Patient-specific detection of ventricular tachycardia in remote continuous health devices

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Ventricular Tachycardia (VT) is a dangerous arrhythmic event which can lead to sudden cardiac death if not detected and taken care of in time. This work uses non-linear features derived from Recurrence Quantification Analysis (RQA) along with Kolmogorov complexity, by analyzing the ECG signals, to train a classifier which can predict VT prior to their onset in remote continuous health devices. Compressed ECG signal along with amplitude ranges extracted from the ECG signal are used as features to strengthen the classifier. Stacked Denoising Autoencoders (SDAE) are used for the purpose of feature extraction and compression of signals, and their performance is compared with other works that detect VT for different window sizes. Softmax Regression is used as the classifier in this work. The proposed method is tested against MIT-BIH Arrhythmia database, MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB) and Creighton University Ventricular Tachyarrhythmia Database (CUDB). A total of 96.52% accuracy with 96.18% sensitivity is obtained after testing the proposed method on all test records.

Original languageEnglish (US)
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages529-532
Number of pages4
Volume2016-October
ISBN (Electronic)9781457702204
DOIs
StatePublished - Oct 13 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: Aug 16 2016Aug 20 2016

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/TerritoryUnited States
CityOrlando
Period8/16/168/20/16

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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