Sequential Forward Floating Search Algorithm Research Articles

Incremental upgrading sensor placement methodology: Application to the leak localization in water networks

This paper presents a new methodology for sensor reallocation in large scale-systems considering fault isolation purposes. From an initial set consisting of a limited number of sensors already installed in certain locations of the system, the proposed methodology produces a new sensor placement where some of the sensors are strategically reallocated in different but available places of the system. The procedure is posed as an optimization problem where the performance index is specific of the fault isolation method to be used. The algorithm that solves the problem is an incremental upgrading approach based in the Sequential Forward Floating Search algorithm and it combines a forward phase (where sensors are added sequentially) with a backward phase (where sensors can be individually removed from the original sensor placement). The proposed methodology is illustrated by means of its application to the problem of leak localization in Water Distribution Networks (WDN), where the particular placement of the pressure sensors has a great impact in the ability to isolate the leaky node. Also, since only a limited number of pressure sensors can be installed in some nodes, space interpolation techniques must be used to estimate the pressure in the other network nodes. The performance of the localization process is measured in terms of pipe distance from the estimated leaky node candidate to the real leaky node. The proposed methodology is applied to two District Metered Areas of the WDN of a metropolitan area of Spain, in simulation and using real measurements and leak scenarios.

Heart Rate Variability and Clinical Features as Predictors of Atrial Fibrillation Recurrence After Catheter Ablation: A Pilot Study.

Single-procedure catheter ablation success rate is as low as 52% in atrial fibrillation (AF) patients. This study evaluated the feasibility of using clinical data and heart rate variability (HRV) features extracted from an implantable cardiac monitor (ICM) to predict recurrences in patients prior to undergoing catheter ablation for AF. HRV-derived features were extracted from the 500 beats preceding the AF onset and from the first 2 min of the last AF episode recorded by an ICM of 74 patients (67% male; 57 ± 12 years; 26% non-paroxysmal AF; 57% AF recurrence) before undergoing their first AF catheter ablation. Two types of classification algorithm were studied to predict AF recurrence: single classifiers including support vector machines, classification and regression trees, and K-nearest neighbor classifiers as well as ensemble classifiers. The sequential forward floating search algorithm was used to select the optimum feature set for each classification method. The optimum weighted voting method, which used an optimum combination of the single classifiers, was the best overall classifier (accuracy = 0.82, sensitivity = 0.76, and specificity = 0.87). Clinical and HRV features can be used to predict rhythm outcome using an ensemble classifier which would enable a more effective pre-ablation patient triage that could reduce the economic and personal burden of the procedure by increasing the success rate of first catheter ablation.

Atrial fibrillation detection using photoplethysmographic signal: the effect of the observation window.

A correct and early diagnosis of cardiac arrhythmias could improve patients' quality of life. The aim of this study is to classify the cardiac rhythm (atrial fibrillation, AF, or normal sinus rhythm NSR) from the photoplethysmographic (PPG) signal and assess the effect of the observation window length. Simulated signals are generated with a PPG simulator previously proposed. The different window lengths taken into account are 20, 30, 40, 50, 100, 150, 200, 250 and 300 beats. After systolic peak detection algorithm, 10 features are computed on the inter-systolic interval series, assessing variability and irregularity of the series. Then, feature selection was performed (using the sequential forward floating search algorithm) which identified two variability parameters (Mean and rMSSD) as the best selection. Finally, the classification by linear support vector machine was performed. Using only two features, accuracy was very high for all the analyzed observation window lengths, going from 0.913±0.055 for length equal to 20 to 0.995±0.011 for length equal to 300 beats.Clinical relevance These preliminary results show that short PPG signals (20 beats) can be used to correctly detect AF.

A product-of-exponential-based robot calibration method with optimal measurement configurations

This article presents a systematic and practical calibration method for an industrial robot to improve its absolute accuracy. The forward kinematics is established based on the global product-of-exponential formula considering some practical constraints. An enhanced partial pose measurement technique is used to construct the linearized error model with only position measurement. All the kinematic parameters are identified via the linear least-squared iteration. The end effector errors are compensated by an inverse Jacobian iteration algorithm in the robot joint space. To suppress the influences of the measurement error, an improved sequential forward floating search algorithm is proposed to select an optimal subset of configurations from a large pool of measured poses based on the D-Optimality. The proposed algorithm is verified via simulations. The calibration method is validated by experiments on an industrial robot, showing that the absolute accuracy of the robot is improved about 10 times under a statistics sense after the calibration.

Detection of atrial fibrillation episodes using a wristband device

Objective: Undiagnosed atrial fibrillation (AF) patients are at high risk of cardioembolic stroke or other complications. The aim of this study was to analyze the blood volume pulse (BVP) signals obtained from a wristband device and develop an algorithm for discriminating AF from normal sinus rhythm (NSR) or from other arrhythmias (ARR). Approach: Thirty patients with AF, 9 with ARR and 31 in NSR were included in the study. The recordings were obtained at rest from Empatica E4 wristband device and lasted 10 min. The analysis, on a 2 min segment, included spectral, variability and irregularity analysis performed on the inter-diastolic interval series, and similarity analysis performed on the BVP signal. Main results and Significance: Variability parameters were the highest in AF, the lowest in NSR and intermediate for ARR, as an example pNN50 values were, respectively, , , (p < 0.05). The similarity parameters were the highest in NSR, the lowest in AF and intermediate for ARR, as an example using a threshold for assessing similarity of : , , , all p < 0.05. The rhythm classification was preceded by over-sampling (using synthetic minority over-sampling technique) the class of ARR, being it the smallest class. Then, the features selection was performed (using the sequential forward floating search algorithm) which identified two variability parameters (pNN70 and pNN40) as the best selection. The classification by the k-nearest neighbor classifier reached an accuracy of about 0.9 for NSR and AF, and 0.8 for ARR. Using pNN70 and pNN40, the specificity for the three rhythms was Spnsr = 0.928, Spaf = 0.963, Sparr = 0.768, while the sensitivity was Spnsr = 0.773, Spaf = 0.754, Sparr = 0.758.

Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study.

ObjectivesTo determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying “triple-negative" breast cancers.Materials and MethodsIn this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation.ResultsImaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p<0.01). Similar improvements were seen in nearly all subtype classification tasks undertaken. Notably, amongst the most discriminating features for predicting triple-negative cancers were textures of background parenchymal enhancement.ConclusionsConsidering the tumor as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative breast cancers. Heterogeneity of background parenchymal enhancement, characterized by quantitative texture features on DCE-MRI, adds value to such differentiation models as they are strongly associated with the triple-negative subtype. Prospective validation studies are warranted to confirm these findings and determine potential implications.

Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method.

Based on the neural efficiency hypothesis and task-induced EEG gamma-band response (GBR), this study investigated the brain regions where neural resource could be most efficiently recruited by the math-gifted adolescents in response to varying cognitive demands. In this experiment, various GBR-based mental states were generated with three factors (level of mathematical ability, task complexity, and short-term learning) modulating the level of neural activation. A feature subset selection method based on the sequential forward floating search algorithm was used to identify an "optimal" combination of EEG channel locations, where the corresponding GBR feature subset could obtain the highest accuracy in discriminating pairwise mental states influenced by each experiment factor. The integrative results from multi-factor selections suggest that the right-lateral fronto-parietal system is highly involved in neural efficiency of the math-gifted brain, primarily including the bilateral superior frontal, right inferior frontal, right-lateral central and right temporal regions. By means of the localization method based on single-trial classification of mental states, new GBR features and EEG channel-based brain regions related to mathematical giftedness were identified, which could be useful for the brain function improvement of children/adolescents in mathematical learning through brain-computer interface systems.

Feature Extraction and Classification of Flaws in Radio Graphical Weld Images Using ANN

In this paper, a novel approach for the detection and classification of flaws in weld images is presented. Computer based weld image analysis is most significant method. The method has been applied for detecting and discriminating flaws in the weld that may corresponds false alarms or all possible nine types of weld defects (Slag Inclusion, Wormhole, Porosity, Incomplete penetration, Under cuts, Cracks, Lack of fusion, Weaving fault Slag line), after being successfully tested on80 radiographic images obtained from EURECTEST, International scientific Association Brussels, Belgium, and 24 radiographs of ship weld provided by Technic Control Co. (Poland) were used, obtained from Ioannis Valavanis Greece.. The procedure to detect all the types of flaws and feature extraction is implemented by segmentation algorithm which can overcome computer complexity problem. Our problem focuses on the high performance classification by optimization of feature set by various selection algorithms like sequential forward search (SFS), sequential backward search algorithm (SBS) and sequential forward floating search algorithm (SFFS). Features are important for measuring parameters which leads in directional to understand image. We introduced 23 geometric features, and 14 texture features. The Experimental results show that our proposed method gives good performance of radiographic images.

Optimization of ECG Classification by Means of Feature Selection

This study tackles the ECG classification problem by means of a methodology, which is able to enhance classification performance while simultaneously reducing the computational resources, making it specially adequate for its application in the improvement of ambulatory settings. For this purpose, the sequential forward floating search (SFFS) algorithm is applied with a new criterion function index based on linear discriminants. This criterion has been devised specifically to be a quality indicator in ECG arrhythmia classification. Based on this measure, a comprehensive feature set is analyzed with the SFFS algorithm, and the most suitable subset returned is additionally evaluated with a multilayer perceptron (MLP) to assess the robustness of the model. Aiming at obtaining meaningful estimates of the real-world performance and facilitating comparison with similar studies, the present contribution follows the Association for the Advancement of Medical Instrumentation standard EC57:1998 and the same interpatient division scheme used in several previous studies. Results show that by applying the proposed methods, the performance obtained in similar studies under the same constraints can be exceeded, while keeping the requirements suitable for ambulatory monitoring

Localisation of cognitive tasks used in EEG-based BCIs

To provide candidate electrode sites and neurophysiological reference information for cognitive tasks used in brain-computer interfacing research. Six cognitive tasks were tested against the idle state. Data representing the idle state were collected with active cognitive task data during each recording session. Cross subject candidate electrode sites were obtained via a wrapper method based upon a sequential forward floating search algorithm. Source localisation results were obtained using sLORETA software. Spatial feature distributions and localisation results are presented. Primary centres of activity for motor imagery tasks are localised to the pre- and postcentral gyrus. Auditory-based tasks show activity in the middle temporal gyrus. Calculation activity was localised to the left inferior frontal gyrus and right supramarginal gyrus. Navigation imagery produced activity in the precuneus and anterior cingulate cortex. Spatial areas of activation suggest that arithmetic and auditory tasks show promise for pairwise discrimination based on single recording sites. sLORETA significance levels suggest that motor imagery tasks will show greatest discrimination from baseline EEG activity. This is the first study to provide candidate electrode sites for multiple tasks used in brain-computer interfacing.