Sarbast Rasheed

Integrating Heterogeneous Classifier Ensembles for EMG Signal Decomposition Based on Classifier Agreement

In this paper, we present a design methodology for integrating heterogeneous classifier ensembles by employing a diversity-based hybrid classifier fusion approach, whose aggregator module consists of two classifier combiners, to achieve an improved classification performance for motor unit potential classification during electromyographic (EMG) signal decomposition. Following the so-called overproduce and choose strategy to classifier ensemble combination, the developed system allows the construction of a large set of base classifiers, and then automatically chooses subsets of classifiers to form candidate classifier ensembles for each combiner. The system exploits kappa statistic diversity measure to design classifier teams through estimating the level of agreement between base classifier outputs. The pool of base classifiers consists of different kinds of classifiers: the adaptive certainty-based, the adaptive fuzzy k -NN, and the adaptive matched template filter classifiers; and utilizes different types of features. Performance of the developed system was evaluated using real and simulated EMG signals, and was compared with the performance of the constituent base classifiers. Across the EMG signal datasets used, the developed system had better average classification performance overall, especially in terms of reducing classification errors. For simulated signals of varying intensity, the developed system had an average correct classification rate CCr of 93.8% and an error rate Er of 2.2% compared to 93.6% and 3.2%, respectively, for the best base classifier in the ensemble. For simulated signals with varying amounts of shape and/or firing pattern variability, the developed system had a CCr of 89.1% with an Er of 4.7% compared to 86.3% and 5.6%, respectively, for the best classifier. For real signals, the developed system had a CCr of 89.4% with an Er of 3.9% compared to 84.6% and 7.1%, respectively, for the best classifier.

A software package for interactive motor unit potential classification using fuzzy k-NN classifier

We present an interactive software package for implementing the supervised classification task during electromyographic (EMG) signal decomposition process using a fuzzy k-NN classifier and utilizing the MATLAB high-level programming language and its interactive environment. The method employs an assertion-based classification that takes into account a combination of motor unit potential (MUP) shapes and two modes of use of motor unit firing pattern information: the passive and the active modes. The developed package consists of several graphical user interfaces used to detect individual MUP waveforms from a raw EMG signal, extract relevant features, and classify the MUPs into motor unit potential trains (MUPTs) using assertion-based classifiers.

Adaptive certainty-based classification for decomposition of EMG signals

An adaptive certainty-based supervised classification approach for electromyographic (EMG) signal decomposition is presented and evaluated. Similarity criterion used for grouping motor unit potentials (MUPs) is based on a combination of MUP shapes and two modes of use of motor unit (MU) firing pattern information: passive and active. Performance of the developed classifier was evaluated using synthetic signals of known properties and real signals and compared with the performance of the certainty classifier (CC). Across the sets of simulated and real EMG signals used for comparison, the adaptive certainty classifier (ACC) had both better average performance and lower performance variability. For simulated signals of varying intensity, the ACC had an average correct classification rate (CCr) of 83.7% with a mean absolute deviation (MAD) of 5.8% compared to 78.3 and 8.7%, respectively, for the CC. For simulated signals with varying amounts of shape and/or firing pattern variability, the ACC had a CCr of 79.7% with a MAD of 4.7% compared to 76.6 and 6.9%, respectively, for the CC. For real signals, the ACC had a CCr of 70.0% with a MAD of 6.3% compared to 64.9 and 6.4%, respectively, for the CC. The test results demonstrate that the ACC can manage both MUP shape variability as well as MU firing pattern variability. The ACC adapts to EMG signal characteristics to create dynamic data driven classification criteria so that the number of MUP assignments made reflects the signal complexity and the number of erroneous assignments is kept sufficiently low. The ability of the ACC to adjust to specific signal characteristics suggests that it can be successfully applied to a wide variety of EMG signals.

Fusion of multiple classifiers for motor unit potential sorting

Abstract To achieve improved classification performance, a multiclassifier fusion approach for motor unit potential (MUP) sorting during electromyographic (EMG) signal decomposition was investigated. A classifier fusion system was developed that aggregates, at the abstract and measurement levels, the outputs of an ensemble of heterogeneous base classifiers to reach a collective decision, and then uses an adaptive feedback control system that detects and processes classification errors by using motor unit firing pattern consistency statistics. Three types of base classifiers were used: certainty, adaptive certainty, and adaptive fuzzy k-NN. Performance of the developed system was evaluated using real and synthetic simulated EMG signals with known properties and compared with the performance of the constituent base classifiers. Across the sets of EMG signal data sets studied, the classifier fusion schemes had better average classification performance, especially in terms of improving correct classification rates. Relative to the average performance of base classifiers and based on the difference between correct classification rate CC r and error rate E r , the adaptive average rule classifier fusion scheme shows on average: for the set of real signals an improvement of 9.2%; for the set of simulated signals of varying intensity an improvement of 6%; and for the set of simulated signals of varying amounts of shape and/or firing pattern variability an improvement of 7.7%.

Multi-classification techniques applied to EMG signal decomposition

In this paper we study the effectiveness of using multiple classifier combination for EMG signal decomposition aiming to obtain more accurate results than is possible from each of the constituent classifiers. The developed system employs an ensemble of error-independent modified certainty classifiers fused at the abstract and measurement levels for integrating information to reach a collective decision. For decision combination at the abstract level, the majority voting scheme has been investigated. While at the measurement level, two types of combination methods have been investigated: one used fixed combination tides that do not require prior training and a trainable combination method. For the second type, the fuzzy integral method was used. The ensemble classification task is completed by feeding the classifiers with different features extracted from the EMG signal. The results show that using classifier fusion methods improved the overall classification performance.

Diversity-based combination of non-parametric classifiers for EMG signal decomposition

Non-parametric classification procedures based on a certainty measure and nearest neighbour rule for motor unit potential classification (MUP) during electromyographic (EMG) signal decomposition were explored. A diversity-based classifier fusion approach is developed and evaluated to achieve improved classification performance. The developed system allows the construction of a set of non-parametric base classifiers and then automatically chooses, from the pool of base classifiers, subsets of classifiers to form candidate classifier ensembles. The system selects the classifier ensemble members by exploiting a diversity measure for selecting classifier teams. The kappa statistic is used as the diversity measure to estimate the level of agreement between base classifier outputs, i.e., to measure the degree of decision similarity between base classifiers. The pool of base classifiers consists of two kinds of classifiers: adaptive certainty-based classifiers (ACCs) and adaptive fuzzy k-NN classifiers (AFNNCs) and both utilize different types of features. Once the patterns are assigned to their classes, by the classifier fusion system, firing pattern consistency statistics for each class are calculated to detect classification errors in an adaptive fashion. Performance of the developed system was evaluated using real and simulated EMG signals and was compared with the performance of the constituent base classifiers and the performance of the fixed ensemble containing the full set of base classifiers. Across the EMG signal data sets used, the diversity-based classifier fusion approach had better average classification performance overall, especially in terms of reducing classification errors.

A Hybrid Classifier Fusion Approach for Motor Unit Potential Classification During EMG Signal Decomposition

In this paper, we propose a hybrid classifier fusion scheme for motor unit potential classification during electromyographic (EMG) signal decomposition. The scheme uses an aggregator module consisting of two stages of classifier fusion: the first at the abstract level using class labels and the second at the measurement level using confidence values. Performance of the developed system was evaluated using one set of real signals and two sets of simulated signals and was compared with the performance of the constituent base classifiers and the performance of a one-stage classifier fusion approach. Across the EMG signal data sets used and relative to the performance of base classifiers, the hybrid approach had better average classification performance overall. For the set of simulated signals of varying intensity, the hybrid classifier fusion system had on average an improved correct classification rate (CCr) (6.1%) and reduced error rate (Er) (0.4%). For the set of simulated signals of varying amounts of shape and/or firing pattern variability, the hybrid classifier fusion system had on average an improved CCr (6.2%) and reduced Er (0.9%). For real signals, the hybrid classifier fusion system had on average an improved CCr (7.5%) and reduced Er (1.7%).

An interactive environment for motor unit potential classification using certainty-based classifiers

Abstract An interactive environment for performing the motor unit potential (MUP) classification tasks required for electromyographic (EMG) signal decomposition is described and developed utilizing the MATLAB high-level programming language and its interactive environment. Certainty-based classification approach had been employed for the classification task in which the assignment criterion used for MUPs is based on a combination of MUP shapes and motor unit firing pattern information. The environment software package consists of several graphical user interfaces used to detect individual MUP waveforms from a raw EMG signal, extract relevant features, and classify the MUPs into motor unit potential trains (MUPTs) using certainty-based classifiers. The development of the proposed software package is useful at enhancing the analysis quality and providing a systematic approach to the EMG signal decomposition process.

Pattern Classification Techniques for EMG Signal Decomposition

The electromyographic (EMG) signal decomposition process is addressed by developing different pattern classification approaches. Single classi- fier and multiclassifier approaches are described for this purpose. Single classifiers include: certainty-based classifiers, classifiers based on the nearest neighbour deci- sion rule: the fuzzy k-NN classifiers, and classifiers that use a correlation measure as an estimation of the degree of similarity between a pattern and a class template: the matched template filter classifiers. Multiple classifier approaches aggregate the decision of the heterogeneous classifiers aiming to achieve better classification per- formance. Multiple classifier systems include: one-stage classifier fusion, diversity- based one-stage classifier fusion, hybrid classifier fusion, and diversity-based hybrid classifier fusion schemes.

Developing a spectral parallelism electronic system for magnetic resonance imaging

In this paper, we present a spectral parallelism electronic system that works as a data acquisition and image reconstruction system for magnetic resonance imaging (MRI). It uses a custom receiver chain and narrowband bandpass filters. The broadband magnetic resonance (MR) signal is spectrally separated into multiple narrowband channels. Then each channel signal is processed individually and the system recombines the frequency-limited narrowband signals from the separate channels to reconstruct images or signal profiles. The final image is reconstructed by recombining all the channels data via weighted addition, where the weights correspond to the frequency responses of each narrowband filter. Results were obtained using a clinical MRI system and the images acquired by the developed embedded system showed the feasibility of achieving images with signal-to-noise ratio comparable to those produced by the clinical system.