Oluwarotimi Williams Samuel

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

This study proposed a hybrid decision support method (ANN and Fuzzy_AHP) for heart failure prediction.The performance of the proposed method was examined using three performance metrics.From the evaluations results, the proposed method performed better than the conventional ANN approachThe proposed method would provide improved and realistic result for efficient therapy administration. Heart failure (HF) has been considered as one of the deadliest human diseases worldwide and the accurate prediction of HF risks would be vital for HF prevention and treatment. To predict HF risks, decision support systems based on artificial neural networks (ANN) have been widely proposed in previous studies. Generally, these existing ANN-based systems usually assumed that HF attributes have equal risk contribution to the HF diagnosis. However, several previous investigations have shown that the risk contributions of the attributes would be different. Thus the equal risk assumption concept associated with existing ANN methods would not properly reflect the diagnosis status of HF patients. In this study, the commonly used 13 HF attributes were considered and their contributions were determined by an experienced cardiac clinician. And Fuzzy analytic hierarchy process (Fuzzy_AHP) technique was used to compute the global weights for the attributes based on their individual contribution. Then the global weights that represent the contributions of the attributes were applied to train an ANN classifier for the prediction of HF risks in patients. The performance of the newly proposed decision support system based on the integration of ANN and Fuzzy_AHP methods was evaluated by using online clinical dataset of 297 HF patients and compared with that of the conventional ANN method. Our result shows that the proposed method could achieve an average prediction accuracy of 91.10%, which is 4.40% higher in comparison to that of the conventional ANN method. In addition, the newly proposed method also had better performance than seven previous methods that reported prediction accuracies in the range of 57.85-89.01%. The improvement of the HF risk prediction in the current study might be due to both the various contributions of the HF attributes and the proposed hybrid method. These findings suggest that the proposed method could be used to accurately predict HF risks in the clinic.

Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification☆

Abstract Feature extraction is essential in Electromyography pattern recognition (EMG-PR) based prostheses control method. Time-domain features have been shown to have good performance in upper limb movement classification. However, the performance of EMG-PR prostheses driven by the existing time-domain features is still unsatisfactory. Hence, this study proposed three new time-domain features to improve the performance of EMG-PR based strategy in arm movement classification. EMG signals were recorded from the residual arms of eight amputees while performing different upper limb movements. Then, the newly proposed features were extracted and used to classify their limb movements. Experimental results showed that the proposed features could achieved an average classification accuracy of 92.00% ± 3.11% which was 6.49% higher than that of the commonly used time-domain features (p

Towards an efficient risk assessment in software projects–Fuzzy reinforcement paradigm

Abstract Effective prioritization of software project risks play an important role in determining whether the project will be successful in terms of performance characteristics or not. In this context, this study aims at hybridizing fuzzy multi criteria decision making approaches for the development of an assessment framework that can be used to efficiently identify and rank notable software project risks will aid decision making during the lifecycle of a software product. Specifically, for the assessment of project risk, we have integrated fuzzy Decision Making Trial and Evaluation Laboratory, Fuzzy Multi-Criteria Decision Making and TODIM (an acronym in Portuguese for Interactive and Multiple Attribute Decision Making) approaches. The proposed methodology is used for providing an effective rating mechanism to measure software risk factors. The effectiveness of the proposed approach results have been compared with earlier methods for the assessment of software project risks. The comparison was done based on the OMRON data set of 40 software projects. Evaluation results reveal that the integration of fuzzy approaches could be effective and as well accurate compared to existing approaches for evaluating software project risks.

A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees

BackgroundMost of the modern motorized prostheses are controlled with the surface electromyography (sEMG) recorded on the residual muscles of amputated limbs. However, the residual muscles are usually limited, especially after above-elbow amputations, which would not provide enough sEMG for the control of prostheses with multiple degrees of freedom. Signal fusion is a possible approach to solve the problem of insufficient control commands, where some non-EMG signals are combined with sEMG signals to provide sufficient information for motion intension decoding. In this study, a motion-classification method that combines sEMG and electroencephalography (EEG) signals were proposed and investigated, in order to improve the control performance of upper-limb prostheses.MethodsFour transhumeral amputees without any form of neurological disease were recruited in the experiments. Five motion classes including hand-open, hand-close, wrist-pronation, wrist-supination, and no-movement were specified. During the motion performances, sEMG and EEG signals were simultaneously acquired from the skin surface and scalp of the amputees, respectively. The two types of signals were independently preprocessed and then combined as a parallel control input. Four time-domain features were extracted and fed into a classifier trained by the Linear Discriminant Analysis (LDA) algorithm for motion recognition. In addition, channel selections were performed by using the Sequential Forward Selection (SFS) algorithm to optimize the performance of the proposed method.ResultsThe classification performance achieved by the fusion of sEMG and EEG signals was significantly better than that obtained by single signal source of either sEMG or EEG. An increment of more than 14% in classification accuracy was achieved when using a combination of 32-channel sEMG and 64-channel EEG. Furthermore, based on the SFS algorithm, two optimized electrode arrangements (10-channel sEMG + 10-channel EEG, 10-channel sEMG + 20-channel EEG) were obtained with classification accuracies of 84.2 and 87.0%, respectively, which were about 7.2 and 10% higher than the accuracy by using only 32-channel sEMG input.ConclusionsThis study demonstrated the feasibility of fusing sEMG and EEG signals towards improving motion classification accuracy for above-elbow amputees, which might enhance the control performances of multifunctional myoelectric prostheses in clinical application.Trial registrationThe study was approved by the ethics committee of Institutional Review Board of Shenzhen Institutes of Advanced Technology, and the reference number is SIAT-IRB-150515-H0077.

Towards reducing the impacts of unwanted movements on identification of motion intentions

Surface electromyogram (sEMG) has been extensively used as a control signal in prosthesis devices. However, it is still a great challenge to make multifunctional myoelectric prostheses clinically available due to a number of critical issues associated with existing EMG based control strategy. One such issue would be the effect of unwanted movements (UMs) that are inadvertently done by users on the performance of movement classification in EMG pattern recognition based algorithms. Since UMs are not considered in training a classifier, they would decay the performance of a trained classifier in identifying the target movements (TMs), which would cause some undesired actions in control of multifunctional prostheses. In this study, the impact of UMs was systemically investigated in both able-bodied subjects and transradial amputees. Our results showed that the UMs would be unevenly classified into all classes of the TMs. To reduce the impact of the UMs on the performance of a classifier, a new training strategy that would categorize all possible UMs into a new movement class was proposed and a metric called Reject Ratio that is a measure of how many UMs is rejected by a trained classifier was adopted. The results showed that the average Reject Ratio across all the participants was greater than 91%, meanwhile the average classification accuracy of TMs was above 99% when UMs occurred. This suggests that the proposed training strategy could greatly reduce the impact of UMs on the performance of the trained classifier in identifying the TMs and may enhance the robustness of myoelectric control in clinical applications.

A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording

Non-invasive fetal electrocardiograms (FECGs) are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG) in the spectral domain. Research in the area of non-invasive separations of FECG from abdominal electrocardiograms (AECGs) is in its infancy and several studies are currently focusing on this area. An adaptive noise canceller (ANC) is commonly used for cancelling interference in cases where the reference signal only correlates with an interference signal, and not with a signal of interest. However, results from some existing studies suggest that propagation of electrocardiogram (ECG) signals from the maternal heart to the abdomen is nonlinear, hence the adaptive filter approach may fail if the thoracic and abdominal MECG lack strict waveform similarity. In this study, singular value decomposition (SVD) and smooth window (SW) techniques are combined to build a reference signal in an ANC. This is to avoid the limitation that thoracic MECGs recorded separately must be similar to abdominal MECGs in waveform. Validation of the proposed method with r01 and r07 signals from a public dataset, and a self-recorded private dataset showed that the proposed method achieved F1 scores of 99.61%, 99.28% and 98.58%, respectively for the detection of fetal QRS. Compared with four other single-channel methods, the proposed method also achieved higher accuracy values of 99.22%, 98.57% and 97.21%, respectively. The findings from this study suggest that the proposed method could potentially aid accurate extraction of FECG from MECG recordings in both clinical and commercial applications.

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.). The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees.

Activity Recognition Based on Pattern Recognition of Myoelectric Signals for Rehabilitation

Limb-amputation, stroke, trauma, and some other congenital anomalies not only decrease patients’ quality of life but also cause severe psychological burdens to them. Several advanced rehabilitation technologies have been developed to help patients with limb disabilities restore their lost motor functions. As a kind of neural signal, surface electromyogram (sEMG) recorded on limb muscles usually contain rich information associated with limb motions. By decoding the sEMG with pattern recognition techniques, the motion intents can be effectively identified and used for the control of rehabilitation devices. In this chapter, the control of upper-limb prostheses and rehabilitation robots based on the pattern recognition of sEMG signals was detailedly introduced and discussed. In addition, the clinical feasibility of sEMG-based pattern recognition technique towards an improved function restoration for upper-limb amputees and stroke survivors is also described.

A preliminary evaluation of myoelectrical energy distribution of the front neck muscles in pharyngeal phase during normal swallowing

Pharyngeal phase is a central hub of swallowing in which food bolus pass through from the oral cavity to the esophageal. Proper understanding of the muscular activities in the pharyngeal phase is useful for assessing swallowing function and the occurrence of dysphagia in humans. In this study, high-density (HD) surface electromyography (sEMG) was used to study the muscular activities in the pharyngeal phase during swallowing tasks involving three healthy male subjects. The root mean square (RMS) of the HD sEMG data was computed by using a series of segmented windows as myoelectrical energy. And the RMS of each window covering all channels (16×5) formed a matrix. During the pharyngeal phase of swallowing, three of the matrixes were chosen and normalized to obtain the HD energy maps and the statistical parameter. The maps across different viscosity levels offered the energy distribution which showed the muscular activities of the left and right sides of the front neck muscles. In addition, the normalized average RMS (NARE) across different viscosity levels revealed a left-right significant correlation (r=0.868±0.629, p<;0.01) quantitatively, while it showed even stronger correlation when swallowing water. This pilot study suggests that HD sEMG would be a potential tool to evaluate muscular activities in pharyngeal phase during normal swallowing. Also, it might provide useful information for dysphagia diagnosis.

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.