An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal.

BACKGROUND: Electromyography (EMG) signal processing and Muscle Onset Latency (MOL) are widely used in rehabilitation sciences and nerve conduction studies. The majority of existing software packages provided for estimating MOL via analyzing EMG signal are computerized, desktop based and not portable; therefore, experiments and signal analyzes using them should be completed locally. Moreover, a desktop or laptop is required to complete experiments using these packages, which costs. OBJECTIVE: Develop a non-expensive and portable Android application (app) for estimating MOL via analyzing surface EMG. MATERIAL AND METHODS: A multi-layer architecture model was designed for implementing the MOL estimation app. Several Android-based algorithms for analyzing a recorded EMG signal and estimating MOL was implemented. A graphical user interface (GUI) that simplifies analyzing a given EMG signal using the presented app was developed too. RESULTS: Evaluation results of the developed app using 10 EMG signals showed promising performance; the MOL values estimated using the presented app are statistically equal to those estimated using a commercial Windows-based surface EMG analysis software (MegaWin 3.0). For the majority of cases relative error <10%. MOL values estimated by these two systems are linearly related, the correlation coefficient value ~ 0.93. These evaluations revealed that the presented app performed as well as MegaWin 3.0 software in estimating MOL. CONCLUSION: Recent advances in smart portable devices such as mobile phones have shown the great capability of facilitating and decreasing the cost of analyzing biomedical signals, particularly in academic environments. Here, we developed an Android app for estimating MOL via analyzing the surface EMG signal. Performance is promising to use the app for teaching or research purposes.

A System for Continuous Estimating and Monitoring Cardiac Output via Arterial Waveform Analysis.

BACKGROUND: Cardiac output (CO) is the total volume of blood pumped by the heart per minute and is a function of heart rate and stroke volume. CO is one of the most important parameters for monitoring cardiac function, estimating global oxygen delivery and understanding the causes of high blood pressure. Hence, measuring CO has always been a matter of interest to researchers and clinicians. Several methods have been developed for this purpose, but a majority of them are either invasive, too expensive or need special expertise and experience. Besides, they are not usually risk free and have consequences. OBJECTIVE: Here, a semi-invasive system was designed and developed for continuous CO measurement via analyzing and processing arterial pulse waves. RESULTS: Quantitative evaluation of developed CO estimation system was performed using 7 signals. It showed that it has an acceptable average error of (6.5%) in estimating CO. In addition, this system has the ability to consistently estimate this parameter and to provide a CO versus time curve that assists in tracking changes of CO. Moreover, the system provides such curve for systolic blood pressure, diastolic blood pressure, average blood pressure, heart rate and stroke volume. CONCLUSION: Evaluation of the results showed that the developed system is capable of accurately estimating CO. The curves which the system provides for important parameters may be valuable in monitoring hemodynamic status of high-risk surgical patients and critically ill patients in Intensive Care Units (ICU). Therefore, it could be a suitable system for monitoring hemodynamic status of critically ill patients.

A Wireless Electronic Esophageal Stethoscope for Continuous Monitoring of Cardiovascular and Respiratory Systems during Anaesthesia.

BACKGROUND: The basic requirements for monitoring anesthetized patients during surgery are assessing cardiac and respiratory function. Esophageal stethoscopes have been developed for this purpose, but these devices may not provide clear heart and lung sound due to existence of various noises in operating rooms. In addition, the stethoscope is not applicable for continues monitoring, and it is unsuitable for observing inaccessible patients in some conditions such as during CT scan. OBJECTIVE: A wireless electronic esophageal stethoscope is designed for continues auscultation of heart and lung sounds in anesthetized patients. The system consists of a transmitter and a receiver. The former acquires, amplifies and transmits the acquired sound signals to the latter via a frequency modulation transmitter. The receiver demodulates, amplifies, and delivers the received signal to a headphone to be heard by anesthesiologist. RESULTS: The usability and effectiveness of the designed system was qualitatively evaluated by 5 anesthesiologists in Namazi Hospital and Shahid Chamran Hospital, Shiraz, Iran on 30 patients in several operating rooms in different conditions; e.g., when electro surgery instruments are working. Fortunately, the experts on average ranked good quality for the heard heart and lung sounds and very good on the user friendly being of the instrument. CONCLUSION: Evaluation results demonstrate that the developed system is capable of capturing and transmitting heart and lung sounds successfully. Therefore, it can be used to continuously monitor anesthetized patients' cardiac and respiratory function. Since via the instrument wireless auscultation is possible, it could be suitable for observing inaccessible patients in several conditions such as during CT scan.

Comparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition.

BACKGROUND: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impact on the performance of a decomposition system. EMG decomposition has been studied well and several systems were proposed, but feature extraction step has not been investigated in detail. OBJECTIVE: Several EMG signals were generated using a physiologically-based EMG signal simulation algorithm. For each signal, the firing patterns of motor units (MUs) provided by the simulator were used to extract MUPs of each MU. For feature extraction, different wavelet families including Daubechies (db), Symlets, Coiflets, bi-orthogonal, reverse bi-orthogonal and discrete Meyer were investigated. Moreover, the possibility of reducing the dimensionality of MUP feature vector is explored in this work. The MUPs represented using wavelet-domain features are transformed into a new coordinate system using Principal Component Analysis (PCA). The features were evaluated regarding their capability in discriminating MUPs of individual MUs. RESULTS: Extensive studies on different mother wavelet functions revealed that db2, coif1, sym5, bior2.2, bior4.4, and rbior2.2 are the best ones in differentiating MUPs of different MUs. The best results were achieved at the 4th detail coefficient. Overall, rbior2.2 outperformed all wavelet functions studied; nevertheless for EMG signals composed of more than 12 MUPTs, syms5 wavelet function is the best function. Applying PCA slightly enhanced the results.

Design and fabrication of helmholtz coils to study the effects of pulsed electromagnetic fields on the healing process in periodontitis: preliminary animal results.

BACKGROUND: Effects of electromagnetic fields on healing have been investigated for centuries. Substantial data indicate that exposure to electromagnetic field can lead to enhanced healing in both soft and hard tissues. Helmholtz coils are devices that generate pulsed electromagnetic fields (PEMF). Objective : In this work, a pair of Helmholtz coils for enhancing the healing process in periodontitis was designed and fabricated. METHOD: An identical pair of square Helmholtz coils generated the 50 Hz magnetic field.  This device was made up of two parallel coaxial circular coils (100 turns in each loop, wound in series) which were separated from each other by a distance equal to the radius of one coil (12.5 cm). The windings of our Helmholtz coil was made of standard 0.95mm wire to provide the maximum possible current. The coil was powered by a function generator.  RESULTS: The Helmholtz Coils generated a uniform magnetic field between its coils. The magnetic field strength at the center of the space between two coils was 97.6 µT. Preliminary biological studies performed on rats show that exposure of laboratory animals to pulsed electromagnetic fields enhanced the healing of periodontitis. CONCLUSION: Exposure to PEMFs can lead to stimulatory physiological effects on cells and tissues such as enhanced healing of periodontitis.

An Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network.

BACKGROUND: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image artifacts such as noise, low contrast and intensity non-uniformity, there are some classification errors in the results of image segmentation. OBJECTIVE: An automated algorithm based on multi-layer perceptron neural networks (MLPNN) is presented for segmenting MR images. The system is to identify two tissues of WM and GM in human brain 2D structural MR images. A given 2D image is processed to enhance image intensity and to remove extra cerebral tissue. Thereafter, each pixel of the image under study is represented using 13 features (8 statistical and 5 non- statistical features) and is classified using a MLPNN into one of the three classes WM and GM or unknown. RESULTS: The developed MR image segmentation algorithm was evaluated using 20 real images. Training using only one image, the system showed robust performance when tested using the remaining 19 images. The average Jaccard similarity index and Dice similarity metric for the GM and WM tissues were estimated to be 75.7 %, 86.0% for GM, and 67.8% and 80.7%for WM, respectively. CONCLUSION: The obtained performances are encouraging and show that the presented method may assist with segmentation of 2D MR images especially where categorizing WM and GM is of interest.

A hybrid classifier for characterizing motor unit action potentials in diagnosing neuromuscular disorders.

BACKGROUND: The time and frequency features of motor unit action potentials (MUAPs) extracted from electromyographic (EMG) signal provide discriminative information for diagnosis and treatment of neuromuscular disorders. However, the results of conventional automatic diagnosis methods using MUAP features is not convincing yet. OBJECTIVE: The main goal in designing a MUAP characterization system is obtaining high classification accuracy to be used in clinical decision system. For this aim, in this study, a robust classifier is proposed to improve MUAP classification performance in estimating the class label (myopathic, neuropathic and normal) of a given MUAP. METHOD: The proposed scheme employs both time and time-frequency features of a MUAP along with an ensemble of support vector machines (SVMs) classifiers in hybrid serial/parallel architecture. Time domain features includes phase, turn, peak to peak amplitude, area, and duration of the MUAP. Time-frequency features are discrete wavelet transform coefficients of the MUAP. RESULTS: Evaluation results of the developed system using EMG signals of 23 subjects (7 with myopathic, 8 with neuropathic and 8 with no diseases)  showed that the system estimated the class label of MUAPs extracted from these signals with average of accuracy of 91% which is at least 5% higher than the accuracy of two previously presented methods. CONCLUSION: Using different optimized subsets of features along with the presented hybrid classifier results in a classification accuracy that is encouraging to be used in clinical applications for MUAP characterization. 

Decomposition of intramuscular EMG signals using a knowledge -based certainty classifier algorithm.

An automated system for resolving an intramuscular electromyographic (EMG) signal into its constituent motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters for each motor unit (MU), such as the motor unit potential (MUP) template and mean firing rate, are required. The system decomposes an EMG signal off-line by filtering the signal, detecting MUPs, and then grouping the detected MUPs using a clustering and a supervised classification algorithm. Both the clustering and supervised classification algorithms use MUP shape and MU firing pattern information to group MUPs into several MUPTs. Clustering is partially based on the K-means clustering algorithm. Supervised classification is implemented using a certainty-based classifier technique that employs a knowledge-based system to merge trains, detect and correct invalid trains, as well as adjust the assignment threshold for each train. The accuracy (93.2%±5.5%), assignment rate (93.9%±2.6%), and error in estimating the number of MUPTs (0.3±0.5) achieved for 10 simulated EMG signals comprised of 3-11 MUPTs are encouraging for using the system for decomposing various EMG signals.

A method for detecting and editing MUPTs contaminated by false classification errors during EMG signal decomposition.

A robust method for detecting motor unit potential trains (MUPTs) contaminated with false classification errors (FCEs) during EMG signal decomposition and then removing the FCEs from a contaminated train is presented. Using motor unit (MU) firing pattern information provided by each MUPT, the developed algorithm first determines whether a given train is contaminated by high number of FCEs and needs to be edited. For contaminated MUPTs, the method uses both MU firing pattern and motor unit potential (MUP) shape information to detect MUPs that were erroneously assigned to the train (i.e., represent FCEs). For the simulated data used in this study contaminated MUPTs could be detected with 88.7% accuracy. For a given contaminated MUPT, the algorithm on average correctly detected 83.4% of the FCEs and left 93.4% of the correctly assigned MUPs. The accuracy of the MUPs classified to a MUPT was estimated to be 92.1% on average.

Dynamic and omni wedge implementation on an Elekta SL linac.

The concommitant use of a multileaf collimator (MLC) and a wedge can result in conflicts in the optimal collimator angle if both MLC and wedge are fixed relative to one another. This is particularly true of linacs in which a single wedge orientation is provided. In this paper, a solution is provided that makes use of two orthogonal universal wedges (omni wedge). Although this technique can be applied regardless of the means by which the wedged fields are implemented, the measurements reported in this paper were performed using a fixed, internal mechanical wedge coupled with a dynamic wedge, formed by the motion of one of the backup jaws. An implementation of a dynamic wedge for the Elekta SL series of linear accelerators is presented. Results of measurements of the dosimetric characteristics of both the particular implementation of the dynamic wedge and of the omni field are presented. For the dynamic wedge, measurements were made of the wedge factor and dose profile as a function of field size and depth. In addition, the effects of variables, such as dynamic delivery technique and direction of diaphragm motion, on the dynamic wedge profiles were studied and discussed. For the omni wedge, measurements were made of the degree to which the mathematical formalism for describing an omni wedge matches the measured isodose distributions. Comparisons between mechanical wedge dose distributions and the omni wedge were also made.

Lung compensator design using an electronic portal imaging device.

Using a liquid filled electronic portal imaging device (EPID) installed on a linear accelerator and a composite chest phantom, exit dose measurements were carried out to establish an empirical relationship between the pixel values of the imaging detector and the corresponding equivalent thickness of the overlying phantom material. Results for 6 and 10 MV photons show that the relationship depends on the so-called input/output characteristics of the imaging device for a particular photon energy. For a chest irradiation, an EPID image obtained under treatment geometry provides the pixel value information that is used to calculate the tissue deficit over the lung region. The compensators are made of lead whose thickness is calculated from the established empirical relationship to replace the tissue deficit over lungs. The effectiveness of the method is demonstrated with thermoluminescent dosimetry (TLD) for 6 and 10 MV beams. With compensators in place, the dose uniformity was found to be within +/- 5%.

The use of an electronic portal imaging system to measure portal dose and portal dose profiles.

The dosimetric characteristics of a scanning liquid-filled ionization chamber (SLIC) electronic portal imaging device have been investigated. To assess the system's response in relation to incident radiation beam intensity, a series of characteristic curves are obtained for various field sizes and nominal energies of 6 and 10 MV photons. The response of the imaging system is dependent on incident radiation intensity and can be described to within 1% accuracy on central axis using a square root function. Portal dose measurements with the SLIC at the plane of the detector, on central axis of the beam using homogeneous attenuating phantom materials show that the imaging system is capable of measuring the portal (transmission) dose to within 3% of the ionization chamber results for homogeneous material. For two-dimensional dosimetry applications, the system is calibrated with a 10 cm Perspex block used as beam flattening material on the detector cassette to correct for variations in individual ion chamber sensitivity and the effect of nonuniform beam profiles produced by the flattening filter. Open and wedged dose profiles measured with the SLIC agreed with ion chamber measured profiles to within 3.5% accuracy.