A Noise-Robust Model-Based Approach to T-Wave Amplitude Measurement and Alternans Detection

T-wave alternans (TWA) is a potential marker for sudden cardiac death, but its reliable analysis is often constrained to noise-free environments, limiting its utility in real-world settings. We explore model-based T-wave estimation and detection to mitigate noise effects on TWA.

Detection was performed using a surrogate-based method as a benchmark and a new approach based on a Markov model state transition matrix (STM). Estimation employed a Modified Moving Average (MMA) and polynomial T-wave modeling to improve noise robustness. Methods were evaluated across signal-to-noise ratios (SNRs) from -5 to 30 dB and noise types: baseline wander, muscle artifacts (MA), electrode movement (EM), and respiratory modulation. Synthetic ECGs with known TWA levels were used: 0 $\bm \mu$V for TWA-free and 30-72 $\bm \mu$V for TWA-present.

T-wave modeling improved estimation accuracy under noisy conditions. With MA noise at SNRs of -5 and 5 dB, mean absolute error (MAE) dropped from 62 to 49 $\bm \mu$V and 27 to 25 $\bm \mu$V, respectively (Mann-Whitney U test, $\bm {p < 0.05}$). Similar improvements occurred with EM noise: MAE decreased from 101 to 71 and 26 to 23 $\bm \mu$V. In detection, STM achieved sensitivity of 0.87, outperforming the surrogate-based method (0.70), though both struggled under EM noise at -5 dB. Detection performance also depended on the number of beats analyzed.

These findings show that applying model-based estimation and STM detection could improve TWA analysis under noise, supporting application in ambulatory and wearable ECG monitoring.