Key result
Markov models improve T-wave alternans detection sensitivity by ~24% over surrogate methods under noisy conditions.
Why the study?
T-wave alternans is a potential marker for sudden cardiac death, but reliable analysis is often constrained to noise-free environments, limiting utility in real-world settings.
Absolute Event Rate: 0.87% vs 0.7%
p-value: p=<0.05
A novel model-based T-wave estimation and Markov model state transition matrix detection approach improves T-wave alternans analysis under noisy conditions, potentially enhancing its utility in ambulatory ECG monitoring.
May improve TWA detection in noisy ambulatory ECGs; leaves open prospective validation for clinical risk stratification.
OBJECTIVE: 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. METHODS: 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 μV for TWA-free and 30-72 μV for TWA-present. RESULTS: 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 μV and 27 to 25 μV, respectively (Mann-Whitney U test, p < 0.05). Similar improvements occurred with EM noise: MAE decreased from 101 to 71 and 26 to 23 μV. 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. CONCLUSION: 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.
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Koscova et al. (2025) studied T-wave alternans. Markov model state transition matrix (STM) and polynomial T-wave modeling vs. Surrogate-based method was evaluated on T-wave estimation accuracy (mean absolute error) and detection sensitivity (p=<0.05). Markov model state transition matrix detection improved T-wave alternans detection sensitivity to 0.87 versus 0.70 for the surrogate method under noisy conditions.
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