Application of the continuous-discrete extended Kalman filter for fault detection in continuous glucose monitors for type 1 diabetes

Mahmoudi, Zeinab; Boiroux, Dimitri; Hagdrup, Morten; Nørgaard, Kirsten; Poulsen, Niels Kjølstad; Madsen, Henrik; Jørgensen, John Bagterp

doi:10.1109/ecc.2016.7810373

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…Several factors are responsible for that kind of fault, such as the decay of sensor sensitivity, pressure-induced sensor attenuation (PISA) [16], and interruption in signal transmission, etc. Furthermore, bias and latency might be present in a CGM reading [33]. Another issue is that the reading of sensors from the same manufacturer might be different due to manufacturing variability.…”

Section: Introductionmentioning

confidence: 99%

Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction

Rabby¹,

Tu²,

Hossen³

et al. 2021

Preprint

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Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep learning techniques have been utilized for a more accurate BG level prediction system. However, continuous glucose monitoring (CGM) readings are susceptible to sensor errors. As a result, inaccurate CGM readings would affect BG prediction and make it unreliable, even if the most optimal machine learning model is used. In this work, we propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model considering sensor fault. We use the Kalman smoothing technique for the correction of the inaccurate CGM readings due to sensor error. For the OhioT1DM dataset, containing eight weeks' data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 minutes and 60 minutes of prediction horizon (PH), respectively. To the best of our knowledge, this is the leading average prediction accuracy for the ohioT1DM dataset. Different physiological information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus insulin, and cumulative step counts in a fixed time interval, are crafted to represent meaningful features used as input to the model. The goal of our approach is to lower the difference between the predicted CGM values and the fingerstick blood glucose readings -the ground truth. Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management.

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Section: Introductionmentioning

confidence: 99%

Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction

Rabby¹,

Tu²,

Hossen³

et al. 2021

Preprint

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“…Following works have focused on the fault-tolerant control in the diabetic system: Mahmoudi et al [19] used the Kalman Filter for the state estimation and FDI purpose. A Bayesian algorithm is utilized to detect faults in a blood glucose control system [20].…”

Section: Introductionmentioning

confidence: 99%

Using Sliding Mode Controller and Eligibility Traces for Controlling the Blood Glucose in Diabetic Patients at the Presence of Fault

Noori

Sadrnia

Sistani

2020

IJE

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Some people suffering from diabetes use insulin injection pumps to control the blood glucose level. Sometimes, the fault may occur in the sensor or actuator of these pumps. The main objective of this paper is controlling the blood glucose level at the desired level and fault-tolerant control of these injection pumps. To this end, the eligibility traces algorithm is combined with the sliding mode control. The eligibility traces algorithm is one of the newest solving methods of the Reinforcement Learning approach. The major disadvantage of the sliding mode control method is the chattering phenomenon. In this paper, the novel idea is the combination of these methods to remove the chattering phenomena in simulation results. To demonstrate the superiority of the proposed method, it is compared with another combinatory method that is the sliding mode control and Artificial Neural Networks. Simulation results reveal that the combination of the eligibility traces algorithm and the sliding mode control can control the blood glucose level and insulin with a higher speed and bring them to the desired level, even in the case the sensor and actuator faults are present in the system. When the proposed hybrid method is used, the injected dosage of the drug is lower, which will result in reduced side effects. Finally, the noise, as well as the uncertainty in system parameters and initial conditions are applied to the system to investigate the performance of the proposed controller, under the faulty condition.

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“…RTS first makes a forward pass through the data using the normal Kalman filter Eqs. (4)(5)(6), storing the sequences of a priori and a posteriori estimatesx k ,x k and state covariance matricesP k andP k . These are then used as input to a backward pass that computes the smoothed estimatesx s k andP s k as follows [15]:…”

Section: B Kalman Smoothingmentioning

confidence: 99%

“…For CGM systems, pressure induced sensor attenuation (PISA) errors are common [4], [5], usually resulting from the patient lying on the sensor. Fallouts, bias and latencies are other occurences in CGM data [6], and some or all of these errors also apply to FGM.…”

mentioning

confidence: 99%

Kalman Smoothing for Objective and Automatic Preprocessing of Glucose Data

Staal

Salid

Fougner

et al. 2019

IEEE J. Biomed. Health Inform.

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Abstract-A method for preprocessing a time series of glucose measurements based on Kalman smoothing is presented. Given a glucose data time series that may be irregularly sampled, the method outputs an interpolated time series of glucose estimates with mean and variance. The method can provide homogenization of glucose data collected from different devices by using separate measurement noise parameters for differing glucose measurement equipment. We establish a link between the ISO 15197 standard and the measurement noise variance used by the Kalman smoother for Self Monitoring of Blood Glucose (SMBG) measurements. The method provides phaseless smoothing, and it can automatically correct errors in the original datasets like small fallouts and erroneous readings when surrounding data allows. The estimated variance can be used for deciding at which times the data are trustworthy. The method can be used as a preprocessing step in many kinds of glucose data processing and analysis tasks,

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Application of the continuous-discrete extended Kalman filter for fault detection in continuous glucose monitors for type 1 diabetes

Cited by 9 publications

References 26 publications

Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction

Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction

Using Sliding Mode Controller and Eligibility Traces for Controlling the Blood Glucose in Diabetic Patients at the Presence of Fault

Kalman Smoothing for Objective and Automatic Preprocessing of Glucose Data

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