Markers of Myocardial Ischemia in Patients with Diabetes Mellitus and Severe Obstructive Sleep Apnea – Impact of Continuous Positive Airway Pressure Therapy

Valo, Misa; Moller, Albert; Teupe, Claudius

doi:10.4172/2155-6156.1000492

J Diabetes Metab

2015

DOI: 10.4172/2155-6156.1000492

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Markers of Myocardial Ischemia in Patients with Diabetes Mellitus and Severe Obstructive Sleep Apnea – Impact of Continuous Positive Airway Pressure Therapy

Misa Valo¹,

Albert Moller

Claudius Teupe³

Abstract: Introduction: Diabetes Mellitus (T2DM) and obstructive sleep apnea (OSA) are common disorders that often coexist. Both disorders are associated with an increased risk of cardiovascular complications. Repeated nocturnal hypoxia in diabetic patients suffering also from OSA may result in myocardial ischemia. We evaluated whether myocardial ischemia is detectable by ST-segment depression and elevation of high sensitive Troponin T (hs-cTnT) and N-terminal pro B-type natriuretic peptide (NT-proBNP). We also examined… Show more

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2019

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References 29 publications

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Noise-tolerant modular neural network system for classifying ECG signal

Ochoa¹,

Mena

Felix³

et al. 2019

IJCAI

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Millions of electrocardiograms (ECG) are interpreted every year, requiring specialized training for accurate interpretation. Because automated and accurate classification ECG signals will improve early diagnosis of heart condition, several neural network (NN) approaches have been proposed for classifying ECG signals. Current strategies for a critical step, the preprocessing for noise removal, are still unsatisfactory. We propose a modular NN approach based on artificial noise injection, to improve the generalization capability of the resulting model. The NN classifier initially performed a fairly accurate recognition of four types of cardiac anomalies in simulated ECG signals with minor, moderate, severe, and extreme noise, with an average accuracy of 99.2%, 95.1%, 91.4%, and 85.2% respectively. Ultimately we discriminated normal and abnormal heartbeat patterns for single lead of raw ECG signals, obtained 95.7% of overall accuracy and 99.5% of Precision. Therefore, the propose approach is a useful tool for the detection and diagnosis of cardiac abnormalities.Povzetek: V članku je opisana metoda modularnih nevronskim mrež za prepoznavanje šumnih ECG signalov. Materials and methodsThe NN classification comprises five stages: (i) simulation of ECG signal, (ii) extraction of features that indicate cardiac abnormalities, (iii) computer generation of normal and abnormal heartbeat patterns, (iv) artificial noise injection, and (v) cardiac rhythm classification on simulated and real ECG signals.

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Noise-tolerant modular neural network system for classifying ECG signal

Ochoa¹,

Mena

Felix³

et al. 2019

IJCAI

View full text Add to dashboard Cite

show abstract

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Markers of Myocardial Ischemia in Patients with Diabetes Mellitus and Severe Obstructive Sleep Apnea – Impact of Continuous Positive Airway Pressure Therapy

Cited by 1 publication

References 29 publications

Noise-tolerant modular neural network system for classifying ECG signal

Noise-tolerant modular neural network system for classifying ECG signal

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