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Dergiler / Turkish Journal of Electrical Engineering and Computer Science / 2014 / Cilt: 22 - Sayı: 2

Feature selection on single-lead ECG for obstructive sleep apnea diagnosis

Hüseyin GÜRÜLER
Mesut ŞAHİN
Abdullah FERİKOĞLU
Sayfa
465–478
Yayın Tarihi
2014-05-16
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The results show that the CMs can be utilized in minimizing the feature sets used for any type of diagnosis.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea (OSA) with a single-lead electrocardiogram.
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Temel Bulgular & Veri:This study presents a classification scheme for OSA using common features belonging to the time domain, frequency domain, and nonlinear calculations of heart rate variability analysis, and then proposes a method of feature selection based on correlation matrices (CMs).
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Sonuç & Katkı:The results show that the CMs can be utilized in minimizing the feature sets used for any type of diagnosis.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Heart rate variability · sleep apnea · feature selection

Özet

Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea (OSA) with a single-lead electrocardiogram. Although high accuracies have been achieved in detection of apneic episodes and classification into apnea/hypopnea, there has not been a consensus on the best method of selecting the feature parameters. This study presents a classification scheme for OSA using common features belonging to the time domain, frequency domain, and nonlinear calculations of heart rate variability analysis, and then proposes a method of feature selection based on correlation matrices (CMs). The results show that the CMs can be utilized in minimizing the feature sets used for any type of diagnosis.

Abstract

Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea (OSA) with a single-lead electrocardiogram. Although high accuracies have been achieved in detection of apneic episodes and classification into apnea/hypopnea, there has not been a consensus on the best method of selecting the feature parameters. This study presents a classification scheme for OSA using common features belonging to the time domain, frequency domain, and nonlinear calculations of heart rate variability analysis, and then proposes a method of feature selection based on correlation matrices (CMs). The results show that the CMs can be utilized in minimizing the feature sets used for any type of diagnosis.

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Bu Makaleye Atıf Yap

GÜRÜLER, H., ŞAHİN, M., FERİKOĞLU, A. (2014). Feature selection on single-lead ECG for obstructive sleep apnea diagnosis. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 22 - Sayı: 2, 465–478. https://ajindex.com/articles/188542

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Kaynakça (9)

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