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Computers in Cardiology (CinC)

作者： Sven Schellenberger Kilin Shi Melanie Mai Jan P Wiedemann Tobias Steigleder Björn Eskofier Robert Weigel Alexander Kölpin Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany Institute for Electronics Engineering Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany Department of Palliative Care Universitätsklinikum Erlangen Erlangen Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9781728109244

To diagnose sleep disorders, hours of sleep data from lots of different physiological sensors have to be analyzed. To do so, experts have to look through all the data which is time-consuming and error-prone. Automatic detection and classification of sleep related breathing disorders and arousals would significantly simplify this task. This years Physionet/CinC Challenge deals with this topic. This paper examines the use of a Long Short-Term Memory network for automatic arousal detection. On the test set, an AUPRC score of 0.14 was achieved.

关键词： Training Feature extraction Sleep Electroencephalography Physiology Electromyography Electrocardiography

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A Bidirectional LSTM Model for Classifying Chatbot Messages

A Bidirectional LSTM Model for Classifying Chatbot Messages

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Nunthawat Lhasiw Nuttapong Sanglerdsinlapachai Tanatorn Tanantong Faculty of Science and Technology Thammasat University Pathum Thani Thailand Strategic Analytics Networks With Machine Learning and AI Research Team National Electronics and Computer Technology Center Pathum Thani Thailand Thammasat Research Unit in Data Innovation and Artificial Intelligence Faculty of Science and Technology Thammasat University Pathum Thani Thailand

ISBN: (纸本)9781665409483

Online channels, e.g., Facebook Messenger and Line, are widely used especially in COVID-19 pandemic. To quickly respond to their customer, chatbot system are implemented in many companies or organizations, connected to those channels. The Office of Registrar, Thammasat University also implements a chatbot to answer questions from students. An important step in the chatbot system is to know an intention of a question message. A bidirectional LSTM model is employed to classify a question message from the chatbot system into five intention classes. The experimental results shows that the obtained model yields an accuracy of 0.80 on our validation dataset.

关键词： Training COVID-19 Social networking (online) Pandemics data integrity Memory management Companies

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WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography

WebPPG: Feasibility and Usability of Self-Performed, Browser...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Michael Nissen Madeleine Flaucher Katharina M. Jaeger Hanna Huebner Nina Danzberger Adriana Titzmann Constanza A. Pontones Peter A. Fasching Bjoern M. Eskofier Heike Leutheuser Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Gynecology and Obstetrics Erlangen University Hospital Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made available to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser’s insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical *** relevance— WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation

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BIM: Improving Graph Neural Networks with Balanced Influence Maximization

BIM: Improving Graph Neural Networks with Balanced Influence...

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International Conference on data Engineering

作者： Wentao Zhang Xinyi Gao Ling Yang Meng Cao Ping Huang Jiulong Shan Hongzhi Yin Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Key Lab of High Confidence Software Technologies Peking University Apple Inc. Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score.

关键词： Training Industries data engineering Graph neural networks

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A Smart Electronic Wheelchair Design Using Artificial Intelligence and Intelligent Sensors Association 2

A Smart Electronic Wheelchair Design Using Artificial Intell...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Balamurugan, R. Kanchana, D. Anni Princy, B. Jaisudha, J. Malathi, P. Glory, E. Ajk College of Arts and Science Department of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India Srm Institute of Science and Technology Department of Computer Applications Tamil Nadu Chennai India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Tamil Nadu Chennai India Simats Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair's controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair's DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user's pulse rate, as well as the wheelchair's functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner. © 2024 IEEE.

关键词： Accelerator AI Artificial Intelligence Batteries DC Power Intelligent Sensors Mobile-Wheelchair MWC Wheelchair

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A Lightweight Robust Approach for Automatic Heart Murmurs and Clinical Outcomes Classification from Phonocardiogram Recordings

A Lightweight Robust Approach for Automatic Heart Murmurs an...

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Computers in Cardiology (CinC)

作者： Hui Lu Julia Beatriz Yip Tobias Steigleder Stefan Grießhammer Maria Heckel Naga Venkata Sai Jitin Jami Bjoern Eskofier Christoph Ostgathe Alexander Koelpin Chair of Electronics and Sensor Systems Brandenburg University of Technology Cottbus Germany Institute of High-Frequency Technology Hamburg University of Technology Hamburg Germany Department of Palliative Medicine Universitätsklinikum Erlangen Erlangen Germany Machine Learning and Data Analytics (MaD) Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

Cardiac auscultation provides an efficient and cost-effective way for cardiac disease pre-screening. The George B. Moody PhysioNet Challenge 2022 aimed to detect heart murmurs and clinical outcomes with heart sound recordings from multiple auscultation locations. Our team HearHeart proposed a lightweight convolutional neural network (CNN) to detect heart murmurs and a random forest model to classify clinical outcomes. 128 Melspectrogram features and wide features like the socio-demographic data and statistical features are extracted. Different techniques are employed to migrate the data imbalance and model the overfitting problem. We used two data augmentation methods, noise injection and spectrogram augmentation in time and frequency domain to increase the training samples and avoid overfitting during training. Besides, weighted loss functions are applied to both tasks to deal with data imbalance. In the end, we ensembled the models from cross-validation and used voting for the final classification. We achieved a murmur score of 0.791, and a clinical outcome score of 11731.64 on 5-fold cross-validation in the hidden validation set. While on the hidden test set, we achieved a murmur score of 0.780, and a clinical outcome score of 12110, placing our team 1st and 10th in the challenge tasks, respectively.

关键词： Heart Training Frequency-domain analysis Feature extraction data models Recording Convolutional neural networks

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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ICDAR 2021 competition on on-line signature verification

arXiv

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arXiv 2021年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Gonzalez-Garcia, Carlos Fierrez, Julian Rengifo, Santiago Morales, Aythami Ortega-Garcia, Javier Ruiz-Garcia, Juan Carlos Romero-Tapiador, Sergio Jiang, Jiajia Lai, Songxuan Jin, Lianwen Zhu, Yecheng Galbally, Javier Diaz, Moises Ferrer, Miguel Angel Gomez-Barrero, Marta Hodashinsky, Ilya Sarin, Konstantin Slezkin, Artem Bardamova, Marina Svetlakov, Mikhail Saleem, Mohammad Szücs, Cintia Lia Kovari, Bence Pulsmeyer, Falk Wehbi, Mohamad Zanca, Dario Ahmad, Sumaiya Mishra, Sarthak Jabin, Suraiya Biometrics and Data Pattern Analytics Lab UAM Spain South China University of Technology China Guangdong Artificial Intelligence and Digital Economy Laboratory China European Commission Joint Research Centre Italy Universidad del Atlantico Medio Spain Universidad de las Palmas de Gran Canaria Spain Hochschule Ansbach Germany Tomsk State University of Control Systems and Radioelectronics Russia Budapest University of Technology and Economics Hungary Machine Learning and Data Analytics Lab FAU Germany Jamia Millia Islamia India

This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). SVC 2021 will be established as an on-going competition12, where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases such as DeepSignDB13 and SVC2021_EvalDB14, and standard experimental protocols. © 2021, CC BY-NC-ND.

关键词： Deep learning

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Recurrent Neural Networks on Drifting Time-of-Flight Measurements

Recurrent Neural Networks on Drifting Time-of-Flight Measure...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Tobias Feigl Thorsten Nowak Michael Philippsen Thorsten Edelhäußer Christopher Mutschler Programming Systems Group (Informatics II) Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Institute of Information Technology (Comm. Electronics) Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Precise Positioning and Analytics Department Fraunhofer Institute for Integrated Circuits IIS Nürnberg Nürnberg Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany

Kalman filters (KFs) are popular methods to estimate position information from a set of time-of-flight (ToF) values in radio frequency (RF)-based locating systems. Such filters are proven to be optimal under zero-mean Gaussian error distributions. In presence of multipath propagation ToF measurement errors drift due to small-scale motion. This results in changing phases of the multipath components (MPCs) which cause a drift on the ToF measurements. Thus, on a short-term scale the ToF measurements have a non-constant bias that changes while moving. KFs cannot distinguish between the drifting measurement errors and the true motion of the tracked object. Hence, very rigid motion models have to be used for the KF which commonly causes the filters to diverge. Therefore, the KF cannot resolve the short-term errors of consecutive measurements and the long-term motion of the tracked object. This paper presents a data-driven approach that uses training sequences to derive a near-optimal position estimator. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) learns to interpret drifting errors in ToF measurements of a tracked dynamic object directly from raw ToF data. Our evaluation shows that our approach outperforms state-of-the-art KFs on both synthetically generated and real-world dynamic motion trajectories that include drifting ToF measurement errors.

关键词： Trajectory Hidden Markov models Tracking Measurement uncertainty Neural networks Position measurement Dynamics

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Achieving Linear Speedup with Network-Independent learning Rates in Decentralized Stochastic Optimization

Achieving Linear Speedup with Network-Independent Learning R...

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IEEE Conference on Decision and Control

作者： Hao Yuan Sulaiman A. Alghunaim Kun Yuan Center for Machine Learning Research Peking University Beijing P. R. China Dept. Electrical Engr. Kuwait University Safat Kuwait AI for Science Institute Beijing P. R. China National Engineering Labratory for Big Data Analytics and Applications Beijing P. R. China

Decentralized stochastic optimization has become a crucial tool for addressing large-scale machine learning and control problems. In decentralized algorithms, all computing nodes are connected through a network topology, and each node communicates only with its direct neighbors. Decentralized algorithms can significantly reduce communication overhead by eliminating the need for global communication. However, existing research on the linear speedup analysis of decentralized stochastic algorithms is limited to the condition of network-dependent learning rates, which rarely holds in practice since the network connectivity is typically unknown to each node. As a result, it remains an open question whether a linear speedup bound can be achieved using network-independent learning rates. This paper provides an affirmative answer. By utilizing a new analysis framework, we prove that D-SGD and Exact-Diffusion, two representative decentralized stochastic algorithms, can achieve linear speedup with network-independent learning rates. Simulations are provided to validate our theories.

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