Invited plenary speakers include:

A Penn State CHOT team's research on developing a novel method to utilize virtual reality (VR) and eye tracking systems to assess mental health disorders was featured in IISE Magazine. VR offers a new means of the immersive environment to assess mental disorders than traditional "gold-standard" cognitive tests. This, in turn, provides a unique opportunity for health care training and treatment of mental disorders in a safe, controlled environment. 

The virtual reality environment simulates a medical clinic where patients take part in mental health assessment tasks designed to mimic common mental health simulations conducted in nursing labs. The research also investigates the effectiveness of the use of intelligent agents for effective metacognitive strategies. These models combine user inputs with the data collected from external sensors to improve metacognitive interventions of mental disorders. Click here to read the full article.

Ruimin Chen, a CHOT scholar, developed a novel method based on heterogeneous recurrence analysis to detect disease-altered spatiotemporal patterns in cardiac signals. Heart diseases alter the rhythmic behaviors of cardiac electrical activity. Recent advances in sensing technology bring the ease to acquire space-time electrical activity of the heart such as vectorcardiogram (VCG) signals. Recurrence analysis of successive heartbeats is conducive to detect the disease-altered cardiac activities. However, conventional recurrence analysis is more concerned about homogeneous recurrences, and overlook heterogeneous types of recurrence variations in VCG signals (i.e., in terms of state properties and state transition dynamics). This work presents a new framework of heterogeneous recurrence analysis for the characterization and modeling of disease-altered spatiotemporal patterns in multi-channel cardiac signals. Experimental results show that the proposed approach yields an accuracy of 96.9%, a sensitivity of 95.0%, and a specificity of 98.7% for the identification of myocardial infarctions. The proposed method of heterogeneous recurrence analysis shows strong potential to be further extended for the analysis of other physiological signals such as electroencephalogram (EEG) and electromyography (EMG) signals toward medical decision making. The INFORMS Annual Meeting will be held virtually Nov. 7-13. View the video presentation by the author below.

Adam Meyers, a CHOT scholar, developed a novel cross recurrence analysis (CRQA) method for pattern matching of physiological signals. CRQA, based on the cross recurrence plot (CRP), is an effective method to characterize and quantify the nonlinear interrelationships between a pair of nonlinear time series. It allows the flexibility of reconstructing signals in phase space and to identify different types of patterns at arbitrary positions between trajectories. These advantages make CRQA attractive for time series data mining tasks, which have been of recent interest in the literature. However, little has been done to exploit CRQA for pattern matching of multidimensional, especially spatiotemporal, physiological signals. In this work, the authors present a novel methodology in which CRQA statistics serve as measures of dissimilarity between pairs of signals and are subsequently used to uncover clusters within the data. This methodology is evaluated on a real dataset consisting of 3D spatiotemporal vectorcardiogram (VCG) signals from healthy and diseased patients. Experimental results show that the length of the longest diagonal line in the CRP yields the best-performing clustering that almost exactly matches the ground truth diagnoses of patients. Results also show that our proposed measure, which characterizes the maximum similarity between signals over all pairwise time-delayed alignments, outperforms all other tested CRQA measures (in terms of matching the ground truth) when the VCG signals are rescaled to reduce the effects of signal amplitude. View the video presentation by the author below.

If you have any exciting news that you would like to feature in our CHOT newsletter, please submit it to Haedong Kim, newsletter editor, at huk344@psu.edu.