Rosalyn Hobson Hargraves

An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram

This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG.

A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis

Noise can compromise the extraction of some fundamental and important features from biomedical signals and hence prohibit accurate analysis of these signals. Baseline wander in electrocardiogram (ECG) signals is one such example, which can be caused by factors such as respiration, variations in electrode impedance, and excessive body movements. Unless baseline wander is effectively removed, the accuracy of any feature extracted from the ECG, such as timing and duration of the ST-segment, is compromised. This paper approaches this filtering task from a novel standpoint by assuming that the ECG baseline wander comes from an independent and unknown source. The technique utilizes a hierarchical method including a blind source separation (BSS) step, in particular independent component analysis, to eliminate the effect of the baseline wander. We examine the specifics of the components causing the baseline wander and the factors that affect the separation process. Experimental results reveal the superiority of the proposed algorithm in removing the baseline wander.

mi-DS: Multiple-Instance Learning Algorithm

Multiple-instance learning (MIL) is a supervised learning technique that addresses the problem of classifying bags of instances instead of single instances. In this paper, we introduce a rule-based MIL algorithm, called mi-DS, and compare it with 21 existing MIL algorithms on 26 commonly used data sets. The results show that mi-DS performs on par with or better than several well-known algorithms and generates models characterized by balanced values of precision and recall. Importantly, the introduced method provides a framework that can be used for converting other rule-based algorithms into MIL algorithms.

An automated method for analysis of microcirculation videos for accurate assessment of tissue perfusion

BackgroundImaging of the human microcirculation in real-time has the potential to detect injuries and illnesses that disturb the microcirculation at earlier stages and may improve the efficacy of resuscitation. Despite advanced imaging techniques to monitor the microcirculation, there are currently no tools for the near real-time analysis of the videos produced by these imaging systems. An automated system tool that can extract microvasculature information and monitor changes in tissue perfusion quantitatively might be invaluable as a diagnostic and therapeutic endpoint for resuscitation.MethodsThe experimental algorithm automatically extracts microvascular network and quantitatively measures changes in the microcirculation. There are two main parts in the algorithm: video processing and vessel segmentation. Microcirculatory videos are first stabilized in a video processing step to remove motion artifacts. In the vessel segmentation process, the microvascular network is extracted using multiple level thresholding and pixel verification techniques. Threshold levels are selected using histogram information of a set of training video recordings. Pixel-by-pixel differences are calculated throughout the frames to identify active blood vessels and capillaries with flow.ResultsSublingual microcirculatory videos are recorded from anesthetized swine at baseline and during hemorrhage using a hand-held Side-stream Dark Field (SDF) imaging device to track changes in the microvasculature during hemorrhage. Automatically segmented vessels in the recordings are analyzed visually and the functional capillary density (FCD) values calculated by the algorithm are compared for both health baseline and hemorrhagic conditions. These results were compared to independently made FCD measurements using a well-known semi-automated method. Results of the fully automated algorithm demonstrated a significant decrease of FCD values. Similar, but more variable FCD values were calculated using a commercially available software program requiring manual editing.ConclusionsAn entirely automated system for analyzing microcirculation videos to reduce human interaction and computation time is developed. The algorithm successfully stabilizes video recordings, segments blood vessels, identifies vessels without flow and calculates FCD in a fully automated process. The automated process provides an equal or better separation between healthy and hemorrhagic FCD values compared to currently available semi-automatic techniques. The proposed method shows promise for the quantitative measurement of changes occurring in microcirculation during injury.

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising.

A hybrid approach for hemorrhage segmentation in pelvic CT scans

Hemorrhage is the leading cause of death in patients with severe pelvic fractures within the first 24 hours after the injury. Hence, it is vital for physicians to quickly identify hemorrhage and assess bleeding severity. However, it is rather time consuming for physicians to evaluate all the CT images. Therefore, an automated hemorrhage segmentation system is needed to assist physicians. This paper proposes a hybrid approach for hemorrhage segmentation from pelvic CT scans. This approach utilizes region growing technique with integration of contrast information from the previous and subsequent slices. The results show that the method is able to segment hemorrhage well with acceptable results. Hemorrhage volume is also determined. A statistical t-test is conducted to determine if the calculated hemorrhage volume using the proposed method is significantly different from the manually detected volume.

Spleen Segmentation and Assessment in CT Images for Traumatic Abdominal Injuries

Spleen segmentation is especially challenging as the majority of solid organs in the abdomen region have similar gray level range. Physician analysis of computed tomography (CT) images to assess abdominal trauma could be very time consuming and hence, automating this process can reduce time to treatment. The proposed method presented in this paper is a fully automated and knowledge based technique that employs anatomical information to accurately segment the spleen in CT images. The spleen detection procedure is proposed to locate the spleen in both healthy and injured cases. In the presence of hemorrhage and laceration, the edge merging technique is used. The accuracy of the method is measured by some criteria such as mis–segmented area, accuracy, specificity and sensitivity. The results show that the proposed spleen segmentation method performs well and outperforms other methods.

Automated Intracranial Pressure Prediction Using Multiple Features Sources

Raised intracranial pressure (ICP) causes serious problem on traumatic brain injury patient. Automated and non-intrusive ICP level prediction saves cost and enhances efficiency. An automated ICP level prediction model based on machine learning method is proposed in this paper. Multiple features, including midline shift, intracranial air cavities, ventricle size, texture patterns, and blood amount, are selected, extracted and aggregated using different methods. Some demographic information, such as age and injury severity score, is also considered as candidate features. After the feature aggregation, the most important features are selected by a feature selection scheme applied on 10 fold nested cross validation. The final support vector machine classification result using RapidMiner shows the effectiveness of the proposed method in ICP level prediction.

Pilot Study: The Use of Electroencephalogram to Measure Attentiveness towards Short Training Videos

Universities, schools, and training centers are seeking to improve their computer-based [3] and distance learning classes through the addition of short training videos, often referred to as podcasts [4]. As distance learning and computer based training become more popular, it is of great interest to measure if students are attentive to recorded lessons and short training videos. The proposed research presents a novel approach to this issue. Signal processing of electroencephalogram (EEG) has proven useful in measuring attentiveness in a variety of applications such as vehicle operation and listening to sonar [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]. Additionally, studies have shown that EEG data can be correlated to the ability of participants to remember television commercials days after they have seen them [16]. Electrical engineering presents a possible solution with recent advances in the use of biometric signal analysis for the detection of affective (emotional) response [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]. Despite the wealth of literature on the use of EEG to determine attentiveness in a variety of applications, the use of EEG for the detection of attentiveness towards short training videos has not been studied, nor is there a great deal of consistency with regard to specific methods that would imply a single method for this new application. Indeed, there is great variety in EEG signal processing and machine learning methods described in the literature cited above and in other literature [28] [29] [30] [31] [32] [33] [34]. This paper presents a novel method which uses EEG as an input to an automated system that measures a participant’s attentiveness while watching a short training video. This paper provides the results of a pilot study, including a structured comparison of signal processing and machine learning methods to find optimal solutions which can be extended to other applications. Keywords—Electroencephalogram; EEG; Signal Analysis; Machine Learning; Attentiveness; Training; Videos

Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks

Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.