Fault Detection in Artificial Pancreas: A Model-Free approach

Meneghetti, Lorenzo; Terzi, Matteo; Susto, Gian Antonio; Favero, Simone Del; Cobelli, Claudio

doi:10.1109/cdc.2018.8619048

“…The UVa/Padova simulator allows the incorporation of different meal scenarios for the virtual patient (VP) population, allowing researchers to analyze the effectiveness of a control algorithm [16][17][18][19][20][21][22], validate optimization and adaptation strategies for insulin delivery [23][24][25][26], develop disturbance detection algorithms for meals [27][28][29] and exercise [30], develop methods for mitigating the risks of hypoglycemia [31,32], and integrate machine learning algorithms into conventional diabetes therapy and bolus calculator for the treatment of T1D patients [33][34][35]. In the literature, the meal scenarios used for testing BG regulation effectiveness are based on typical values considering three meals per day [36][37][38][39][40][41][42][43][44][45][46][47]. However, in real life, the amount of carbohydrate intake and number of meals per day may vary patient to patient.…”

Section: Introductionmentioning

confidence: 99%

Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts

Ahmad

¹

,

Ramkissoon

²

,

Beneyto

³

et al. 2021

View full text Add to dashboard Cite

Preclinical testing and validation of therapeutic strategies developed for patients with type 1 diabetes (T1D) require a cohort of virtual patients (VPs). However, current simulators provide a limited number of VPs, lack real-life scenarios, and inadequately represent intra- and inter-day variability in insulin sensitivity and blood glucose (BG) profile. The generation of a realistic scenario was achieved by using the meal patterns, insulin profiles (basal and bolus), and exercise sessions estimated as disturbances using clinical data from a cohort of 14 T1D patients using the Medtronic 640G insulin pump provided by the Hospital Clínic de Barcelona. The UVa/Padova’s cohort of adult patients was used for the generation of a new cohort of VPs. Insulin model parameters were optimized and adjusted in a day-by-day fashion to replicate the clinical data to create a cohort of 75 VPs. All primary and secondary outcomes reflecting the BG profile of a T1D patient were analyzed and compared to the clinical data. The mean BG 166.3 versus 162.2 mg/dL ( = 0.19), coefficient of variation 32% versus 33% ( = 0.54), and percent of time in range (70 to 180 mg/dL) 59.6% versus 66.8% ( = 0.35) were achieved. The proposed methodology for generating a cohort of VPs is capable of mimicking the BG metrics of a real cohort of T1D patients from the Hospital Clínic de Barcelona. It can adopt the inter-day variations in the BG profile, similar to the observed clinical data, and thus provide a benchmark for preclinical testing of control techniques and therapy strategies for T1D patients.

show abstract

“…physical knowledge. These are called data-driven methods [8], [6], [26] which are principally rely on the information gathered by data acquisition system.…”

Section: Introductionmentioning

confidence: 99%

“…Data-driven methods can be categorized into three main categories: 1) supervised [14], [1], [8], [27], 2) semi-supervised [28], and 3) unsupervised [6], [26], [3], [23] anomaly detection. Since the labelled data, which is required for supervised anomaly detection, is often not available or in other words, collecting sufficient anomolous samples is infeasible in most of the cases, many researchers have tried to detect anomalies without labelled data.…”

Section: Introductionmentioning

confidence: 99%

Developing a Data-Driven Unsupervised Pattern Recognition Approach for Sensor Signal Anomaly Detection

Iranmehr¹,

Ferreira²,

Böhnert³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

Coming up with a system for early detection of machine damages and failures is one of the important challenges in the industrial maintenance procedure to avoid additional costs and downtimes. To approach this goal, this paper uses the signal gathered by a sensing system which employed a spintropic sensor to measure the magnetic field around the machine which somehow shows the machine's behaviour. Using this signal and focusing on analysing and processing the signal, this paper develops a data-driven method to recognize signal patterns and subsequently detects anomalies. A challenging task that we succeeded to overcome in this paper is recognizing relevant signal patterns without having any prior knowledge. An algorithm designed for this task is therefore completely unsupervised which makes it consistent and suitable to apply it for the signals gathered for other types of machines. Using both frequency and time domain information, the proposed algorithm, which utilizes signal processing and machine learning techniques, is able to efficiently identify relevant signal patterns. Clustering results on the real data gathered by the aforementioned sensor have shown the high accuracy of 99.38% in recognizing patterns. Furthermore, an anomaly score measure is used and according to its distribution, anomalies are detected appropriately. <br>

show abstract

“…Data-driven methods can be categorized into three main categories: 1) supervised [14], [1], [8], [27], 2) semi-supervised [28], and 3) unsupervised [6], [26], [3], [23] anomaly detection. Since the labelled data, which is required for supervised anomaly detection, is often not available or in other words, collecting sufficient anomolous samples is infeasible in most of the cases, many researchers have tried to detect anomalies without labelled data.…”

Section: Introductionmentioning

confidence: 99%

Developing a Data-Driven Unsupervised Pattern Recognition Approach for Sensor Signal Anomaly Detection

Iranmehr¹,

Ferreira²,

Böhnert³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

Coming up with a system for early detection of machine damages and failures is one of the important challenges in the industrial maintenance procedure to avoid additional costs and downtimes. To approach this goal, this paper uses the signal gathered by a sensing system which employed a spintropic sensor to measure the magnetic field around the machine which somehow shows the machine's behaviour. Using this signal and focusing on analysing and processing the signal, this paper develops a data-driven method to recognize signal patterns and subsequently detects anomalies. A challenging task that we succeeded to overcome in this paper is recognizing relevant signal patterns without having any prior knowledge. An algorithm designed for this task is therefore completely unsupervised which makes it consistent and suitable to apply it for the signals gathered for other types of machines. Using both frequency and time domain information, the proposed algorithm, which utilizes signal processing and machine learning techniques, is able to efficiently identify relevant signal patterns. Clustering results on the real data gathered by the aforementioned sensor have shown the high accuracy of 99.38% in recognizing patterns. Furthermore, an anomaly score measure is used and according to its distribution, anomalies are detected appropriately. <br>

show abstract

Fault Detection in Artificial Pancreas: A Model-Free approach

Cited by 6 publications

References 23 publications

Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts

Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts

Developing a Data-Driven Unsupervised Pattern Recognition Approach for Sensor Signal Anomaly Detection

Developing a Data-Driven Unsupervised Pattern Recognition Approach for Sensor Signal Anomaly Detection

Contact Info

Product

Resources

About