield correct estimates. Digital signal processing techniques that were developed in previous works, allow the use of industry-standard slow time response thermistors for the measurement EF. However, these algorithms were not automated, and the works did not present a complete evaluation of the methods performance. This article presents a modified automated version of these algorithms, and uses numerical and in vitro simulations to test their performance. In the simulations, the measured ejection fraction was compared to the true ejection fraction. RVEFs ranging from 0.20 to 0.80 were tested for heart rates ranging from 30 to 120 heart beats per min. Statistical analysis of data showed that the new method presents an improved performance.

Spectral Shape Estimation in Data Compression for Smart Grid Monitoring

This paper proposes a transform-based compression algorithm for waveforms associated with power quality and transient phenomena in power systems. This method uses the wavelet transform, a dynamic bit allocation in the transform domain through estimation of the spectral shape, as well as entropy coding in order to minimize residual redundancy. Five distinct approaches for estimating the spectral shape are proposed. Four of them are based on analytical models that seek to describe the decreasing behavior of the transformed coefficients: (1) decreasing linear bit allocation shape; (2) decreasing quadratic bit allocation shape; (3) decreasing exponential bit allocation shape; (4) rotated sigmoid bit allocation shape; and (5) the fifth approach-the neural shape estimator (NSE)-is an adaptive model that uses an artificial neural network to map the changes in the spectrum shape. Results with databases of real signals and a performance evaluation using objective measures are reported. The results indicate that the NSE approach outperforms the other proposed solutions that use spectral shape estimation for coding, as well as other compression contributions reported in the literature.

Myoelectric Knee Angle Estimation Algorithms for Control of Active Transfemoral Leg Prostheses

The electromyographic signal is the electrical manifestation of the neuromuscular activation associated with a contracting muscle. The surface electromyographic (SEMG) signal represents the current generated by ionic flow across the membrane of the muscle fibers that propagates through the intervening tissues to reach the detection surface of an electrode located over skin (De Luca (2006)). The SEMG signal provides a non-invasive tool for investigating the properties of skeletal muscles (Sommerich et al. (2000)). The main challenge in implementing controlled motion for prosthesis is correctly predicting the user’s motion intention. SEMG signals have been used in an effective way in prosthesis control systems (Merletti & Parker (2004); Parker et al. (2006)). The SEMG signal is very convenient for prosthesis control, because it is intrinsically related to the user’s intention (Hudgins et al. (1993)). Amyoelectric control algorithm should be capable of learning themuscular activation patterns that are used in natural form for typical movements. It also needs robustness against variations in conditions during the operation, and the response time cannot create delays that are noticeable to the user (Fukuda et al. (2003)). Pattern recognition of the SEMG signal allows discriminating amongst the desired classes of limb motion and plays a key role in advanced control of powered prostheses for amputees and for individuals with congenital deficiency in the upper or lower limbs. The success of a myoelectric control scheme depends greatly on the classification accuracy. Electronic knees can be designed for providing different levels of damping during swing, and for adjusting to different walking speeds, assuming they have the appropriate sensors and control algorithms for estimating the knee joint angle and the walking speed. With the appropriate control algorithm, it is possible to program the prosthesis to allow the knee to flex and extend while bearing a subject’s weight (stance flexion). This feature of normal walking is not possible with conventional prostheses. Electronic knees use some form of computational intelligence to control the resistive torque about the knee. Several research groups have been involved in designing prototype knee controllers. Grimes et al. (1977) developed an echo control scheme for gait control, in which a modified knee trajectory from the sound leg is played back on the contralateral side. Popovic et al. (1995) presented a battery-powered active knee joint actuated by direct-current motors, together with a finite state knee controller 22