Type-2 diabetes identification from toe-photoplethysmography using Fourier decomposition method

Mishra, Bhanupriya; Nirala, Neelamshobha; Singh, Bikesh Kumar

doi:10.1007/s00521-023-09208-2

Cited by 2 publications

(1 citation statement)

References 50 publications

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“…A modulated KP scan line signal can be obtained by superimposing different delay functions on the pixel points at each aperture position. When the 0-angle plane wave is emitted, this article does not focus on the image object, resulting in image contrast and signal-to-noise ratio [ 25 , 26 ]. To improve image quality, a synthetic imaging method is proposed, as shown in Fig.…”

Section: High Frame Rate Imaging Algorithmmentioning

confidence: 99%

Predictive value of Cmmi-MHR combined with thromboelastography parameters in acute cerebral infarction

Rao,

Tan,

Wang

et al. 2024

BMC Med Imaging

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Cerebral infarction is a common neurological disease with high rates of morbidity, mortality, and recurrence, posing a great threat to human life and health. Cerebral infarction is the second leading cause of death in the world and the leading cause of long-term disability in humans. The results of the third national retrospective sampling survey on causes of death in 2008 showed that cerebral infarction has become the leading cause of death in China and its mortality rate is 4–5 times that of European and American countries. Therefore, this article proposed a study on the predictive value of Cmmi-MHR combined with thromboelastography parameters that was performed for acute cerebral infarction. This paper mainly proposed a high frame rate imaging technology and analyzed its algorithm. In this article, in the experimental part, an in-depth analysis of the predictive value of the Monocyte-to-high-density lipoprotein cholesterol ratio (MHR) combined with thromboelastography parameters was performed for acute cerebral infarction. The final experimental results showed that HDL (OR = 1.695%, P-trend = 0.049) had a probability of death within 90 days of hospitalization (OR = 0.81, 95% CI = 1.06–3.11, P-trend = 0.523). There were no significant differences in mortality rate after 90 days. Regardless of adjusting for confounders such as age, gender, and NIHSS score, there was no significant difference in the risk of MHR or monocyte count within 90 days of hospitalization. The conclusion indicates that the combination of Cmmi-MHR and thromboelastography parameters provides a new perspective and method for the diagnosis and treatment of cerebral infarction, and provides important support for personalized treatment and management of cerebral infarction.

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Section: High Frame Rate Imaging Algorithmmentioning

confidence: 99%

Predictive value of Cmmi-MHR combined with thromboelastography parameters in acute cerebral infarction

Rao,

Tan,

Wang

et al. 2024

BMC Med Imaging

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Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

Kumar,

Jeya Prabha

et al. 2024

Biomed. Phys. Eng. Express

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The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement of non-invasive alternatives for initial disease detection. Machine learning in integration with the optical sensing technology can effectively analyze the signal patterns associated with diabetes. The objective of this research is to develop and evaluate a non-invasive optical-based method combined with machine learning algorithms for the classification of individuals into normal, prediabetic, and diabetic categories. A novel device was engineered to capture real-time optical vascular signals from participants representing the three glycemic states. The signals were then subjected to quality assessment and preprocessing to ensure data reliability. Subsequently, feature extraction was performed using time-domain analysis and wavelet scattering techniques to derive meaningful characteristics from the optical signals. The extracted features were subsequently employed to train and validate a suite of machine learning algorithms. An ensemble bagged trees classifier with wavelet scattering features and random forest classifier with time-domain features demonstrated superior performance, achieving an overall accuracy of 86.6% and 80.0% in differentiating between normal, prediabetic, and diabetic individuals based on the optical vascular signals. The proposed non-invasive optical-based approach, coupled with advanced machine learning techniques, holds promise as a potential screening tool for diabetes mellitus. The classification accuracy achieved in this study warrants further investigation and validation in larger and more diverse populations.

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Type-2 diabetes identification from toe-photoplethysmography using Fourier decomposition method

Cited by 2 publications

References 50 publications

Predictive value of Cmmi-MHR combined with thromboelastography parameters in acute cerebral infarction

Predictive value of Cmmi-MHR combined with thromboelastography parameters in acute cerebral infarction

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

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