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An Open-Source Standard T-Wave Alternans Detector for Benchmarking A.Khaustov St.-Petersburg Institute of Cardiological Technics (Incart) S.Nemati Massachusetts Institute of Technology (MIT) G.D.Clifford MIT, Harvard- MIT Division of Health Sciences and Technology (HST)
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Open Source Solution - an open source algorithm detailed repeatable The software is at www.physionet.org/challenge/2008/www.physionet.org/challenge/2008/ = ? ?
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Preprocessing Baseline wander filtration QRS and T detection Independent QRS and T alignment Abnormal, noisy beat rejection via cross- correlation on QS and ST-T segments QT variation necessitates alignment on ST-T segment (sample from a real record) Aligned T waves A – even B – odd
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Spectral method (SM) Periodogram for a point on ST-T Averaged periodogram Alternans seriesSuccessive beats
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Modified Moving Average (MMA) ‘Continuous’ estimate (vs 128 beat segment in SM) Maximum difference on ST-T (vs averaging in SM) Even vs odd averageAlternans trend
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Tests on synthetic data TWA amplitudes were 2, 4, 6, 8, 10, 16, 22, 28, 34, 40, 60 V Maximum error after scaling: Clean records – all methods better than 2 V White noise with standard deviation of 5, 10, 20, 30, 40 V –SM ‘standard’ better than 6 V, SM ‘differences’ better than 5 V where successful (TWA greater than 0.35*noise level) –MMA better than 5 V where SM is successful; fails to reject low TWA/noise ratio –All methods improve as noise decreases and TWA increases Baseline wander (NST DB) added – all methods better than 7 V Tests on CinC challenge data Possible global bias in challenge results Challenge score and maximum error on synthetic records after scaling: SM ‘differences’ – 0.881 (third in challenge) and 8 V SM ‘standard’ – 0.880 and 10 V MMA – 0.400 and 12 V (0.834 with SM as noise detector!)
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To Do Noise vs TWA estimation for MMA Determining noise floor HR interval selection Periodogram normalization (TWA in V) TWA interpretation: positive, negative, indeterminate Lead-specific requirements TWA: from separate leads to ‘space’?
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