Anomaly Detection from BCG Signal With Artificial Intelligence

• Typ der Arbeit: Forschungsprojekt
• Status der Arbeit: abgeschlossen
• Projekte: PotatoNet
• Betreuer: Ulf Kulau
• Ende der Arbeit: 27. Jan 2023

From recent work it can be summarized that deep learning has been performing reliably and accurately in detecting anomalies from ECG data. According to the current state of the art, automatic anomaly detection from BCG data is still lacking. Thus, in this work, we will use deep learning to detect anomalies from BCG data.

Research will be done more specifically on the BCG data and anomaly detection with deep learning. The workflow of the implementation will be finalized and required tools will be determined.

BCG data will be collected and Exploratory Data Analysis will be performed. The collected data will be processed and used as training data. As we do not have BCG data from heart disease patients, we will generate test data by separating a part of the training data and injecting erroneous data into it.

As deep learning model development is an iterative process combined with develop- ment, training, testing, and evaluation, we will spend most of the time in this work package. We will continuously improve the model by tuning model architecture, layers, and parameters and determine the best-performed model.

Tasks

• Data analysis and pre-processing: Analysis of the dataset. Find out the model’s relevant feature. Generate training and test dataset
• Model development: We will develop deep learning-based autoencoder models. We will iteratively train the model with our dataset for different layers, activation functions, and parameters.
• Evaluation of the model: Evaluate the models with different performance parameters and determine the best-performed model.

Further Reading

• Ballistocardiography and seismocardiography: A review of recent advances

  Omer T Inan, Pierre-Francois Migeotte, Kwang-Suk Park, Mozziyar Etemadi, Kouhyar Tavakolian, Ramon Casanella, John Zanetti, Jens Tank, Irina Funtova, G Kim Prisk, othersIEEE journal of biomedical and health informatics19.4IEEE2014.
  [BibTex]

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  BibTex Entry

  @article{inan2014ballistocardiography,
  author = {Inan, Omer T and Migeotte, Pierre-Francois and Park, Kwang-Suk and Etemadi, Mozziyar and Tavakolian, Kouhyar and Casanella, Ramon and Zanetti, John and Tank, Jens and Funtova, Irina and Prisk, G Kim and others},
  journaltitle = {IEEE journal of biomedical and health informatics},
  number = {4},
  pages = {1414–1427},
  publisher = {IEEE},
  title = {Ballistocardiography and seismocardiography: A review of recent advances},
  volume = {19},
  year = {2014},
  }

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• Estimation of central aortic forces in the ballistocardiogram under rest and exercise conditions

  Richard M Wiard, Hyun Jin Kim, C Alberto Figueroa, Gregory TA Kovacs, Charles A Taylor, Laurent Giovangrandi2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society2009.
  [BibTex]

  🗙

  BibTex Entry

  @inproceedings{wiard2009estimation,
  author = {Wiard, Richard M and Kim, Hyun Jin and Figueroa, C Alberto and Kovacs, Gregory TA and Taylor, Charles A and Giovangrandi, Laurent},
  booktitle = {2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
  organization = {IEEE},
  pages = {2831–2834},
  title = {Estimation of central aortic forces in the ballistocardiogram under rest and exercise conditions},
  year = {2009},
  }

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• Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks

  Muhammad Zubair, Changwoo YoonSensors22.11Multidisciplinary Digital Publishing Institute2022.
  [BibTex]

  🗙

  BibTex Entry

  @article{zubair2022cost,
  author = {Zubair, Muhammad and Yoon, Changwoo},
  journaltitle = {Sensors},
  number = {11},
  pages = {4075},
  publisher = {Multidisciplinary Digital Publishing Institute},
  title = {Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks},
  volume = {22},
  year = {2022},
  }

  Close