Real-time monitoring of ST change for telemedicine

Modern medical breakthroughs and general improvements in environmental and social conditions have raised the global life expectancy. As the world’s population is aging, the incidence and prevalence of chronic diseases increases. Dramatic increase in the numbers of chronically ill patients is profoundly affects the healthcare system. Care at home provides benefits not only to patients but also the community and the health care providers. A telemedicine system utilizing today’s information and mobile communication technologies plays a crucial role in providing care at home. Currently, diverse telemedicine projects are progress in the most countries. A telemedicine project is supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant 114E452in Turkey. This project aims end to end remote monitoring of patients with chronic diseases such as heart failure, diabetes, asthma, and high blood pressure. A clinical decision support system integrated to telemedicine improves prognosis and quality of life in patients. The mainstay of a decision support system is early detection of important clinical signs and prompts medical intervention. Cardiovascular diseases are the leading cause of death globally. People with cardiovascular disease need early detection. An effective decision support system is needed to detect ECG arrhythmia before a serious heart failure occurs. One of the aims of the project is to develop decision support system which will detect whether a beat is normal or arrhythmia. The ECG signals in MIT-BIH arrhythmia database and Long Term ST Database are used for training and testing the algorithm. A total of 103026 beat samples attributing to fifteen ECG beat types are selected for experiments in MIT-BIH arrhythmia database. 103026 RR intervals with ST segment change were selected from the Long Term ST Database. ST segment changes detection is just based on the signal between two consecutive R peaks. The features are obtained from Wigner-Ville transform of this signal. The classification algorithms provided by the MATLAB Classification Learner Toolbox were tested. The Cubic SVM achieved best results with accuracy of 98.03%, sensitivity of 98.04%, specificity of 98 % and positive predictive value of 98%. © Springer Nature Singapore Pte Ltd. 2017.

Yazar Kayıkçıoğlu İ.
Akdeniz F.
Kayıkçıoğlu T.
Kaya İ.
Yayın Türü Conference Object
Tek Biçim Adres https://hdl.handle.net/20.500.12628/7323
Tek Biçim Adres 10.1007/978-981-10-4166-2_101
Konu Başlıkları Electrocardiography(ECG)
Myocardial ischemia
ST segment
Telemedicine
Wigner-Ville distribution
Koleksiyonlar Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu
Dergi Adı IFMBE Proceedings
Dergi Cilt Bilgisi 62
Sayfalar 671 - 677
Yayın Yılı 2017
Eser Adı
[dc.title]
Real-time monitoring of ST change for telemedicine
Yayıncı
[dc.publisher]
Springer Verlag
Yayın Türü
[dc.type]
conferenceObject
Açıklama
[dc.description]
Erkona doo Sarajevo, Bosnia and Herzegovina;et al.;Oracle Bosnia and Herzegovina;Privredna banka dd Sarajevo, Bosnia and Herzegovina;Symphony Sarajevo, Bosnia and Herzegovina;Verlab doo Sarajevo, Bosnia and Herzegovina
Açıklama
[dc.description]
International Conference on Medical and Biological Engineering, CMBEBIH 2017 -- 16 March 2017 through 18 March 2017 -- -- 189859
Özet
[dc.description.abstract]
Modern medical breakthroughs and general improvements in environmental and social conditions have raised the global life expectancy. As the world’s population is aging, the incidence and prevalence of chronic diseases increases. Dramatic increase in the numbers of chronically ill patients is profoundly affects the healthcare system. Care at home provides benefits not only to patients but also the community and the health care providers. A telemedicine system utilizing today’s information and mobile communication technologies plays a crucial role in providing care at home. Currently, diverse telemedicine projects are progress in the most countries. A telemedicine project is supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant 114E452in Turkey. This project aims end to end remote monitoring of patients with chronic diseases such as heart failure, diabetes, asthma, and high blood pressure. A clinical decision support system integrated to telemedicine improves prognosis and quality of life in patients. The mainstay of a decision support system is early detection of important clinical signs and prompts medical intervention. Cardiovascular diseases are the leading cause of death globally. People with cardiovascular disease need early detection. An effective decision support system is needed to detect ECG arrhythmia before a serious heart failure occurs. One of the aims of the project is to develop decision support system which will detect whether a beat is normal or arrhythmia. The ECG signals in MIT-BIH arrhythmia database and Long Term ST Database are used for training and testing the algorithm. A total of 103026 beat samples attributing to fifteen ECG beat types are selected for experiments in MIT-BIH arrhythmia database. 103026 RR intervals with ST segment change were selected from the Long Term ST Database. ST segment changes detection is just based on the signal between two consecutive R peaks. The features are obtained from Wigner-Ville transform of this signal. The classification algorithms provided by the MATLAB Classification Learner Toolbox were tested. The Cubic SVM achieved best results with accuracy of 98.03%, sensitivity of 98.04%, specificity of 98 % and positive predictive value of 98%. © Springer Nature Singapore Pte Ltd. 2017.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Yılı
[dc.date.issued]
2017
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1007/978-981-10-4166-2_101
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/7323
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
Electrocardiography(ECG)
Konu Başlıkları
[dc.subject]
Myocardial ischemia
Konu Başlıkları
[dc.subject]
ST segment
Konu Başlıkları
[dc.subject]
Telemedicine
Konu Başlıkları
[dc.subject]
Wigner-Ville distribution
Yazar
[dc.contributor.author]
Kayıkçıoğlu İ.
Yazar
[dc.contributor.author]
Akdeniz F.
Yazar
[dc.contributor.author]
Kayıkçıoğlu T.
Yazar
[dc.contributor.author]
Kaya İ.
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
1680-0737
Sponsor YAYINCI
[dc.description.sponsorship]
114E452
Sponsor YAYINCI
[dc.description.sponsorship]
This research is supported by The Scientific and Technological Research Council of Turkey (T?B?TAK) under Grant 114E452 in Turkey.
Yazar Departmanı
[dc.contributor.department]
Zonguldak Bülent Ecevit Üniversitesi
İlk Sayfa Sayısı
[dc.identifier.startpage]
671
Son Sayfa Sayısı
[dc.identifier.endpage]
677
Dergi Adı
[dc.relation.journal]
IFMBE Proceedings
Dergi Cilt Bilgisi
[dc.identifier.volume]
62
ISBN
[dc.identifier.isbn]
9789811041655
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
6
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
09 Şubat 2024 22:41
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Tıklayınız
system telemedicine arrhythmia patients decision support diseases project detection 103026 medical selected signal Turkey Database segment detect failure MIT-BIH chronic clinical database fifteen develop whether signals experiments attributing samples algorithm before serious testing training occurs
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