Efficient compressive sensing of ECG segments based on machine learning for QRS-based arrhythmia detection | IEEE Conference Publication | IEEE Xplore

Efficient compressive sensing of ECG segments based on machine learning for QRS-based arrhythmia detection


Abstract:

A novel method for efficient telemonitoring of arrhythmia based on using QRS complexes is proposed. Two features, namely, sum of absolute differences (SAD) and maximum of...Show More

Abstract:

A novel method for efficient telemonitoring of arrhythmia based on using QRS complexes is proposed. Two features, namely, sum of absolute differences (SAD) and maximum of absolute differences (MAD) are efficiently computed for each ECG segment in the bio-sensor. The computed features can be transmitted from the bio-sensor using wireless channel, and they can be used in the receiver for determining the absence of QRS complex in the segment. By avoiding computationally expensive signal reconstruction for the ECG segments without QRS complex, it is shown, using simulation results, that computation time can be reduced by approximately 7.4% for long-term telemonitoring of QRS-based arrhythmia. Detection of the absence of QRS complex can be carried out in around 7 milliseconds in a standard laptop computer with 2.2GHz processor and 8GB RAM.
Date of Conference: 16-20 August 2016
Date Added to IEEE Xplore: 18 October 2016
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ISSN Information:

PubMed ID: 28269328
Conference Location: Orlando, FL, USA

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