Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals

作者：

Highlights：

• Classification 17 ECG classes is proposed.

• A novel hexadecimal ternary pattern is employed.

• Multilevel wavelet is used to extract features deeply.

• Obtained classification accuracy of 95.0%.

• Proposed method is computationally less intensive and fast.

摘要

•Classification 17 ECG classes is proposed.•A novel hexadecimal ternary pattern is employed.•Multilevel wavelet is used to extract features deeply.•Obtained classification accuracy of 95.0%.•Proposed method is computationally less intensive and fast.

论文关键词：Hexadecimal local pattern,Multilevel DWT,ECG classification,Pattern recognition,Biomedical engineering

论文评审过程：Received 4 February 2019, Revised 3 August 2019, Accepted 6 August 2019, Available online 12 August 2019, Version of Record 5 November 2019.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.104923