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Neural Control of Respiration01:18

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Related Experiment Video

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Hyperparameter Optimization Method Based on Harmony Search Algorithm to Improve Performance of 1D CNN Human

Seong-Hoon Kim1, Zong Woo Geem2, Gi-Tae Han1

  • 1Department of Computer Engineering, Gachon University, Seongnam 13120, Korea.

Sensors (Basel, Switzerland)
|July 8, 2020
PubMed
Summary

This study introduces a novel harmony search algorithm to optimize hyperparameters for 1D convolutional neural networks (CNNs), significantly improving respiration pattern recognition accuracy and reducing computational iterations.

Keywords:
1D convolutional neural networkharmony search algorithmhyperparameter optimizationrespiration patternsultra-wideband radar

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Accurate respiration pattern recognition is crucial for diagnosing respiratory conditions.
  • Existing methods using 1D convolutional neural networks (CNNs) face challenges in hyperparameter optimization, impacting recognition accuracy and efficiency.
  • Optimizing hyperparameters like convolutional layer depth, kernel configuration, and dense layer neuron count is essential for enhancing CNN performance.

Purpose of the Study:

  • To develop and evaluate a novel method for optimizing hyperparameters of 1D CNNs for improved respiration pattern recognition.
  • To integrate the harmony search algorithm with 1D CNNs to efficiently find optimal hyperparameter combinations.
  • To enhance the accuracy and reduce the computational cost of respiration pattern recognition.

Main Methods:

  • The proposed method integrates the harmony search algorithm with a 1D convolutional neural network (CNN).
  • Hyperparameters optimized include convolutional layer depth, number and size of kernels, and number of neurons in the dense layer.
  • Experiments were conducted to evaluate the recognition rate of five distinct respiration patterns.

Main Results:

  • The proposed method achieved an average recognition rate of approximately 96.7% for five respiration patterns.
  • This represents an approximate 2.8% improvement in recognition accuracy compared to existing methods.
  • The number of iterations required for hyperparameter optimization was drastically reduced from 2,000,000 to 3,652.

Conclusions:

  • The harmony search algorithm effectively optimizes 1D CNN hyperparameters for superior respiration pattern recognition.
  • The proposed method offers a significant improvement in accuracy and computational efficiency over previous approaches.
  • This optimized approach holds promise for advancing the clinical application of AI in respiratory monitoring.