Academic Journal

Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network

التفاصيل البيبلوغرافية
العنوان: Analysis of Abnormal Intra-QRS Potentials in Signal-Averaged Electrocardiograms Using a Radial Basis Function Neural Network
المؤلفون: Chun-Cheng Lin
المصدر: Sensors; Volume 16; Issue 10; Pages: 1580
بيانات النشر: Multidisciplinary Digital Publishing Institute
سنة النشر: 2016
المجموعة: MDPI Open Access Publishing
مصطلحات موضوعية: abnormal intra-QRS potentials, ventricular late potentials, radial basis function neural network, orthogonal least squares, ventricular tachycardia
الوصف: Abnormal intra-QRS potentials (AIQPs) are commonly observed in patients at high risk for ventricular tachycardia. We present a method for approximating a measured QRS complex using a non-linear neural network with all radial basis functions having the same smoothness. We extracted the high frequency, but low amplitude intra-QRS potentials using the approximation error to identify possible ventricular tachycardia. With a specified number of neurons, we performed an orthogonal least squares algorithm to determine the center of each Gaussian radial basis function. We found that the AIQP estimation error arising from part of the normal QRS complex could cause clinicians to misjudge patients with ventricular tachycardia. Our results also show that it is possible to correct this misjudgment by combining multiple AIQP parameters estimated using various spread parameters and numbers of neurons. Clinical trials demonstrate that higher AIQP-to-QRS ratios in the X, Y and Z leads are visible in patients with ventricular tachycardia than in normal subjects. A linear combination of 60 AIQP-to-QRS ratios can achieve 100% specificity, 90% sensitivity, and 95.8% total prediction accuracy for diagnosing ventricular tachycardia.
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
Relation: https://dx.doi.org/10.3390/s16101580
DOI: 10.3390/s16101580
الاتاحة: https://doi.org/10.3390/s16101580
Rights: https://creativecommons.org/licenses/by/4.0/
رقم الانضمام: edsbas.FCAA7A74
قاعدة البيانات: BASE