A Joint QRS Detection and Data Compression Scheme for Wearable Sensors

Author

Deepu, C.J. ; Lian, Yong

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore

Volume

62

Issue

1

fYear

2015

fDate

Jan. 2015

Firstpage

165

Lastpage

175

Abstract

This paper presents a novel electrocardiogram (ECG) processing technique for joint data compression and QRS detection in a wireless wearable sensor. The proposed algorithm is aimed at lowering the average complexity per task by sharing the computational load among multiple essential signal-processing tasks needed for wearable devices. The compression algorithm, which is based on an adaptive linear data prediction scheme, achieves a lossless bit compression ratio of 2.286x. The QRS detection algorithm achieves a sensitivity (Se) of 99.64% and positive prediction (+P) of 99.81% when tested with the MIT/BIH Arrhythmia database. Lower overall complexity and good performance renders the proposed technique suitable for wearable/ambulatory ECG devices.

Keywords

biomedical equipment; data compression; electrocardiography; medical signal detection; medical signal processing; sensors; MIT-BIH arrhythmia database; adaptive linear data prediction scheme; ambulatory ECG devices; computational load; electrocardiogram processing technique; joint QRS detection; joint data compression; lossless bit compression ratio; multiple essential signal-processing tasks; wearable ECG devices; wireless wearable sensor; Complexity theory; Data compression; Electrocardiography; Image edge detection; Noise; Sensors; Wireless communication; ECG-on-chip; QRS detection; lossless data compression; wearable devices; wireless sensors;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/TBME.2014.2342879

Filename

6863633