Browsing by Subject "LSTM"

Now showing 1 - 4 of 4
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    Deep ECG Mining for Arrhythmia Detection Towards Precision Cardiac Medicine
    (2024-08) Patnaik, Shree; Zhang, Qingxue; Schubert, Peter J.; King, Brian
    Cardiac disease is one of the prominent reasons of deaths worldwide. The timely de tection of arrhythmias, one of the highly prevalent cardiac abnormalities, is very important and promising for treatment. Electrocardiography (ECG) is well applied to probe the car diac dynamics, nevertheless, it is still challenging to robustly detect the arrhythmia with automatic algorithms, especially when the noise may contaminate the signal to some extent. In this research study, we have not only built and assessed different neural network models to understand their capability in terms of ECE-based arrhythmia detection, but also com prehensively investigated the detection under different kinds of signal-to-noise ratio (SNR). Both Long Short-Term Memory (LSTM) model and Multi-Layer Perception (MLP) model have been developed in the study. Further, we have studied the necessity of fine-tuning of the neural network models, which are pre-trained on other data and demonstrated that it is very important to boost the performance when ECG is contaminated by noise. In the experiments, the LSTM model achieves an accuracy of 99.0%, F1 score of 97.9%, and high precision and recall, with the clean ECE signal. Further, in the high SNR scenario, the LSTM maintains an attractive performance. With the low SNR scenario, though there is some performance drop, the fine-tuning approach helps performance improvement critically. Overall, this study has built the neural network models, and investigated different kinds of signal fidelity including clean, high-SNR, and low-SNR, towards robust arrhythmia detection.
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    Deep Multimodal Physiological Learning of Cerebral Vasoregulation Dynamics on Stroke Patients Towards Precision Brain Medicine
    (2024-08) Tipparti, Akanksha; Zhang, Qingxue; King, Brain; Yung-Ping Chien, Stanley
    Impaired cerebral vasoregulation is one of the most common post-ischemic stroke effects. Diagnosis and prevention of this condition is often invasive, costly and in-effective. This impairment restricts the cerebral blood vessels to properly regulate blood flow, which is very important for normal brain functioning. Developing accurate, non-invasive and efficient methods to detect this condition aids in better stroke diagnosis and prevention. The aim of this thesis is to develop deep learning techniques for the purpose of detection of cerebral vasoregulation impairments by analyzing physiological signals. This research employs various Deep learning techniques like Convolution Neural Networks (CNN), Mo bileNet, and Long-Short-Term Memory (LSTM) to determine variety of physiological signals from the PhysioNet database like Electrocardio-gram (ECG), Transcranial Doppler (TCD), Electromyogram (EMG), and Blood Pressure(BP) as stroke or non-stroke subjects. The effectiveness of these algorithms is demonstrated by a classification accuracy of 90% for the combination of ECG and EMG signals. Furthermore, this research explores the importance of analyzing dynamic physiologi cal activities in determining the impairment. The dynamic activities include Sit-stand, Sit-stand-balance, Head-up-tilt, and Walk dataset from the PhysioNet website. CNN and MobileNetV3 are employed in classification purposes of these signals, attempting to iden tify cerebral health. The accuracy of the model and robustness of these methods is greatly enhanced when multiple signals are integrated. Overall, this study highlights the potential of deep multimodal physiological learning in the development of precision brain medicine further enhancing stroke diagnosis. The results pave the way for the development of advanced diagnostic tools to determine cerebral health.
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    Electricity Demand Time Series Forecasting Based on Empirical Mode Decomposition and Long Short-Term Memory
    (Tech Science Press, 2021) Taheri, Saman; Talebjedi, Behnam; Laukkanen, Timo; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    Load forecasting is critical for a variety of applications in modern energy systems. Nonetheless, forecasting is a difficult task because electricity load profiles are tied with uncertain, non-linear, and non-stationary signals. To address these issues, long short-term memory (LSTM), a machine learning algorithm capable of learning temporal dependencies, has been extensively integrated into load forecasting in recent years. To further increase the effectiveness of using LSTM for demand forecasting, this paper proposes a hybrid prediction model that incorporates LSTM with empirical mode decomposition (EMD). EMD algorithm breaks down a load time-series data into several sub-series called intrinsic mode functions (IMFs). For each of the derived IMFs, a different LSTM model is trained. Finally, the outputs of all the individual LSTM learners are fed to a meta-learner to provide an aggregated output for the energy demand prediction. The suggested methodology is applied to the California ISO dataset to demonstrate its applicability. Additionally, we compare the output of the proposed algorithm to a single LSTM and two state-of-the-art data-driven models, specifically XGBoost, and logistic regression (LR). The proposed hybrid model outperforms single LSTM, LR, and XGBoost by, 35.19%, 54%, and 49.25% for short-term, and 36.3%, 34.04%, 32% for long-term prediction in mean absolute percentage error, respectively.
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    Modeling Spatiotemporal Pedestrian-Environment Interactions for Predicting Pedestrian Crossing Intention from the Ego-View
    (2021-08) Chen, Chen (Tina); Li, Lingxi; Tian, Renran; Lauren, Christopher; Ding, Zhengming
    For pedestrians and autonomous vehicles (AVs) to co-exist harmoniously and safely in the real-world, AVs will need to not only react to pedestrian actions, but also anticipate their intentions. In this thesis, we propose to use rich visual and pedestrian-environment interaction features to improve pedestrian crossing intention prediction from the ego-view.We do so by combining visual feature extraction, graph modeling of scene objects and their relationships, and feature encoding as comprehensive inputs for an LSTM encoder-decoder network. Pedestrians react and make decisions based on their surrounding environment, and the behaviors of other road users around them. The human-human social relationship has al-ready been explored for pedestrian trajectory prediction from the bird’s eye view in stationary cameras. However, context and pedestrian-environment relationships are often missing incurrent research into pedestrian trajectory, and intention prediction from the ego-view. To map the pedestrian’s relationship to its surrounding objects we use a star graph with the pedestrian in the center connected to all other road objects/agents in the scene. The pedestrian and road objects/agents are represented in the graph through visual features extracted using state of the art deep learning algorithms. We use graph convolutional networks, and graph autoencoders to encode the star graphs in a lower dimension. Using the graph en-codings, pedestrian bounding boxes, and human pose estimation, we propose a novel model that predicts pedestrian crossing intention using not only the pedestrian’s action behaviors(bounding box and pose estimation), but also their relationship to their environment. Through tuning hyperparameters, and experimenting with different graph convolutions for our graph autoencoder, we are able to improve on the state of the art results. Our context-driven method is able to outperform current state of the art results on benchmark datasetPedestrian Intention Estimation (PIE). The state of the art is able to predict pedestrian crossing intention with a balanced accuracy (to account for dataset imbalance) score of 0.61, while our best performing model has a balanced accuracy score of 0.79. Our model especially outperforms in no crossing intention scenarios with an F1 score of 0.56 compared to the state of the art’s score of 0.36. Additionally, we also experiment with training the state of the art model and our model to predict pedestrian crossing action, and intention jointly. While jointly predicting crossing action does not help improve crossing intention prediction, it is an important distinction to make between predicting crossing action versus intention.