ECG-based pulse detection during cardiac arrest using random forest classifier.
Sudden cardiac arrest is one of the leading causes of death in the industrialized world. Pulse detection is essential for the recognition of the arrest and the recognition of return of spontaneous circulation during therapy, and it is therefore crucial for the survival of the patient. This paper int...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 2; pp. 453 - 463 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134311238&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134311238 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2019 vid: 57 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134311238 134311238 NLM30215212 10.1007/s11517-018-1892-2 NLM30215212 134311238 ppf: 453 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: ECG-based pulse detection during cardiac arrest using random forest classifier. aug: au: Elola, Andoni Aramendi, Elisabete Irusta, Unai Del Ser, Javier Alonso, Erik Daya, Mohamud affil: Communications Engineering Department, University of the Basque Country UPV/EHU, Alameda Urquijo S/N, 48013, Bilbao, Spain sug: subj: Heart Arrest Physiopathology Heart Rate Physiology Algorithms Resuscitation, Cardiopulmonary Methods Sensitivity and Specificity Electrocardiography Methods ab: Sudden cardiac arrest is one of the leading causes of death in the industrialized world. Pulse detection is essential for the recognition of the arrest and the recognition of return of spontaneous circulation during therapy, and it is therefore crucial for the survival of the patient. This paper introduces the first method based exclusively on the ECG for the automatic detection of pulse during cardiopulmonary resuscitation. Random forest classifier is used to efficiently combine up to nine features from the time, frequency, slope, and regularity analysis of the ECG. Data from 191 cardiac arrest patients was used, and 1177 ECG segments were processed, 796 with pulse and 381 without pulse. A leave-one-patient out cross validation approach was used to train and test the algorithm. The statistical distributions of sensitivity (SE) and specificity (SP) for pulse detection were estimated using 500 patient-wise bootstrap partitions. The mean (std) SE/SP for nine-feature classifier was 88.4 (1.8) %/89.7 (1.4) %, respectively. The designed algorithm only requires 4-s-long ECG segments and could be integrated in any commercial automated external defibrillator. The method permits to detect the presence of pulse accurately, minimizing interruptions in cardiopulmonary resuscitation therapy, and could contribute to improve survival from cardiac arrest. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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