Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network
المؤلفون المشاركون
Wang, Tao
Lu, Changhua
Shen, Guohao
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-12-23
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Sleep apnea (SA) is a ubiquitous sleep-related respiratory disease.
It can occur hundreds of times at night, and its long-term occurrences can lead to some serious cardiovascular and neurological diseases.
Polysomnography (PSG) is a commonly used diagnostic device for SA.
But it requires suspected patients to sleep in the lab for one to two nights and records about 16 signals through expert monitoring.
The complex processes hinder the widespread implementation of PSG in public health applications.
Recently, some researchers have proposed using a single-lead ECG signal for SA detection.
These methods are based on the hypothesis that the SA relies only on the current ECG signal segment.
However, SA has time dependence; that is, the SA of the ECG segment at the previous moment has an impact on the current SA diagnosis.
In this study, we develop a time window artificial neural network that can take advantage of the time dependence between ECG signal segments and does not require any prior assumptions about the distribution of training data.
By verifying on a real ECG signal dataset, the performance of our method has been significantly improved compared to traditional non-time window machine learning methods as well as previous works.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Tao& Lu, Changhua& Shen, Guohao. 2019. Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network. BioMed Research International،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1128828
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Tao…[et al.]. Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network. BioMed Research International No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1128828
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Tao& Lu, Changhua& Shen, Guohao. Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1128828
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1128828
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر