pwc-archive/papers-with-abstracts huggingface dataset:
A review on Epileptic Seizure Detection using Machine Learning
Authors: Muhammad Shoaib Farooq, Aimen Zulfiqar, Shamyla Riaz
Date: 2022-10-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG), feature selection, Seizure Detection, Systematic Literature Review

Epilepsy is a neurological brain disorder which life threatening and gives rise to recurrent seizures that are unprovoked. It occurs due to the abnormal chemical changes in our brain. Over the course of many years, studies have been conducted to support automatic diagnosis of epileptic seizures for the ease of clinicians. For that, several studies entail the use of machine learning methods for the early prediction of epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine and then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of feature selection process as well as the classification performance. This study was limited to the finding of most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MPDI, IEEEXplore, Wiley, Elsevier, ACM, Springerlink and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges and opportunities which can further help researchers in prediction of epileptic seizure
Two-stream Network for ECG Signal Classification
Authors: Xinyao Hou, Shengmei Qin, Jianbo Su
Date: 2022-10-05 00:00:00
Tasks: Classification, Diagnostic, Vocal Bursts Valence Prediction

Electrocardiogram (ECG), a technique for medical monitoring of cardiac activity, is an important method for identifying cardiovascular disease. However, analyzing the increasing quantity of ECG data consumes a lot of medical resources. This paper explores an effective algorithm for automatic classifications of multi-classes of heartbeat types based on ECG. Most neural network based methods target the individual heartbeats, ignoring the secrets embedded in the temporal sequence. And the ECG signal has temporal variation and unique individual characteristics, which means that the same type of ECG signal varies among patients under different physical conditions. A two-stream architecture is used in this paper and presents an enhanced version of ECG recognition based on this. The architecture achieves classification of holistic ECG signal and individual heartbeat and incorporates identified and temporal stream networks. Identified networks are used to extract features of individual heartbeats, while temporal networks aim to extract temporal correlations between heartbeats. Results on the MIT-BIH Arrhythmia Database demonstrate that the proposed algorithm performs an accuracy of 99.38\%. In addition, the proposed algorithm reaches an 88.07\% positive accuracy on massive data in real life, showing that the proposed algorithm can efficiently categorize different classes of heartbeat with high diagnostic performance.
Development of a Cobalt Electrochemical Sensor for Measuring Phosphate in Municipal Wastewaters
Authors: Saif S. S. Al Wahaibi, Benjamin D. Martin, Ana Soares
Date: 2022-10-03 00:00:00

The introduction of the Water Framework directive sets stringent limits on phosphorous discharge from wastewater treatment plants to maintain the complex interdependent relationship between water tributaries and the ecosystem. This paper studies a cobalt based electrochemical sensor for phosphate detection in wastewater. An evaluation of the sensors operational envelope, impact of pH, detection limits, linearity of response, accuracy and reproducibility in a single ion solution was conducted. An indirect method was employed to assess the effect of all of these parameters; the parameter was kept constant, while the phosphate concertation was varied. Tests on real wastewater samples verified the effect of the interfering factors, as phosphate measurements from three different sampling points (influent, activated sludge mixed liquors and effluent) did not correlate favourably with measurements acquired from a specialised laboratory. The success of this sensor is probably dependent on the simultaneous measurement of, or the calibration for, interfering parameters. However, the former approach would most likely require additional probes to measure these interfering parameters and the latter would probably require a complex calibrating matrix to account for all the interfering parameters. Nonetheless, variations of such sensors reviewed in this paper and their encouraging results offer an optimistic field of improvement on the design of the sensor studied in this paper for it to be employed on real wastewater systems.
Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond
Authors: Di wu, Jie Yang, Mohamad Sawan
Date: 2022-10-03 00:00:00
Tasks: Electromyography (EMG), Survey, Transfer Learning

Machine learning on electromyography (EMG) has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the assumption that the training and future data must be of the same data distribution. However, this assumption may not hold in many real-world applications. Model calibration is required via data re-collection and label annotation, which is generally very expensive and time-consuming. To address this problem, transfer learning (TL), which aims to improve target learners' performance by transferring the knowledge from related source domains, is emerging as a new paradigm to reduce the amount of calibration effort. In this survey, we assess the eligibility of more than fifty published peer-reviewed representative transfer learning approaches for EMG applications. Unlike previous surveys on purely transfer learning or EMG-based machine learning, this survey aims to provide an insight into the biological foundations of existing transfer learning methods on EMG-related analysis. In specific, we first introduce the physiological structure of the muscles and the EMG generating mechanism, and the recording of EMG to provide biological insights behind existing transfer learning approaches. Further, we categorize existing research endeavors into data based, model based, training scheme based, and adversarial based. This survey systematically summarizes and categorizes existing transfer learning approaches for EMG related machine learning applications. In addition, we discuss possible drawbacks of existing works and point out the future direction of better EMG transfer learning algorithms to enhance practicality for real-world applications.
Cross Task Neural Architecture Search for EEG Signal Classifications
Authors: Yiqun Duan, Zhen Wang, Yi Li, Jianhang Tang, Yu-Kai Wang, Chin-Teng Lin
Date: 2022-10-01 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Emotion Recognition, Motor Imagery, Neural Architecture Search

Electroencephalograms (EEGs) are brain dynamics measured outside the brain, which have been widely utilized in non-invasive brain-computer interface applications. Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition. However, these approaches severely rely on manually designed network structures for different tasks which generally are not sharing the same empirical design cross-task-wise. In this paper, we propose a cross-task neural architecture search (CTNAS-EEG) framework for EEG signal recognition, which can automatically design the network structure across tasks and improve the recognition accuracy of EEG signals. Specifically, a compatible search space for cross-task searching and an efficient constrained searching method is proposed to overcome challenges brought by EEG signals. By unifying structure search on different EEG tasks, this work is the first to explore and analyze the searched structure difference cross-task-wise. Moreover, by introducing architecture search, this work is the first to analyze model performance by customizing model structure for each human subject. Detailed experimental results suggest that the proposed CTNAS-EEG could reach state-of-the-art performance on different EEG tasks, such as Motor Imagery (MI) and Emotion recognition. Extensive experiments and detailed analysis are provided as a good reference for follow-up researchers.
Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning
Authors: Thinh Phan, Duc Le, Patel Brijesh, Donald Adjeroh, Jingxian Wu, Morten Olgaard Jensen, Ngan Le
Date: 2022-09-30 00:00:00
Tasks: ECG Classification, Knowledge Distillation, Rhythm, Self-Knowledge Distillation, Self-Supervised Learning, Time Series, Time Series Analysis

Electrocardiogram (ECG) signal is one of the most effective sources of information mainly employed for the diagnosis and prediction of cardiovascular diseases (CVDs) connected with the abnormalities in heart rhythm. Clearly, single modality ECG (i.e. time series) cannot convey its complete characteristics, thus, exploiting both time and time-frequency modalities in the form of time-series data and spectrogram is needed. Leveraging the cutting-edge self-supervised learning (SSL) technique on unlabeled data, we propose SSL-based multimodality ECG classification. Our proposed network follows SSL learning paradigm and consists of two modules corresponding to pre-stream task, and down-stream task, respectively. In the SSL-pre-stream task, we utilize self-knowledge distillation (KD) techniques with no labeled data, on various transformations and in both time and frequency domains. In the down-stream task, which is trained on labeled data, we propose a gate fusion mechanism to fuse information from multimodality.To evaluate the effectiveness of our approach, ten-fold cross validation on the 12-lead PhysioNet 2020 dataset has been conducted.
The computational and learning benefits of Daleian neural networks
Authors: Adam Haber, Elad Schneidman
Date: 2022-10-12 00:00:00

Dale's principle implies that biological neural networks are composed of neurons that are either excitatory or inhibitory. While the number of possible architectures of such Daleian networks is exponentially smaller than non-Daleian ones, the computational and functional implications of using Daleian networks by the brain are mostly unknown. Here, we use models of recurrent spiking neural networks and rate-based networks to show, surprisingly, that despite the structural limitations on Daleian networks, they can approximate the computation performed by non-Daleian networks to a very high degree of accuracy. Moreover, we find that Daleian networks are more functionally robust to synaptic noise. We then show that unlike non-Daleian networks, Daleian ones can learn efficiently by tuning single neuron features, nearly as well as learning by tuning individual synaptic weights - suggesting a simpler and more biologically plausible learning mechanism. We thus suggest that in addition to architectural simplicity, Dale's principle confers computational and learning benefits for biological networks, and offers new directions for constructing and training biologically-inspired artificial neural networks
Interpretable AI for relating brain structural and functional connectomes
Authors: Haoming Yang, Steven Winter, Zhengwu Zhang, David Dunson
Date: 2022-10-10 00:00:00

One of the central problems in neuroscience is understanding how brain structure relates to function. Naively one can relate the direct connections of white matter fiber tracts between brain regions of interest (ROIs) to the increased co-activation in the same pair of ROIs, but the link between structural and functional connectomes (SCs and FCs) has proven to be much more complex. To learn a realistic generative model characterizing population variation in SCs, FCs, and the SC-FC coupling, we develop a graph auto-encoder that we refer to as Staf-GATE. We trained Staf-GATE with data from the Human Connectome Project (HCP) and show state-of-the-art performance in predicting FC and joint generation of SC and FC. In addition, as a crucial component of the proposed approach, we provide a masking-based algorithm to extract interpretable inferences about SC-FC coupling. Our interpretation methods identified important SC subnetworks for FC coupling and relating SC and FC with sex.
Predictive Event Segmentation and Representation with Neural Networks: A Self-Supervised Model Assessed by Psychological Experiments
Authors: Hamit Basgol, Inci Ayhan, Emre Ugur
Date: 2022-10-04 00:00:00
Tasks: Event Segmentation, Segmentation

People segment complex, ever-changing and continuous experience into basic, stable and discrete spatio-temporal experience units, called events. Event segmentation literature investigates the mechanisms that allow people to extract events. Event segmentation theory points out that people predict ongoing activities and observe prediction error signals to find event boundaries that keep events apart. In this study, we investigated the mechanism giving rise to this ability by a computational model and accompanying psychological experiments. Inspired from event segmentation theory and predictive processing, we introduced a self-supervised model of event segmentation. This model consists of neural networks that predict the sensory signal in the next time-step to represent different events, and a cognitive model that regulates these networks on the basis of their prediction errors. In order to verify the ability of our model in segmenting events, learning them during passive observation, and representing them in its internal representational space, we prepared a video that depicts human behaviors represented by point-light displays. We compared event segmentation behaviors of participants and our model with this video in two hierarchical event segmentation levels. By using point-biserial correlation technique, we demonstrated that event segmentation decisions of our model correlated with the responses of participants. Moreover, by approximating representation space of participants by a similarity-based technique, we showed that our model formed a similar representation space with those of participants. The result suggests that our model that tracks the prediction error signals can produce human-like event boundaries and event representations. Finally, we discussed our contribution to the literature of event cognition and our understanding of how event segmentation is implemented in the brain.
A unified model for the human response to lipopolysaccharide-induced inflammation
Authors: Kristen A. Windoloski, Elisabeth O. Bansgaard, Atanaska Dobreva, Johnny T. Ottesen, Mette S. Olufsen
Date: 2022-10-07 00:00:00

This study develops a unified model predicting the whole-body response to endotoxin. We simulate dynamics using differential equations examining the response to a lipopolysaccharide (LPS) injection. The model tracks pro- and anti-inflammatory cytokines (TNF-$\alpha$, IL-6, IL-10), concentrations of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and cortisol in the hypothalamic-pituitary-adrenal (HPA) axis. Daily hormonal variations are integrated into the model by including circadian oscillations when tracking CRH. Additionally, the model tracks heart rate, blood pressure, body temperature, and pain perception. Studied quantities function on timescales ranging from minutes to days. To understand how endotoxin impacts the body over this vast span of timescales, we examine the response to variations in LPS administration methods (single dose, repeated dose, and continuous dose) as well as the timing of the administration and the amount of endotoxin released into the system. We calibrate the model to literature data for a 2 ng/kg LPS bolus injection. Results show that LPS administration during early morning or late evening generates a more pronounced hormonal response. Most of the LPS effects are eliminated from the body 24 hours after administration, the main impact of inflammation remains in the system for 48 hours, and repeated dose simulations show that residual effects remain more than 10 days after the initial injection. We also show that if the LPS administration method or total dosage is increased, the system response is amplified, posing a greater risk of hypotension and pyrexia.
Retirement spending problem under Habit Formation Model
Authors: S. Kirusheva, H. Huang, T. S. Salisbury
Date: 2022-10-12 00:00:00
Tasks: model

In this paper we consider the problem of optimizing lifetime consumption under a habit formation model. Our work differs from previous results, because we incorporate mortality and pension income. Lifetime utility of consumption makes the problem time inhomogeneous, because of the effect of ageing. Considering habit formation means increasing the dimension of the stochastic control problem, because one must track smoothed-consumption using an additional variable, habit $\bar c$. Including exogenous pension income $\pi$ means that we cannot rely on a kind of scaling transformation to reduce the dimension of the problem as in earlier work, therefore we solve it numerically, using a finite difference scheme. We also explore how consumption changes over time based on habit if the retiree follows the optimal strategy. Finally, we answer the question of whether it is reasonable to annuitize wealth at the time of retirement or not by varying parameters, such as asset allocation $\theta$ and the smoothing factor $\eta$.
Monte-Carlo Estimation of CoVaR
Authors: Weihuan Huang, Nifei Lin, L. Jeff Hong
Date: 2022-10-12 00:00:00

${\rm CoVaR}$ is one of the most important measures of financial systemic risks. It is defined as the risk of a financial portfolio conditional on another financial portfolio being at risk. In this paper we first develop a Monte-Carlo simulation-based batching estimator of CoVaR and study its consistency and asymptotic normality. We show that the optimal rate of convergence of the batching estimator is $n^{-1/3}$, where $n$ is the sample size. We then develop an importance-sampling inspired estimator under the delta-gamma approximations to the portfolio losses, and we show that the rate of convergence of the estimator is $n^{-1/2}$. Numerical experiments support our theoretical findings and show that both estimators work well.
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation
Authors: Ioana Bica, Mihaela van der Schaar
Date: 2022-10-08 00:00:00
Tasks: Representation Learning, Transfer Learning

Consider the problem of improving the estimation of conditional average treatment effects (CATE) for a target domain of interest by leveraging related information from a source domain with a different feature space. This heterogeneous transfer learning problem for CATE estimation is ubiquitous in areas such as healthcare where we may wish to evaluate the effectiveness of a treatment for a new patient population for which different clinical covariates and limited data are available. In this paper, we address this problem by introducing several building blocks that use representation learning to handle the heterogeneous feature spaces and a flexible multi-task architecture with shared and private layers to transfer information between potential outcome functions across domains. Then, we show how these building blocks can be used to recover transfer learning equivalents of the standard CATE learners. On a new semi-synthetic data simulation benchmark for heterogeneous transfer learning we not only demonstrate performance improvements of our heterogeneous transfer causal effect learners across datasets, but also provide insights into the differences between these learners from a transfer perspective.
Changing the Representation: Examining Language Representation for Neural Sign Language Production
Authors: Harry Walsh, Ben Saunders, Richard Bowden
Date: 2022-09-16 00:00:00
Proceeding: SLTAT (LREC) 2022 6
Tasks: Sentence, Sign Language Production, Sign Language Translation, Translation

Neural Sign Language Production (SLP) aims to automatically translate from spoken language sentences to sign language videos. Historically the SLP task has been broken into two steps; Firstly, translating from a spoken language sentence to a gloss sequence and secondly, producing a sign language video given a sequence of glosses. In this paper we apply Natural Language Processing techniques to the first step of the SLP pipeline. We use language models such as BERT and Word2Vec to create better sentence level embeddings, and apply several tokenization techniques, demonstrating how these improve performance on the low resource translation task of Text to Gloss. We introduce Text to HamNoSys (T2H) translation, and show the advantages of using a phonetic representation for sign language translation rather than a sign level gloss representation. Furthermore, we use HamNoSys to extract the hand shape of a sign and use this as additional supervision during training, further increasing the performance on T2H. Assembling best practise, we achieve a BLEU-4 score of 26.99 on the MineDGS dataset and 25.09 on PHOENIX14T, two new state-of-the-art baselines.
Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors
Authors: Vignav Ramesh, Nathan Andrew Chi, Pranav Rajpurkar
Date: 2022-09-27 00:00:00
Tasks: token-classification, Token Classification

Current deep learning models trained to generate radiology reports from chest radiographs are capable of producing clinically accurate, clear, and actionable text that can advance patient care. However, such systems all succumb to the same problem: making hallucinated references to non-existent prior reports. Such hallucinations occur because these models are trained on datasets of real-world patient reports that inherently refer to priors. To this end, we propose two methods to remove references to priors in radiology reports: (1) a GPT-3-based few-shot approach to rewrite medical reports without references to priors; and (2) a BioBERT-based token classification approach to directly remove words referring to priors. We use the aforementioned approaches to modify MIMIC-CXR, a publicly available dataset of chest X-rays and their associated free-text radiology reports; we then retrain CXR-RePaiR, a radiology report generation system, on the adapted MIMIC-CXR dataset. We find that our re-trained model--which we call CXR-ReDonE--outperforms previous report generation methods on clinical metrics, achieving an average BERTScore of 0.2351 (2.57% absolute improvement). We expect our approach to be broadly valuable in enabling current radiology report generation systems to be more directly integrated into clinical pipelines.