On the behavior of surface electromyographic variables during the menstrual cycle.

The goal of this work is to study the behavior of electromyographic variables during the menstrual cycle. Ten female volunteers (24.0 ± 2.8 years of age) performed fatiguing isometric contractions, and electromyographic signals were measured on the biceps brachii in four phases of the menstrual cycle. Adaptations of classical algorithms were used for the estimation of the root mean square (RMS) value, absolute rectified value (ARV), mean frequency (MNF), median frequency (MDF), and conduction velocity (CV). The CV estimator had a higher (p = 0.002) rate of decrease at the end of the follicular phase and at the end of the luteal phase. The MDF (p = 0.002) and MNF (p = 0.004) estimators had a higher rate of decrease at the beginning of the follicular phase and at the end of the luteal phase. No significant differences between phases of the menstrual cycle were detected with the ARV and RMS estimators (p > 0.05). These results suggest that the behavior of the muscles in women presents different characteristics during different phases of the menstrual cycle. In particular, women were more susceptible to fatigue at the end of the luteal phase.

Electromyographic evaluation of muscle recovery after isometric fatigue.

Despite growing interest in the behavior of electromyographic signals during muscle fatigue, few studies investigate fatigue recovery. In this work, we use surface electromyographic signals to determine the recovery time after isometric fatigue of the biceps brachii muscle in 90° flexion of the non-dominant elbow. Sixty volunteers were arranged into six experimental groups. Experiments were performed in three stages: reference phase (REF), fatigue resistance phase (RES), and recovery phase (REC). An isometric exercise was performed during the RES stage. The time interval between the RES and REC stages was different for each experimental group: 1, 2, 4, 8, 24 and 48 hours. Surface electromyographic signals were acquired during each phase, and the following electromyographic variables were calculated for each phase: median frequency (MDF), root mean squared (RMS) value, and maximum voluntary contraction (MVC). The REF data were compared with the REC data using a paired Wilcoxon test. The results show that the MVC is recovered 2 hours after the exercise. The MDF seems not to be fully recovered after 48 hours, but displays an apparent recovery trend.

Improved segmentation of echocardiographic images using fusion of images from different cardiac cycles.

In this work, an algorithm for the detection of the left ventricular border in two-dimensional long axis echocardiographic images is presented. In its preprocessing stage, images fusion was applied to a sequence of images composed of three cardiac cycles. This method exploits the similarity of corresponding frames from different cycles and produces contrast enhancement in the left ventricular boundary. This result improves the performance of the segmentation stage which is based on watershed transformation. The obtained left ventricle border is quantitatively and qualitatively compared with contours manually segmented by a cardiologist, and with results obtained using seven different techniques from the literature.

Fusion of electromyographic signals with proprioceptive sensor data in myoelectric pattern recognition for control of active transfemoral leg prostheses.

This paper presents a myoelectric knee joint angle estimation algorithm for control of active transfemoral prostheses, based on feature extraction and pattern classification. The feature extraction stage uses a combination of time domain and frequency domain methods (entropy of myoelectric signals and cepstral coefficients, respectively). Additionally, the methods are fused with data from proprioceptive sensors (gyroscopes), from which angular rate information is extracted using a Kalman filter. The algorithm uses a Levenberg-Marquardt neural network for estimating the intended knee joint angle. The proposed method is demonstrated in a normal volunteer, and the results are compared with pattern classification methods based solely on electromyographic data. The use of surface electromyographic signals and additional information related to proprioception improves the knee joint angle estimation precision and reduces estimation artifacts.

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.