title: The effect of lossy ECG compression on QRS and HRV feature extraction.
abstract: This paper describes the performance of beat detection and heart rate variability (HRV) feature extraction on electrocardiogram signals which have been compressed and reconstructed with a lossy compression algorithm. The set partitioning in hierarchical trees (SPIHT) compression algorithm was used with sixteen compression ratios (CR) between 2 and 50 over the records of the MIT/BIH arrhythmia database. Sensitivities and specificities between 99% and 85% were computed for each CR utilised. The extracted HRV features were between 99% and 82% similar to the features extracted from the annotated records. A notable accuracy drop over all features extracted was noted beyond a CR of 30, with falls of 10% accuracy beyond this compression ratio.
authors.affiliation: Department of Electrical and Electronic Engineering, University College Cork, Ireland, Galway. niall@rennes.ucc.ie
authors.forename: Niall
authors.initials: N
authors.lastname: Twomey
authors.forename: Noel
authors.initials: N
authors.lastname: Walsh
authors.forename: Orla
authors.initials: O
authors.lastname: Doyle
authors.forename: Brian
authors.initials: B
authors.lastname: McGinley
authors.forename: Martin
authors.initials: M
authors.lastname: Glavin
authors.forename: Edward
authors.initials: E
authors.lastname: Jones
authors.forename: W P
authors.initials: WP
authors.lastname: Marnane
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D016477:Artifacts; D001185:Artificial Intelligence; D044962:Data Compression / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted