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Deep learning framework for cardiorespiratory disease detection using smartphone IMU sensors.

Lorenzo Simone1, Luca Miglior1, Vincenzo Gervasi1

  • 1Department of Computer Science, University of Pisa, Pisa, Italy.

Computers in Biology and Medicine
|July 4, 2025
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Smartphone sensors can detect cardiorespiratory conditions early. This non-invasive method uses breathing patterns for accessible remote health monitoring, aiding early diagnosis in diverse settings.

Keywords:
Cardiorespiratory diseasesDeep learningIMU sensorsLarge-scale population screeningSmartphone

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Area of Science:

  • Biomedical Engineering
  • Digital Health
  • Cardiorespiratory Medicine

Background:

  • Cardiovascular and respiratory diseases pose a significant global health challenge.
  • There is a critical need for accessible, cost-effective screening tools for early detection.
  • Current remote monitoring solutions often require specialized equipment or are not widely accessible.

Purpose of the Study:

  • To develop and validate a smartphone-based framework for early detection of cardiorespiratory conditions.
  • To leverage inertial measurement unit (IMU) sensors for non-invasive respiratory kinematics acquisition.
  • To establish a cost-effective and accessible solution for remote health monitoring.

Main Methods:

  • Utilized commodity smartphones with IMU sensors (accelerometer, gyroscope) for data collection.
  • Implemented a standardized protocol involving data acquisition from five thoracoabdominal regions.
  • Employed a bidirectional recurrent neural network (BRNN) for binary classification of healthy individuals versus patients with cardiovascular disease after signal preprocessing and breathing cycle segmentation.

Main Results:

  • The BRNN model achieved robust classification performance: average sensitivity of 0.81±0.02, specificity of 0.82±0.05, F1 score of 0.81±0.02, and accuracy of 80.2%±3.9.
  • The model demonstrated generalization capability on an independent dataset, achieving a true negative rate of 74.8%±4.5.
  • The framework successfully differentiated between healthy individuals and preoperative patients with conditions like valvular insufficiency, coronary artery disease, and aortic aneurysm.

Conclusions:

  • The proposed smartphone-based framework offers a promising, non-invasive, and low-cost approach for early cardiorespiratory condition detection.
  • This technology is suitable for remote health monitoring, particularly in resource-limited settings and during public health crises.
  • The study supports the potential for improving public health outcomes through enhanced early diagnosis and remote patient management.