A hybrid intelligent classifier to estimate obesity levels based on ERG signals

Yapici, İrem Şenyer; Erkaymaz, Okan; Arslan, Rukiye Uzun

doi:10.1016/j.physleta.2021.127281

Cited by 6 publications

(8 citation statements)

References 52 publications

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“…Particularly, our high-fat diet is more enriched in lipids than carbohydrates [50]. While the ERG B-wave is affected in patients with obesity [54,55], there is no previous report of the effect of obesity on OPs in humans. In patients with diabetes but no DR, the amplitude and implicit time of the OPs tend to decrease and increase, respectively, as previously reported [56].…”

Section: Strengths and Interpretation Of Spontaneous Erg Signals' Pre...mentioning

confidence: 94%

Preventable risk factors for type 2 diabetes can be detected using noninvasive spontaneous electroretinogram signals

Imm

Muñoz-Benitez

Medina

et al. 2022

Preprint

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Given the ever-increasing prevalence of type 2 diabetes and obesity, the pressure on global healthcare is expected to be colossal, especially in terms of blindness. Electroretinogram (ERG) has long been perceived as a first-use technique for diagnosing eye diseases, and some studies suggested its use for preventable risk factors of type 2 diabetes and thereby diabetic retinopathy (DR). Here, we show that in a non-evoked mode, ERG signals contain spontaneous oscillations that predict disease cases in rodent models of obesity and in people with overweight, obesity, and metabolic syndrome but not yet diabetes, using one single random forest-based model. Classification performance was both internally and externally validated, and correlation analysis showed that the spontaneous oscillations of the non-evoked ERG are altered before oscillatory potentials, which are the current gold-standard for early DR. Principal component and discriminant analysis suggested that the slow frequency (0.4–0.7 Hz) components are the main discriminators for our predictive model. In addition, we established that the optimal conditions to record these informative signals, are 5-minute duration recordings under daylight conditions, using any ERG sensors, including ones working with portative, non-mydriatic devices. Our study provides an early warning system with promising applications for prevention, monitoring and even the development of new therapies against type 2 diabetes.

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Section: Strengths and Interpretation Of Spontaneous Erg Signals' Pre...mentioning

confidence: 94%

Preventable risk factors for type 2 diabetes can be detected using noninvasive spontaneous electroretinogram signals

Imm

Muñoz-Benitez

Medina

et al. 2022

Preprint

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“…ML based algorithms have been applied to Electrocardiogram (ECG) signals in order to develop predictive models for diagnosing heart diseases (Li et al, 2014 ; Al'Aref et al, 2019 ). Recently machine learning-based Artificial Neural Networks (ANN) have been applied to ERG signals for obesity diagnosis (Yapici et al, 2021 ). However, to date, machine learning-based methods have not been applied systematically to analyze ERG signals for glaucoma detection.…”

Section: Methodsmentioning

confidence: 99%

“…A visualization of nine ERG signals resulting from two ERG components (OP and STR) and seven ERG test responses is provided in Figure 2 . The dynamics of ERG signals vary in people with various conditions and can therefore aid in differentiating individuals with glaucoma (Grillo et al, 2018 ), schizophrenia (Demmin et al, 2018 ), obesity (Yapici et al, 2021 ), and bipolar disorder (Hébert et al, 2020 ). ERG can also help in evaluating the effectiveness of new or existing drugs and therapy modalities (Lai et al, 2006 , 2009 ; Nebbioso et al, 2009 ; da Silva et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

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Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma

et al. 2022

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PurposeEarly-stage glaucoma diagnosis has been a challenging problem in ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not completely leverage the functional measures' such as electroretinogram's immense potential; instead, focus is on structural measures like optical coherence tomography. The current study aims to take a foundational step toward the development of a novel and reliable predictive framework for early detection of glaucoma using machine-learning-based algorithm capable of leveraging medically relevant information that ERG signals contain.MethodsERG signals from 60 eyes of DBA/2 mice were grouped for binary classification based on age. The signals were also grouped based on intraocular pressure (IOP) for multiclass classification. Statistical and wavelet-based features were engineered and extracted. Important predictors (ERG tests and features) were determined, and the performance of five machine learning-based methods were evaluated.ResultsRandom forest (bagged trees) ensemble classifier provided the best performance in both binary and multiclass classification of ERG signals. An accuracy of 91.7 and 80% was achieved for binary and multiclass classification, respectively, suggesting that machine-learning-based models can detect subtle changes in ERG signals if trained using advanced features such as those based on wavelet analyses.ConclusionsThe present study describes a novel, machine-learning-based method to analyze ERG signals providing additional information that may be used to detect early-stage glaucoma. Based on promising performance metrics obtained using the proposed machine-learning-based framework leveraging an established ERG data set, we conclude that the novel framework allows for detection of functional deficits of early/various stages of glaucoma in mice.

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“…Particularly, our high-fat diet is more enriched in lipids than carbohydrates 50 . While the ERG B-wave is affected in patients with obesity 54,55 , there is no previous report of the effect of obesity on OPs in humans. In our group of patients with diabetes but no DR, the amplitude and implicit time of the OPs tend to decrease and increase, respectively, as previously reported 56 .…”

Section: Strengths and Interpretation Of Spontaneous Erg Signals' Pre...mentioning

confidence: 98%

Spontaneous electroretinogram signals to screen people with early risk factors for diabetic retinopathy

Imm

Muñoz-Benitez

Medina

et al. 2022

Preprint

View full text Add to dashboard Cite

Given the ever-increasing prevalence of type 2 diabetes and obesity, the pressure on global healthcare is expected to be colossal, especially in terms of blindness. Electroretinogram (ERG) has long been perceived as a first-use technique for diagnosing eye diseases, but existing methods are insufficient to screen early risk factors of diabetic retinopathy (DR). Here, we introduce non-evoked ERG as a simple, fast modality to record spontaneous activity, from which we developed a single random forest-based model that predicts disease cases in rodent models of obesity and in people with overweight, obesity, and metabolic syndrome. Classification performance was validated using a dataset from an independent eye center. Our algorithm can be coupled with different ERG sensors, including ones working with portative, non-mydriatic devices. Principal component and discriminant analysis suggest slow spontaneous ERG frequencies as main discriminators for our predictive model. Our study will facilitate the implementation of interventions for the prevention of overweight and obesity by providing a robust, quantitative, and non-invasive identification and follow-up approach, which should ultimately reduce DR incidence.

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A hybrid intelligent classifier to estimate obesity levels based on ERG signals

Cited by 6 publications

References 52 publications

Preventable risk factors for type 2 diabetes can be detected using noninvasive spontaneous electroretinogram signals

Preventable risk factors for type 2 diabetes can be detected using noninvasive spontaneous electroretinogram signals

Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma

Spontaneous electroretinogram signals to screen people with early risk factors for diabetic retinopathy

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