Evaluating Deep Learning Algorithms for Real-Time Arrhythmia Detection

Petty, Tyler; Vu, Thong; Zhao, Xinghui; Hirsh, Robert A.; Murray, Greggory; Haas, Francis M.; Xue, Wei

doi:10.1109/bdcat50828.2020.00022

Cited by 6 publications

(3 citation statements)

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“…However, the computational complexity of the DTW algorithm is relatively high, especially when processing long time series, it will consume more computational resources and time. This may limit the practical feasibility of DTW-CNN models in large-scale datasets or real-time applications (Petty et al, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

Multimodal audio-visual robot fusing 3D CNN and CRNN for player behavior recognition and prediction in basketball matches

Wang

2024

Front. Neurorobot.

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IntroductionIntelligent robots play a crucial role in enhancing efficiency, reducing costs, and improving safety in the logistics industry. However, traditional path planning methods often struggle to adapt to dynamic environments, leading to issues such as collisions and conflicts. This study aims to address the challenges of path planning and control for logistics robots in complex environments.MethodsThe proposed method integrates information from different perception modalities to achieve more accurate path planning and obstacle avoidance control, thereby enhancing the autonomy and reliability of logistics robots. Firstly, a 3D convolutional neural network (CNN) is employed to learn the feature representation of objects in the environment for object recognition. Next, long short-term memory (LSTM) is used to model spatio-temporal features and predict the behavior and trajectory of dynamic obstacles. This enables the robot to accurately predict the future position of obstacles in complex environments, reducing collision risks. Finally, the Dijkstra algorithm is applied for path planning and control decisions to ensure the robot selects the optimal path in various scenarios.ResultsExperimental results demonstrate the effectiveness of the proposed method in terms of path planning accuracy and obstacle avoidance performance. The method outperforms traditional approaches, showing significant improvements in both aspects.DiscussionThe intelligent path planning and control scheme presented in this paper enhances the practicality of logistics robots in complex environments, thereby promoting efficiency and safety in the logistics industry.

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Section: Related Workmentioning

confidence: 99%

Multimodal audio-visual robot fusing 3D CNN and CRNN for player behavior recognition and prediction in basketball matches

Wang

2024

Front. Neurorobot.

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“…In the area of cardiovascular medicine, initial efforts have been made toward real-time processing of ECG signals to diagnose relevant diseases (Jin et al, 2009 ; Oresko et al, 2010 ). To this end, processing and analyzing ECG data as time series attracts increasing attention, and long short-term memory networks (Petty et al, 2020 ) have been used to achieve higher performance.…”

Section: Related Workmentioning

confidence: 99%

“…Existing studies on applying big data and machine learning technologies on ECG data for arrhythmic detection often focus on the learning performance, such as accuracy, precision, etc., instead of the potentials of supporting real-time processing. Some early work on real-time analysis of ECG data uses 1-D representation coupled with time-series based processing to achieve higher computational performance (Petty et al, 2020 ; Zhou et al, 2020 ; Bertsimas et al, 2021 ). However, representing ECG data in 1D loses the rich 2D features, and may hinder the potential of integrating data analytics technologies with traditional diagnostic approaches.…”

Section: Introductionmentioning

confidence: 99%

Real-time arrhythmia detection using convolutional neural networks

Vu,

Petty,

Yakut

et al. 2023

Front. Big Data

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Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow. In this paper, we present our work on supporting real-time arrhythmic detection using convolutional neural networks, which take images of electrocardiogram (ECG) segments as input, and classify the arrhythmia conditions. To support real-time processing, we have carried out extensive experiments and evaluated the computational cost of each step of the classification workflow. Our results show that it is feasible to achieve real-time arrhythmic detection using convolutional neural networks. To further demonstrate the generalizability of this approach, we used the trained model with processed data collected by a customized wearable sensor from a lab setting, and the results shown that our approach is highly accurate and efficient. This research provides the potentials to enable in-home real-time heart monitoring based on 2D image data, which opens up opportunities for integrating both machine learning and traditional diagnostic approaches.

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