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)

Open Source Solution - an open source algorithm detailed repeatable The software is at = ? ?

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

Spectral method (SM) Periodogram for a point on ST-T Averaged periodogram Alternans seriesSuccessive beats

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

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’ – (third in challenge) and 8  V SM ‘standard’ – and 10  V MMA – and 12  V (0.834 with SM as noise detector!)

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’?