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<p>The monitoring of blood glucose levels (BGL) in the human body is a successful step in the fight against diabetes. Continuous glucose monitoring (CGM) devices are designed to monitor BGL but are unable to detect either lower or higher insulin levels in the future. These situations resulted in the occurrence of hypoglycemia and hyperglycemia. Predicting BGL accurately is a challenging task because of the nature of its time series data. Adverse diabetes conditions can only be avoided if the BGL has been monitored correctly. The existing mathematical models have high inter-patient variability, which does not allow such methods to be applied in clinical settings. This has led to the search for an artificial intelligence-based solution. Machine learning-based methods suffer from significant feature extraction problems in time series data. The proposed novel method utilises a 1D convolution layer (Conv1D) to extract deep hierarchical features and a long short-term memory (LSTM) recurrent connection to capture crucial temporal information. The framework uses CGM data for a 60-minute prediction horizon (PH). The experiment results demonstrate that the proposed framework, which utilises a convolutional recurrent connection, achieved superior results compared to other recent state-of-the-art models.</p>

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Forecasting blood glucose level using convolutional recurrent connection

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Sunny Arora,
  • Shailender Kumar,
  • Pardeep Kumar

摘要

The monitoring of blood glucose levels (BGL) in the human body is a successful step in the fight against diabetes. Continuous glucose monitoring (CGM) devices are designed to monitor BGL but are unable to detect either lower or higher insulin levels in the future. These situations resulted in the occurrence of hypoglycemia and hyperglycemia. Predicting BGL accurately is a challenging task because of the nature of its time series data. Adverse diabetes conditions can only be avoided if the BGL has been monitored correctly. The existing mathematical models have high inter-patient variability, which does not allow such methods to be applied in clinical settings. This has led to the search for an artificial intelligence-based solution. Machine learning-based methods suffer from significant feature extraction problems in time series data. The proposed novel method utilises a 1D convolution layer (Conv1D) to extract deep hierarchical features and a long short-term memory (LSTM) recurrent connection to capture crucial temporal information. The framework uses CGM data for a 60-minute prediction horizon (PH). The experiment results demonstrate that the proposed framework, which utilises a convolutional recurrent connection, achieved superior results compared to other recent state-of-the-art models.