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A portable household detection system based on the combination of bidirectional LSTM and residual block for automatical arrhythmia detection

  • Zeqiong Huang , Shaohua Yang , Qinhong Zou , Xuliang Gao and Bin Chen EMAIL logo
Published/Copyright: September 29, 2023
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Biomedical Engineering / Biomedizinische Technik
From the journal Biomedical Engineering / Biomedizinische Technik Volume 69 Issue 2

Abstract

Objectives

Arrhythmia is an important component of cardiovascular disease, and electrocardiogram (ECG) is a method to detect arrhythmia. Arrhythmia detection is often paroxysmal, and ECG signal analysis is time-consuming and expensive. We propose a model and device for convenient monitoring of arrhythmia at any time.

Methods

This work proposes a model combining residual block and bidirectional long-term short-term memory network (BiLSTM) to detect and classify ECG signals. Residual blocks can extract deep features and avoid performance degradation caused by convolutional networks. Combined with the feature of BiLSTM to strengthen the connection relationship of the local window, it can achieve a better classification and prediction effect.

Results

Model optimization experiments were performed on the MIT-BIH Atrial Fibrillation Database (AFDB) and MIT-BIH Arrhythmia Database (MITDB). The accuracy simulation results on both long and short signal was higher than 99 %. To further demonstrate the applicability of the model, validation experiments were conducted on MIT-BIH Normal Sinus Rhythm Database (NSRDB) and the Long-Term AF Database (LTAFDB) datasets, and the related recognition accuracy were 99.830 and 91.252 %, respectively. Additionally, we proposed a portable household detection system including an ECG and a blood pressure detection module. The detection accuracy was higher than 98 % using the collected data as testing set.

Conclusions

Hence, we thought our system can be used for practical application.

Keywords: ECG detection; residual block; BiLSTM; portable household detection system
Corresponding author: Bin Chen, Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China, E-mail:

Funding source: National Nature Science Foundation of China

Award Identifier / Grant number: 61801400

Funding source: JSPS KAKENHI

Award Identifier / Grant number: JP18F18392

Acknowledgments

We thank the PhysioNet repository and the donators for sharing the datasets.

  1. Research ethics: Not applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Zeqiong Huang: methodology, software, validation, writing original draft. Shaohua Yang: software. Qinhong Zou: investigation. Xuliang Gao: validation. Bin Chen: conceptualization, supervision.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: This study was supported by the National Nature Science Foundation of China [No. 61801400] and JSPS KAKENHI [No. JP18F18392].

  6. Data availability: The raw data can be obtained on request from the corresponding author.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/bmt-2021-0146).

Received: 2021-05-12
Accepted: 2023-09-11
Published Online: 2023-09-29
Published in Print: 2024-04-25

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/bmt-2021-0146
Keywords for this article
ECG detection; residual block; BiLSTM; portable household detection system

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Epileptic EEG patterns recognition through machine learning techniques and relevant time–frequency features
  4. Research Articles
  5. Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task
  6. Development of audio-guided deep breathing and auditory Go/No-Go task on evaluating its impact on the wellness of young adults: a pilot study
  7. Detection of driver drowsiness level using a hybrid learning model based on ECG signals
  8. A portable household detection system based on the combination of bidirectional LSTM and residual block for automatical arrhythmia detection
  9. Probabilistic hierarchical clustering based identification and segmentation of brain tumors in magnetic resonance imaging
  10. Biomechanical comparison of three fixation strategies for radial head fractures: a biomechanical study
  11. Computer-based analysis of the taper connection strength of different revision head and adapter sleeve designs
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