Detection Of Abnormal Patterns Research Articles

Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

Ventricular fibrillation (VF) is a critical ventricular arrhythmia with severe consequences. Due to the severity of VF, it urgently requires a rapid and accurate detection of abnormal patterns in ECG signals. Here, we present an efficient method to detect abnormal electrocardiogram (ECG) signals associated with VF by measuring orthogonality between intrinsic mode functions (IMFs) derived from a data-driven decomposition method, namely, ensemble empirical mode decomposition (EEMD). The proposed method incorporates the decomposition of the ECG signal into its IMFs using EEMD, followed by the computation of the angles between subsequent IMFs, especially low-order IMFs, as the features to discriminate normal and abnormal ECG patterns. The proposed method was validated through experiments using a public MIT-BIH ECG dataset for its effectiveness in detecting VF ECG signals compared to conventional methods. The proposed method achieves a sensitivity of 99.22%, a specificity of 99.37%, and an accuracy of 99.28% with a 3 s ECG window and a support vector machine (SVM) with a linear kernel, which performs better than existing VF detection methods. The capability of the proposed method can provide a perspective approach for the real-time and practical computer-aided diagnosis of VF.

Detection of Abnormal Patterns in Children’s Handwriting by Using an Artificial-Intelligence-Based Method

Using camera-based algorithms to detect abnormal patterns in children’s handwriting has become a promising tool in education and occupational therapy. This study analyzes the performance of a camera- and tablet-based handwriting verification algorithm to detect abnormal patterns in handwriting samples processed from 71 students of different grades. The study results revealed that the algorithm saw abnormal patterns in 20% of the handwriting samples processed, which included practices such as delayed typing speed, excessive pen pressure, irregular slant, and lack of word spacing. In addition, it was observed that the detection accuracy of the algorithm was 95% when comparing the camera data with the abnormal patterns detected, which indicates a high reliability in the results obtained. The highlight of the study was the feedback provided to children and teachers on the camera data and any abnormal patterns detected. This can significantly impact students’ awareness and improvement of writing skills by providing real-time feedback on their writing and allowing them to adjust to correct detected abnormal patterns.

A new multimodal paradigm for biomarkers longitudinal monitoring: a clinical application to women steroid profiles in urine and blood

BackgroundMost current state-of-the-art strategies to generate individual adaptive reference ranges are designed to monitor one clinical parameter at a time. An innovative methodology is proposed for the simultaneous longitudinal monitoring of multiple biomarkers. The estimation of individual thresholds is performed by applying a Bayesian modeling strategy to a multivariate score integrating several biomarkers (compound concentration and/or ratio). This multimodal monitoring was applied to data from a clinical study involving 14 female volunteers with normal menstrual cycles receiving testosterone via transdermal route, as to test its ability to detect testosterone administration. The study samples consisted of urine and blood collected during 4 weeks of a control phase and 4 weeks with a daily testosterone gel application. ResultsIntegrating multiple biomarkers improved the detection of testosterone gel administration with substantially higher sensitivity compared with the distinct follow-up of each biomarker, when applied to selected urine and serum steroid biomarkers, as well as the combination of both. Among the 175 known positive samples, 38% were identified by the multimodal approach using urine biomarkers, 79% using serum biomarkers and 83% by combining biomarkers from both biological matrices, whereas 10%, 67% and 64% were respectively detected using standard unimodal monitoring. Significance and noveltyThe detection of abnormal patterns can be improved using multimodal approaches. The combination of urine and serum biomarkers reduced the overall number of false-negatives, thus evidencing promising complementarity between urine and blood sampling for doping control, as highlighted in the case of the use of transdermal testosterone preparations. The generation in a multimodal setting of adaptive and personalized reference ranges opens up new opportunities in clinical and anti-doping profiling. The integration of multiple parameters in a longitudinal monitoring is expected to provide a more complete evaluation of individual profiles generating actionable intelligence to further guide sample collection, analysis protocols and decision-making in clinics and anti-doping.

Voxelwise and Regional Brain Apparent Diffusion Coefficient Changes on MRI from Birth to 6 Years of Age.

Background A framework for understanding rapid diffusion changes from 0 to 6 years of age is important in the detection of neurodevelopmental disorders. Purpose To quantify patterns of normal apparent diffusion coefficient (ADC) development from 0 to 6 years of age. Materials and Methods Previously constructed age-specific ADC atlases from 201 healthy full-term children (108 male; age range, 0-6 years) with MRI scans acquired from 2006 to 2013 at one large academic hospital were analyzed to quantify four patterns: ADC trajectory, rate of ADC change, age of ADC maturation, and hemispheric asymmetries of maturation ages. Patterns were quantified in whole-brain, segmented regional, and voxelwise levels by fitting a two-term exponential model. Hemispheric asymmetries in ADC maturation ages were assessed using t tests with Bonferroni correction. Results The posterior limb of the internal capsule (mean ADC: left hemisphere, 1.18 ×103μm2/sec; right hemisphere, 1.17 ×103μm2/sec), anterior limb of the internal capsule (left, 1.11 ×103μm2/sec; right, 1.09 ×103μm2/sec), vermis (1.26 ×103μm2/sec), thalami (left, 1.17 ×103μm2/sec; right, 1.15 ×103μm2/sec), and basal ganglia (left, 1.26 ×103μm2/sec; right, 1.23 ×103μm2/sec) demonstrate low initial ADC values, indicating an earlier prenatal time course of development. ADC maturation was completed between 1.3 and 2.4 years of age, depending on the region. The vermis and left thalamus matured earliest (1.3 years). The frontolateral gray matter matured latest (right, 2.3 years; left, 2.4 years). ADC maturation occurred earlier in the left hemisphere (P < .001) in several regions, including the frontal (mean ± standard deviation) (left, 2.16 years ± 0.29; right, 2.19 years ± 0.31), temporal (left, 1.93 years ± 0.22; right, 1.99 years ± 0.22), and parietal (left, 1.92 years ± 0.30; right, 2.03 years ± 0.28) white matter. Maturation occurred earlier in the right hemisphere (P < .001) in several regions, including the thalami (left, 1.63 years ± 0.32; right, 1.45 years ± 0.33), basal ganglia (left, 1.79 years ± 0.31; right, 1.70 years ± 0.37), and hippocampi (left, 1.93 years ± 0.34; right, 1.78 years ± 0.33). Conclusion Normative apparent diffusion coefficient developmental patterns on diffusion-weighted MRI scans were quantified in children aged 0 to 6 years. This work provides knowledge about early brain development and may guide the detection of abnormal patterns of maturation. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Rollins in this issue.

Design and Modeling of Resource-constrained IoT based Body Area Networks

Due to the recent advancement and development of sensing, wireless, and communication technologies, therehas been a shift in attention towards Body Area Networks (BANs). One of the most important services of BAN is the remote monitoring of patients, enabling doctors to observe, diagnose, and prescribe the patients without being physically present. Various vital signs are being monitored by body sensing devices installed inside, on or off the body of patients, but most of these devices are constrained in terms of resources such as storage, processing, bandwidth, and energy due to their smaller size. This paper aims at highlighting the key findings related to BAN applications, constrained resources, and various resource management techniques. The paper also presents the design and modeling of a resource-constrained BAN system and discusses the various scenarios of BAN in the context of resource constraints. It further proposes an Advanced Edge Clustering (AEC) approach to manage the resources such as energy, storage, and processing of BAN devices while performing real-time data capture of critical health parameters and detection of abnormal patterns. The comparison of the AEC approach is done with the Stable Election Protocol (SEP) through simulations and empirical data analysis. The results show an improvement in energy, processing time and storage requirements for the processing of data on BAN devices in AEC as compared to SEP.

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

Using Syndromic Data for Opioid Overdose Surveillance in Utah

Objective: To monitor opioid-related overdose in real-time using emergency department visit data and to develop an opioid overdose surveillance report for Utah Department of Health (UDOH) and its public health partners.Introduction: The current surveillance system for opioid-related overdoses at UDOH has been limited to mortality data provided by the Office of the Medical Examiner (OME). Timeliness is a major concern with OME data due to the considerable lag in its availability, often up to six months or more. To enhance opioid overdose surveillance, UDOH has implemented additional surveillance using timely syndromic data to monitor fatal and nonfatal opioid-related overdoses in Utah.Methods: As one of the agencies participating in the National Syndromic Surveillance Program (NSSP), UDOH submits de-identified data on emergency department visit from Utah’s hospitals and urgent care facilities in close to real-time to the NSSP platform. Emergency department visit data are available for analysis using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) system provided by NSSP. ESSENCE provides UDOH with patient-level syndromic data for analysis and early detection of abnormal patterns in emergency visits. A total of 38 out of 48 acute care hospitals and multiple urgent care facilities are enrolled in the system in Utah. More than 90% of these hospitals report chief complaint data, and discharge data are available from about 15% of the facilities. Data were analyzed by querying key terms in the chief complaint field including: any entry of: ‘overdose’, drug and brand names for opioids, street names, ‘naloxone’, and miss-spellings. Exclusion terms included any mention of: ‘denies’, ‘quit’, ‘refill’, ‘withdraw’, ‘dependence’, etc. Data containing any ICD entry of: T40.0-T40.4, T40.60, and T40.69 were included in the analysis.Results: Between September 1, 2016 and August 31, 2017, Utah Department of Health identified 4,063 opioid-related overdose emergency department (ED) visits through the ESSENCE system using both chief complaint and discharge diagnosis queries. Of these visits, 3,865 (95%) were identified using chief complaints alone and 198 (5%) visits were added by searching the discharge diagnosis field. Opioid-related visits comprised approximately 0.3% of the total ED visits (1,267,244) reported during this time (Graph 1). More than half of the opioid-related emergency visits were reported from just five facilities. Rate of opioid-related visits ranging from 0 to 292 visits per 100,000 population per year (median: 108 visits per 100,000 population per year), with an overall rate for the state of 129 visits per100, 000 population per year. The highest rate of opioid-related visits occurred among patients aged 18 to 24 (219 visits per 100,000 population per year), and 59% of all opioid-related patients in Utah were female.Conclusions: The results presented are estimates of opioid-related overdoses reported using close to real-time data. These results would not include visits with incomplete or incorrectly coded chief complaints or discharge codes, or cases of opioid overdose who do not present to an emergency department or urgent care facility. The results from using syndromic data are consistent with existing surveillance findings using mortality data in Utah. This suggests that syndromic surveillance data are useful for rapidly capturing opioid events, which may allow for a timelier public health response. UDOH is currently evaluating syndromic surveillance data versus hospital discharge data for opioid-related emergency department visits, which may further optimize queries in ESSENCE, in order to provide improved opioid surveillance data to local public health partners. This analysis demonstrates that using syndromic surveillance data provides a more time-efficient alternative, enabling more rapid public health interventions, which improved opportunities to reduce opioid-related morbidity and mortality in Utah.

A robust unsupervised consensus control chart pattern recognition framework

Early identification and detection of abnormal patterns is vital for a number of applications. In manufacturing for example, slide shifts and alterations of patterns might be indicative of some production process anomaly, such as machinery malfunction. Usually due to the continuous flow of data, monitoring of manufacturing processes and other types of applications requires automated control chart pattern recognition (CCPR) algorithms. Most of the CCPR literature consists of supervised classification algorithms. Fewer studies consider unsupervised versions of the problem. Despite the profound advantage of unsupervised methodology for less manual data labeling their use is limited due to the fact that their performance is not robust enough and might vary significantly from one algorithm to another. In this paper, we propose the use of a consensus clustering framework that takes care of this shortcoming and produces results that are robust with respect to the chosen pool of algorithms. Computational results show that the proposed method achieves not less than 79.10% G-mean with most of test instances achieving higher than 90%. This happens even when in the algorithmic pool are included algorithms with performance less than 15%. To our knowledge, this is the first paper proposing an unsupervised consensus learning approach in CCPR. The proposed approach is promising and provides a new research direction in unsupervised CCPR literature.

Advanced BrainAGE in older adults with type 2 diabetes mellitus

Aging alters brain structure and function and diabetes mellitus (DM) may accelerate this process. This study investigated the effects of type 2 DM on individual brain aging as well as the relationships between individual brain aging, risk factors, and functional measures. To differentiate a pattern of brain atrophy that deviates from normal brain aging, we used the novel BrainAGE approach, which determines the complex multidimensional aging pattern within the whole brain by applying established kernel regression methods to anatomical brain magnetic resonance images (MRI). The “Brain Age Gap Estimation” (BrainAGE) score was then calculated as the difference between chronological age and estimated brain age. 185 subjects (98 with type 2 DM) completed an MRI at 3Tesla, laboratory and clinical assessments. Twenty-five subjects (12 with type 2 DM) also completed a follow-up visit after 3.8 ± 1.5 years. The estimated brain age of DM subjects was 4.6 ± 7.2 years greater than their chronological age (p = 0.0001), whereas within the control group, estimated brain age was similar to chronological age. As compared to baseline, the average BrainAGE scores of DM subjects increased by 0.2 years per follow-up year (p = 0.034), whereas the BrainAGE scores of controls did not change between baseline and follow-up. At baseline, across all subjects, higher BrainAGE scores were associated with greater smoking and alcohol consumption, higher tumor necrosis factor alpha (TNFα) levels, lower verbal fluency scores and more severe deprepession. Within the DM group, higher BrainAGE scores were associated with longer diabetes duration (r = 0.31, p = 0.019) and increased fasting blood glucose levels (r = 0.34, p = 0.025). In conclusion, type 2 DM is independently associated with structural changes in the brain that reflect advanced aging. The BrainAGE approach may thus serve as a clinically relevant biomarker for the detection of abnormal patterns of brain aging associated with type 2 DM.

A System for Outlier Detection of High Dimensional Data

In high dimensional data large no of outliers are embedded in low dimensional subspaces known as projected outliers, but most of existing outlier detection techniques are unable to find these projected outliers, because these methods perform detection of abnormal patterns in full data space. So, outlier detection in high dimensional data becomes an important research problem. In this paper we are proposing an approach for outlier detection of high dimensional data. Here we are modifying the existing SPOT approach by adding three new concepts namely Adaption of Sparse Sub-Space Template (SST), Different combination of PCS parameters and set of non outlying cells for testing data set.

EFFECT OF MOVEMENT SPEED ON CERVICAL MUSCLE ACTIVATION PATTERN IN HEALTHY ADULTS: VOLUNTARY RESPONSE INDEX ANALYSIS

INTRODUCTION People with chronic neck pain demonstrate changes in motor control pattern [1]. The early detection of abnormal patterns is important for preventing the neck disorders. Recently, voluntary response index (VRI) is proposed as a sensitive measure of voluntary motor control in the differentiation of abnormality [2,3]. It contains the similarity index (SI) and the measurement of the magnitude of EMG activities (MAG). Although the speed is believed to significantly affect the muscle activity in extremities, there is lack of studies to examine the different speeds of cervical movements on the surface electromyographic activity (sEMG) of cervical muscles. The purpose of the study accordingly was to evaluate the effect of different speeds on the VRI of cervical muscles during different directions of movement.

Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes

Non-random (abnormal) behaviour indicates that a process is under the influence of special causes of variation. Detection of abnormal patterns is well established in univariate statistical process control (SPC). Various solutions including heuristics, traditional computer programming, expert systems and neural networks (NNs) have been successfully implemented. In multivariate SPC (MSPC), on the other hand, there is a clear need for more investigations into pattern detection. Bivariate SPC is a special case of MSPC where the number of variates is two and is studied here in terms of identification of shift patterns. In this work, an existing NN classification technique—known as novelty detection (ND)—whose application for MSPC has not been reported is applied for pattern recognition. ND successfully detects non-random bivariate time-series patterns representing shifts of various magnitudes in the process mean vector. The investigation proposes a simple heuristic approach for applying ND as an effective and useful tool for pattern detection in bivariate SPC with potential applicability for MSPC in general.

Efficient control chart pattern recognition through synergistic and distributed artificial neural networks

Accurate and fast control chart pattern recognition is essential for efficient system monitoring to maintain the production of high-quality goods. This paper addresses three major issues of control chart pattern recognition: (a) transparency, (b) accuracy and (c) fast detection of abnormal patterns. A new approach is described which uses novel shape features extracted from a control chart pattern (CCP) instead of the unprocessed CCP data or its statistical properties. These features represent the shape of the CCP explicitly. A set of algorithms is described for extraction of the shape features from a CCP. The paper discusses the use of artificial neural networks for recognition of the shape features. Synergistic, distributed and distributed synergistic neural networks are proposed for learning efficiently non-linear characteristics and overlapping ranges of values of the data set describing CCPs. The paper presents the results of analysing several hundred control chart patterns and gives a comparison with those reported in previous work.

A Probabilistic Resource Allocating Network for Novelty Detection

The detection of novel or abnormal input vectors is of importance in many monitoring tasks, such as fault detection in complex systems and detection of abnormal patterns in medical diagnostics. We have developed a robust method for novelty detection, which aims to minimize the number of heuristically chosen thresholds in the novelty decision process. We achieve this by growing a gaussian mixture model to form a representation of a training set of “normal” system states. When previously unseen data are to be screened for novelty we use the same threshold as was used during training to define a novelty decision boundary. We show on a sample problem of medical signal processing that this method is capable of providing robust novelty decision boundaries and apply the technique to the detection of epileptic seizures within a data record.

重心動揺のスペクトル解析の再検討

Body sway was analyzed in seven healthy subjects and three patients with central disorders. Power spectra were calculated by three different methods: 1) fast F ourier transform [FFT] with no smoothing procedures [degree of freedom 2],2) FFT with smoothing treatment [degree of freedom 26], and the maximum entropy method [MEM].Although the spectra obtained from FFT with no smoothing procedures varied widely, those calculated with the second two methods were very stable and allowed detection of abnormal patterns.

Application of Multivariate Analysis to the Enzyme Patterns in the Serum of Patients Undergoing Coronary Artery Operation

Serial determinations of serum glutamic oxaloacetic transaminase, lactic dehydrogenase, and creatine phosphokinase were performed in 50 consecutive patients undergoing cardiac operation for coronary artery bypass or combined valve replacement and coronary artery bypass. Thirty-seven patients (74%) who demonstrated minimal or no changes on the electrocardiogram manifested a recognizable pattern of distribution of the enzyme sequences. The pattern of these patients served as controls for the detection of abnormal patterns. All other patients were grouped together, regardless of clinical behavior. Perioperative myocardial infarction was established in 5 patients (10%) and resulted in 1 death. The purpose of this study was to apply discriminant analysis to two clinically determined patient groups in order to ascertain whether the three enzyme readings can be used to classify patients into their respective groups. The results suggest that enzyme profiles reflect degrees of myocardial damage that can serve to identify clinical infarction.

Evaluation of lactate dehydrogenase isoenzyme patterns in serum of patients undergoing cardiac surgery

Serial determinations of serum lactate dehydrogenase (LD) isoenzymes were performed in 50 patients undergoing cardiac surgery for coronary artery bypass and heart valve replacement. A sequence of LD isoenzyme patterns was established in the patients with uncomplicated recovery. These patterns served as controls for the detection of abnormal patterns associated with clinical complications. Perioperative myocardial infarction was detected in seven patients by a characteristic reversal of the LD1:LD2 ratio. These studies established that accurate determination of LD isoenzymes improved their diagnostic specificity. These isoenzymes were determined by (1) using the same technique for tissues and for serum samples, (2) applying a volume of serum containing a standard amount of enzymatic activity, and (3) making a clear separation, which allows accurate quantitation. This test can provide useful information to the surgeon for the evaluation of operative procedures and to the clinician for the appropriate management of the patient undergoing cardiac surgery.