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1. Automatically Detecting Arrhythmia-related Irregular Patterns using the Temporal and Spectro-Temporal Textures of ECG Signals

   https://doi.org/10.1109/ICPR.2018.8546045  

   Abdeldayem, Sara S. ; Bourlai, Thirimachos ( August 2018 , International Conference on Pattern Recognition (ICPR))

   Arrhythmia is an abnormal heart rhythm that occurs due to the improper operation of the electrical impulses that coordinate the heartbeats. It is one of the most well-known heart conditions (including coronary artery disease, heart failure etc.) that is experienced by millions of people around the world. While there are several types of arrhythmias, not all of them are dangerous or harmful. However, there are arrhythmias that can often lead to death in minutes (e.g, ventricular fibrillation and ventricular tachycardia) even in young people. Thus, the detection of arrhythmia is critical for stopping and reversing its progression and for increasing longevity and life quality. While a doctor can perform different heart-monitoring tests specific to arrhythmias, the electrocardiogram (ECG) is one of the most common ones used either independently or in combination with other tests (to only detect, e.g. echocardiogram, or trigger arrhythmia and, then, detect, e.g. stress test). We propose a machine learning approach that augments the traditional arrhythmia detection approaches via our automatic arrhythmia classification system. It utilizes the texture of the ECG signal in both the temporal and spectro-temporal domains to detect and classify four types of heartbeats. The original ECG signal is first preprocessed, and then, the R-peaks associated with heartbeat estimation are identified. Next, 1D local binary patterns (LBP) in the temporal domain are utilized, while 2D LBPs and texture-based features extracted by a grayscale co-occurrence matrix (GLCM) are utilized in the spectro-temporal domain using the short-time Fourier transform (STFT) and Morse wavelets. Finally, different classifiers, as well as different ECG lead configurations are examined before we determine our proposed time-frequency SVM model, which obtains a maximum accuracy of 99.81%, sensitivity of 98.17%, and specificity of 99.98% when using a 10 cross-validation on the MIT-BIH database. 

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2. Spatial versus temporal heterogeneity in abundance of fishes in north-temperate lakes [Spatial versus temporal heterogeneity in abundance of fishes in north-temperate lakes]

   https://doi.org/10.1127/fal/2021/1405  

   Rypel, Andrew L. ( December 2021 , Fundamental and Applied Limnology)
3. Self-Conscious Emotions and the Right Fronto-Temporal and Right Temporal Parietal Junction

   https://doi.org/10.3390/brainsci12020138  

   LaVarco, Adriana ; Ahmad, Nathira ; Archer, Qiana ; Pardillo, Matthew ; Nunez Castaneda, Ray ; Minervini, Anthony ; Keenan, Julian Paul ( February 2022 , Brain Sciences)

   For more than two decades, research focusing on both clinical and non-clinical populations has suggested a key role for specific regions in the regulation of self-conscious emotions. It is speculated that both the expression and the interpretation of self-conscious emotions are critical in humans for action planning and response, communication, learning, parenting, and most social encounters. Empathy, Guilt, Jealousy, Shame, and Pride are all categorized as self-conscious emotions, all of which are crucial components to one’s sense of self. There has been an abundance of evidence pointing to the right Fronto-Temporal involvement in the integration of cognitive processes underlying the expression of these emotions. Numerous regions within the right hemisphere have been identified including the right temporal parietal junction (rTPJ), the orbitofrontal cortex (OFC), and the inferior parietal lobule (IPL). In this review, we aim to investigate patient cases, in addition to clinical and non-clinical studies. We also aim to highlight these specific brain regions pivotal to the right hemispheric dominance observed in the neural correlates of such self-conscious emotions and provide the potential role that self-conscious emotions play in evolution. 

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4. Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization

   https://doi.org/10.1109/TBME.2017.2778007  

   Nahlawi, Layan ; Goncalves, Caroline ; Imani, Farhad ; Gaed, Mena ; Gomez, Jose A. ; Moussa, Madeleine ; Gibson, Eli ; Fenster, Aaron ; Ward, Aaron ; Abolmaesumi, Purang ; et al ( August 2018 , IEEE Transactions on Biomedical Engineering)
5. What to Do When You Can't Do It All: Temporal Logic Planning with Soft Temporal Logic Constraints

   Rahmani, Hahzar ; O'Kane, Jason M. ( January 2020 , IEEE/RSJ International Conference on Intelligent Robots and Systems)

   null (Ed.)

   In this paper, we consider a temporal logic planning problem in which the objective is to find an infinite trajectory that satisfies an optimal selection from a set of soft specifications expressed in linear temporal logic (LTL) while nevertheless satisfying a hard specification expressed in LTL. Our previous work considered a similar problem in which linear dynamic logic for finite traces (LDL_f), rather than LTL, was used to express the soft constraints. In that work, LDL_f was used to impose constraints on finite prefixes of the infinite trajectory. By using LTL, one is able not only to impose constraints on the finite prefixes of the trajectory, but also to set `soft' goals across the entirety of the infinite trajectory. Our algorithm first constructs a product automaton, on which the planning problem is reduced to computing a lasso with minimum cost. Among all such lassos, it is desirable to compute a shortest one. Though we prove that computing such a shortest lasso is computationally hard, we also introduce an efficient greedy approach to synthesize short lassos nonetheless. We present two case studies describing an implementation of this approach, and report results of our experiment comparing our greedy algorithm with an optimal baseline. 

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