Well-being and -ageing with chronical disease: the BV2 project

International audienceThe BV2 project aims to propose a monitoring system for wellbeing but also well-aging working on the prevention, detection and monitoring using a System of the Systems (SoS) approach. The project partner already uses the IoT technologies and the BV2 platform will combine the different developed systems. The main originality of the project consist s in the development of a virtual platform by combining the existing system

Bidirectional Recurrent Neural Network based Early Prediction of Cardiovascular Diseases using Electrocardiogram Signals for Type 2 Diabetic Patients

Introduction: The electrocardiogram (ECG) signal is important for early diagnosis of heart abnormalities. Type 2 diabetic individuals’ ECG signals provide pertinent data about their heart and are one of the most important diagnostic techniques used by doctors to identify Cardiovascular Disease (CVD). Bidirectional Recurrent Neural Network (RNN) classifies the features linked to normal and abnormal stage ECG signal. Aim: To analyse ECG signals of type 2 diabetic patients for early prediction of CVDs using feature extraction and bidirectional RNN based classification. Materials and Methods: This was a secondary data-based modelling study at Shri Ramasamy Memorial University Sikkim, India from December 2020 to January 2022. Different noises were removed by hybrid preprocessing filter made up of a Median and Savitzky-Golay filter. Undecimated Dual Tree Complex Wavelet Transform (UDTCWT) along with Detrended fluctuation (DA) analysis and Empirical Orthogonal Function (EOF) analysis were then used to extract features. These features were classified with Bidirectional RNN. Results: The proposed method was tested on the MIT-BIH, Physionet and DICARDIA databases, and the findings showed that it achieves an average accuracy of 97.6% when compared to the conventional techniques. Conclusion: The proposed method proves to be the most effective way for detecting anomalies in ECG signals in both the early and pathological stages. This method is also effective to diagnose the early intervention of cardiovascular symptoms

Comparative analysis of photoplethysmography signal quality from right and left index fingers

Photoplethysmography (PPG) has emerged as an increasingly attractive signal for non-invasive physiological measurements, owing to its simplicity, cost-effectiveness, and broad applicability spanning cardiovascular to respiratory systems. The burgeoning interest in PPG signal processing has facilitated its extensive incorporation in wearable devices, thus stimulating active research in this field. The present study undertakes a comprehensive evaluation to discern the optimal index finger (right or left) for PPG data acquisition and subsequent filtration, appraised through the lens of the signal-to-noise ratio (SNR) of the filtered signal. An analysis conducted on signals contaminated with white Gaussian noise unveiled that the Savitzky-Golay filter (a polynomial filter) with a window size of three outperformed other window lengths, rendering the highest SNR. Among the Infinite Impulse Response (IIR) filters compared; the Chebyshev I filter emerged as superior. Interestingly, the right index finger consistently demonstrated a higher mean SNR across filters: 0.49% for the Savitzky-Golay filters, 4.32% for the Butterworth (order 6), 7.71% for the Chebyshev I (order 10), and 4.02% for the Chebyshev II (order 4), relative to the left index finger for PPG signals perturbed by white Gaussian noise. These findings provide an insightful perspective for future research and development in wearable devices, suggesting potential superiority of the right index finger for PPG signal acquisition and filtration

Precision Medicine: Viable Pathways to Address Existing Research Gaps

Precision Medicine (PM) seeks to customize medical treatments for patients based on measurable and identifiable characteristics. Unlike personalized medicine, this effort is not intended to result in tailored care for each patient. Instead, this effort seeks to improve overall care within the medical domain by shifting the focus from one-size-fits-all care to optimized care for specified subgroups. In order for the benefits of PM to be expeditiously realized, the diverse skills sets of the scientific community must be brought to bear on the problem. This research effort explores the intersection of quality engineering (QE) and healthcare to outline how existing methodologies within the QE field could support existing PM research goals. Specifically this work examines how to determine the value of patient characteristics for use in disease prediction models with select machine learning algorithms, proposes a method to incorporate patient risk into treatment decisions through the development of performance functions, and investigates the potential impact of incorrect assumptions on estimation methods used in optimization models

Type 2 diabetes screening test by means of a pulse oximeter

In this paper, we propose a method for screening for the presence of type 2 diabetes by means of the signal obtained from a pulse oximeter. The screening system consists of two parts; the first analyses the signal obtained from the pulse oximeter, and the second consists of a machine-learning module. The system consists of a front end that extracts a set of features form the pulse oximeter signal. These features are based on physiological considerations. The set of features were the input of a machine-learning algorithm that determined the class of the input sample, i.e. whether the subject had diabetes or not. The machine-learning algorithms were random forests, gradient boosting, and linear discriminant analysis as benchmark. The system was tested on a database of 1, 157 subjects (two samples per subject) collected from five community health centres. The mean receiver operating characteristic (ROC) area found was 69.4% (median value 71.9% and range [75.4%-61.1%]), with a specificity=64% for a threshold that gave a sensitivity=65%. We present a screening method for detecting diabetes that has a performance comparable to the glycated haemoglobin (haemoglobin A1c HbA1c) test, does not require blood extraction, and yields results in less than five minutes.Peer Reviewe

Koneoppiminen päätöksenteon tukijana diabetes mellituksen hoidossa

Koneoppiminen on yksi tekoälyn osa-alue, jota voidaan hyödyntää laajasti terveydenhuollossa erilaisiin käyttötarkoituksiin. Diabetes hoidossa koneoppimisteknologioiden käyttöönotto voi merkitä huomattavaa laadullista parannusta ja kustannustehokasta hoitoa. Tutkimuksen tavoitteena on tuottaa käyttökelpoista ja ohjeellistavaa tietoa koneoppimisen soveltamismahdollisuuksista toimivan kliinisen ei-tietämyskantaisen päätöksenteon tukijärjestelmän suunnittelumallin luomiseksi terveydenhuolto-organisaatioihin, terveydenhuollon ammattihenkilökunnan kliinisen päätöksenteon edistämiseksi. Tutkimuksen teoreettisen viitekehyksen muodostaa koneoppiminen terveydenhoidossa ja kliininen päätöksenteko. Tutkimuksen osioita ovat koneoppimisen sovellettavuus diabeteshoitoon, koneoppimisen soveltaminen diabetes hoitotulosten ennustamiseen ja koneoppiminen diabeteksen diagnosointityökaluna. Tutkimusmenetelmä on kvalitatiivinen, integroiva kirjallisuuskatsaus. Aineisto kerättiin useasta eri tietokannasta, ja se muodostuu pääasiassa tieteellisistä katsaus-, tutkimus- ja konferenssiartikkeleista. Tutkimuksen aineisto analysoitiin ymmärtämään pyrkivällä laadullisella analyysilla. Tämä tehtiin induktiivisella lähestymistavalla aineistolähtöisenä sisällönanalyysina. Integroivan kirjallisuuskatsauksen synteesin pohjalta saatu tutkimustulos vastaa esitettyihin tutkimuskysymyksiin ja määrittelee toimivan ei-tietämyskantaisen kptj:n vaatimuksia järjestelmän varsinaista suunnittelua ja teknistä toteutusta varten. Tulokset osoittavat, että koneoppimistekniikoista syväoppiminen, ohjaamaton oppiminen, ohjattu oppiminen, yhteen liittynyt koneoppiminen ja äärimmäinen oppimiskone ovat niitä koneoppimisalgoritmeja, joita pitäisi integroida mukaan ei-tietoon-perustuvaan kliiniseen päätöksenteon tukijärjestelmään, varsinaisen kliinisen päätöksenteko prosessin tukemiseksi diabetes hoidossa. Tutkimuksen tuloksia on selostettu tarkemmin diskussio kappaleessa ja rajoitukset on myös pyritty tuomaan esille

Type 2 diabetes screening test by means of a pulse oximeter

In this paper, we propose a method for screening for the presence of type 2 diabetes by means of the signal obtained from a pulse oximeter. The screening system consists of two parts; the first analyses the signal obtained from the pulse oximeter, and the second consists of a machine-learning module. The system consists of a front end that extracts a set of features form the pulse oximeter signal. These features are based on physiological considerations. The set of features were the input of a machine-learning algorithm that determined the class of the input sample, i.e. whether the subject had diabetes or not. The machine-learning algorithms were random forests, gradient boosting, and linear discriminant analysis as benchmark. The system was tested on a database of 1, 157 subjects (two samples per subject) collected from five community health centres. The mean receiver operating characteristic (ROC) area found was 69.4% (median value 71.9% and range [75.4%-61.1%]), with a specificity=64% for a threshold that gave a sensitivity=65%. We present a screening method for detecting diabetes that has a performance comparable to the glycated haemoglobin (haemoglobin A1c HbA1c) test, does not require blood extraction, and yields results in less than five minutes.Peer Reviewe