One-Minute Finger Pulsation Measurement for Diabetes Rapid Screening with 1.3% to 13% False-Negative Prediction Rate

Chu, Justin; Yang, Wen-Tse; Hsieh, Tung‐Han; Yang, Fu-Liang

doi:10.11648/j.bsi.20210601.12

Cited by 5 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors stated that those results are comparable with the hemoglobin A1c HbA1c test. Another HRV based analysis was proposed by Chu et al [74]. In this case, a classical frequency HRV analysis was computed from 1228 diabetic and 1310 healthy subjects.…”

Section: Diabetes Detection With Machine Learningmentioning

confidence: 99%

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

Zanelli

Ammi

Hallab³

et al. 2022

Sensors

View full text Add to dashboard Cite

(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects. Thanks to their cost effectiveness and their ease of use, electrocardiographic (ECG) and photoplethysmographic (PPG) signals have recently been used in diabetes detection, blood glucose estimation and diabetes-related complication detection. This review’s aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (2) Method: We performed a systematic review based on articles that focus on the use of ECG and PPG signals in diabetes care. The search was focused on keywords related to the topic, such as “Diabetes”, “ECG”, “PPG”, “Machine Learning”, etc. This was performed using databases, such as PubMed, Google Scholar, Semantic Scholar and IEEE Xplore. This review’s aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (3) Results: A total of 78 studies were included. The majority of the selected studies focused on blood glucose estimation (41) and diabetes detection (31). Only 7 studies focused on diabetes complications detection. We present these studies by approach: traditional, machine learning and deep learning approaches. (4) Conclusions: ECG and PPG analysis in diabetes care showed to be very promising. Clinical validation and data processing standardization need to be improved in order to employ these techniques in a clinical environment.

show abstract

Section: Diabetes Detection With Machine Learningmentioning

confidence: 99%

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

Zanelli

Ammi

Hallab³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…[2017] Chu et al [2021]. However, this feature may not be calculated with confidence in short segments of data such as the available in our dataset.…”

Section: Discussionmentioning

confidence: 99%

Mortality predictors in a cohort of patients with COVID-19 admitted to a large tertiary hospital in the city of São Paulo, Brazil: a retrospective study

Oliveira¹,

Gorzoni

Rosa

2023

Sao Paulo Med. J.

View full text Add to dashboard Cite

INTRODUCTIONCoronavirus disease (COVID-19) is a potentially fatal infection caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that was first reported in the city of Wuhan, China, in December 2019. Since then, the disease has achieved a pandemic status, assuming dramatic proportions, thus leading to a global public health emergency. [1][2][3] In Brazil, the first case was officially confirmed on February 26, 2020, in the city of São Paulo, which has remained the epicenter of the country's pandemic with the third highest total number of confirmed cases. 4,5 Reports issued by the World Health Organization (WHO) on July 31, 2020, show that there is a significant difference in prognoses across countries, with a case fatality rate of the disease ranging between 3.53% (90,134/2,552,265 cases) in Brazil, 3.42% (150,054/4,388,566 cases) in the United States of America, and 14.21% (35,132/247,158 cases) in Italy. The reasons for such discrepancies remain to be fully elucidated, with population and genetic aspects, epidemiological control, population testing, and local management of the pandemic seeming to contribute to this situation. 6 Moreover, in Brazil, approximately 70% of the population relied solely on the services of its National Healthcare System (known as the "Unified Health System, " or by its acronym in Portuguese: SUS, Sistema Único de Saúde), 7 which rapidly led to hospital overcapacity and, therefore, possible preventable fatalities. In addition, hospitalization is known to have an array of negative impacts on the overall health of patients, such as secondary infections, needless procedures, psychological distress, and economic fallout of the health system that covers most of the nation's population.

show abstract

“…Medical treatments were classified into 4 categories, namely hyperglycemia, hypertension, hyperlipidemia, and other medical-doctor-prescribed medications (for example, cancer, sleeping pills, antidepressants, etc.). The detailed experimental setup and procedures can be found in [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c

Chu

Yang

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements have not yet been validated over 500 subjects. As illustrated in this work, we increased the number subjects recruited to 2538 and found that the prediction accuracy (the ratio in zone A of Clarke’s error grid) reduced to undesirable 60.6%. We suspect the low prediction accuracy induced by larger sample size might arise from the physiological diversity of subjects, and one possibility is that the diversity might originate from medication. Therefore, we split the subjects into two cohorts for deep learning: with and without medication (1682 and 856 recruited subjects, respectively). In comparison, the cohort training for subjects without any medication had approximately 30% higher prediction accuracy over the cohort training for those with medication. Furthermore, by adding quarterly (every 3 months) measured glycohemoglobin (HbA1c), we were able to significantly boost the prediction accuracy by approximately 10%. For subjects without medication, the best performing model with quarterly measured HbA1c achieved 94.3% prediction accuracy, RMSE of 12.4 mg/dL, MAE of 8.9 mg/dL, and MAPE of 0.08, which demonstrates a very promising solution for NIBG prediction via deep learning. Regarding subjects with medication, a personalized model could be a viable means of further investigation.

show abstract

One-Minute Finger Pulsation Measurement for Diabetes Rapid Screening with 1.3% to 13% False-Negative Prediction Rate

Cited by 5 publications

References 19 publications

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

Mortality predictors in a cohort of patients with COVID-19 admitted to a large tertiary hospital in the city of São Paulo, Brazil: a retrospective study

90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c

Contact Info

Product

Resources

About