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1.
Res. Biomed. Eng. (Online) ; 34(2): 127-137, Apr.-June 2018. tab, graf
Article in English | LILACS | ID: biblio-956297

ABSTRACT

Abstract Introduction Impairment of sleep quality directly increases the risk of heart attack, obesity, and stroke, among other conditions, which makes polysomnography (PSG) an important public health tool. However, the inherent problems with PSG render the correct diagnosis of sleep diseases a difficult task. As a novel alternative to the class II PSG system, this work presents a distributed system composed of three modules, which together are capable of the simultaneous monitoring of environmental variables and patient signals. This system could reduce the distress of a PSG exam in certain cases, dismiss the need for an overnight sleep in a healthcare/sleep centre, and facilitate self-setup and internet-based diagnosis. Methods Hardware and software capable of synchronously monitoring, processing and logging into a µSD card environmental parameters (temperature, humidity, visible light intensity and audible noise level) and directly measured patient signals (electrocardiogram, electrooculogram, and body and limb posture) were designed and implemented. Results A novel alternative to the class III PSG system was demonstrated with independent boards capable of operating for more than 16 hours powered by a 750 mAh/3.7 V battery with 0.003% data loss during preliminary PSG exams. Additionally, a computer-based library capable of reading, decoding, estimating respiration through ECG, and calculating the heart rate was developed and described. Conclusion This article contributes to PSG research through the development of a new PSG system and the improvement of patient comfort. All software and hardware developed are fully open source and available on GitHub.

2.
Res. Biomed. Eng. (Online) ; 32(4): 318-326, Oct.-Dec. 2016. tab, graf
Article in English | LILACS | ID: biblio-842471

ABSTRACT

Abstract Introduction This paper presents a complete approach for the automatic classification of heartbeats to assist experts in the diagnosis of typical arrhythmias, such as right bundle branch block, left bundle branch block, premature ventricular beats, premature atrial beats and paced beats. Methods A pre-processing step was performed on the electrocardiograms (ECG) for baseline removal. Next, a QRS complex detection algorithm was implemented to detect the heartbeats, which contain the primary information that is employed in the classification approach. Next, ECG segmentation was performed, by which a set of features based on the RR interval and the beat waveform morphology were extracted from the ECG signal. The size of the feature vector was reduced by principal component analysis. Finally, the reduced feature vector was employed as the input to an artificial neural network. Results Our approach was tested on the Massachusetts Institute of Technology arrhythmia database. The classification performance on a test set of 18 ECG records of 30 min each achieved an accuracy of 96.97%, a sensitivity of 95.05%, a specificity of 90.88%, a positive predictive value of 95.11%, and a negative predictive value of 92.7%. Conclusion The proposed approach achieved high accuracy for classifying ECG heartbeats and could be used to assist cardiologists in telecardiology services. The main contribution of our classification strategy is in the feature selection step, which reduced classification complexity without major changes in the performance.

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