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MeSH: určení tepové frekvence-přístrojové vybavení

5 záznamů v Medvik Filtry

Článek

Telemedicína v ambulantnej diabetologickej praxi
[Telemedicine in outpatient diabetes practice]

Forum Diabetologicum. 2024 ; 13 (1) : 6-15.

ISSN 1805–3807
Medvik
Zdroj

Diabetes mellitus (DM) je chronické, postupujúce, celoživotné ochorenie, spojené s pravidelnými vyšetreniami u lekára. Monitoring meraní patrí medzi kľúčové faktory v manažmente ochorenia a je aj ukazovateľom adherencie a kompliancie diabetika. Najvyššia ochota k spolupráci na liečbe zo strany pacienta je na začiatku ochorenia a v prvých niekoľkých mesiacoch, potom dlhodobo pozvoľne slabne. V štúdii sme v podmienkach bežnej medicínskej praxe diabetologickej ambulancie vyhodnocovali údaje u 211 novodiagnostikovaných diabetikov 2. typu po dobu 11 mesiacov. Okrem rutinnej praxe osobných návštev na ambulancii sme aktívne využili možnosti telemedicíny. Pacienti dostali pri prvej návšteve telemedicínsky kit, ktorý obsahoval glukomer, váhu, tlakomer, EKG, spirometer, teplomer a oxymeter. Úroveň metabolickej kompenzácie sme verifikovali vyhodnotením hladín glykovaného hemoglobínu. Zavedenie telemedicíny umožňuje sledovanie kompliancie a adherencie pri liečbe DM. Narastajúci počet diabetikov a frekvencia vyšetrení vyvoláva tlak na zdravotný systém. V manažmente pacienta s DM sú potrebné nástroje na objektivizáciu na trvalú motiváciu pacientov. Telemedicína prispieva k lepšej kompliancii, adherencii a metabolickej kompenzácii a znižuje potrebu frekvencie osobných návštev pacienta.

Diabetes mellitus (DM) is a chronic, progressive, lifelong disease associated with regular visits at doctor. Monitoring measurements is one of the key factors in disease management and it is also an indicator of diabetic adherence and compliance of the patient. Patient is mostly willing to cooperate in the treatment at the beginning of the disease and in the first few months, after that it gradually weakens over the long term. In the study, we evaluated the data of 211 newly diagnosed patients with type 2 diabetes for a period of 11 months. In addition to the routine practice of personal visits of the doctor, we actively used the possibilities of telemedicine. During the first visit, patients received a telemedicine kit that includes glucometer, scale, blood pressure monitor, ECG and oximeter. The metabolic compensation was verified by evaluating glycated hemoglobin. By establishing telemedicine it enabled monitoring of compliance and adherence in the treatment of DM. The growing number of diabetics and the frequency of examinations puts pressure on the health system. In the management of patients with DM, tools for objectification are needed to continuously motivate patients. Telemedicine contributes to better compliance, adherence, metabolic compensation and reduces the need for frequent personal patient visits.

Článek

Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device

Physiological measurement. 2018 ; 39 (9) : 094003. [pub] 20180913

Physiol Meas
ISSN 1361-6579
Medvik
Zdroj

OBJECTIVE: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal quality, small number of leads and short records, leading to incorrect recognition of pathological events. This paper introduces a novel approach to classification (normal/'N', atrial fibrillation/'A', other/'O', and noisy/'P') of short single-lead ECGs recorded by wearable devices. APPROACH: Various rhythm and morphology features are derived from the separate beats ('local' features) as well as the entire ECGs ('global' features) to represent short-term events and general trends respectively. Various types of atrial and ventricular activity, heart beats and, finally, ECG records are then recognised by a multi-level approach combining a support vector machine (SVM), decision tree and threshold-based rules. MAIN RESULTS: The proposed features are suitable for the recognition of 'A'. The method is robust due to the noise estimation involved. A combination of radial and linear SVMs ensures both high predictive performance and effective generalisation. Cost-sensitive learning, genetic algorithm feature selection and thresholding improve overall performance. The generalisation ability and reliability of this approach are high, as verified by cross-validation on a training set and by blind testing, with only a slight decrease of overall F1-measure, from 0.84 on training to 0.81 on the tested dataset. 'O' recognition seems to be the most difficult (test F1-measures: 0.90/'N', 0.81/'A' and 0.72/'O') due to high inter-patient variability and similarity with 'N'. SIGNIFICANCE: These study results contribute to multidisciplinary areas, focusing on creation of robust and reliable cardiac monitoring systems in order to improve diagnosis, reduce unnecessary time-consuming expert ECG scoring and, consequently, ensure timely and effective treatment.