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   Kulkarni, Kanchan; Walton, Richard D.; Armoundas, Antonis A.; Tolkacheva, Elena G. (June 2021, Journal of the American Heart Association)

   null (Ed.)

   Abstract Life‐threatening ventricular arrhythmias and sudden cardiac death are often preceded by cardiac alternans, a beat‐to‐beat oscillation in the T‐wave morphology or duration. However, given the spatiotemporal and structural complexity of the human heart, designing algorithms to effectively suppress alternans and prevent fatal rhythms is challenging. Recently, an antiarrhythmic constant diastolic interval pacing protocol was proposed and shown to be effective in suppressing alternans in 0‐, 1‐, and 2‐dimensional in silico studies as well as in ex vivo whole heart experiments. Herein, we provide a systematic review of the electrophysiological conditions and mechanisms that enable constant diastolic interval pacing to be an effective antiarrhythmic pacing strategy. We also demonstrate a successful translation of the constant diastolic interval pacing protocol into an ECG‐based real‐time control system capable of modulating beat‐to‐beat cardiac electrical activity and preventing alternans. Furthermore, we present evidence of the clinical utility of real‐time alternans suppression in reducing arrhythmia susceptibility in vivo. We provide a comprehensive overview of this promising pacing technique, which can potentially be translated into a clinically viable device that could radically improve the quality of life of patients experiencing abnormal cardiac rhythms. 

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2. Beat-by-beat Classification of ECG Signals with Machine Learning Algorithm for Cardiac Episodes

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3. Scalable and Upgradable AI for Detected Beat-By-Beat ECG Signals in Smart Health

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5. Enhancing Beat-to-Beat Analysis of Heart Signals With Respiration Harmonics Reduction Through Demodulation and Template Matching

   https://doi.org/10.1109/TMTT.2023.3324444  

   Sameera, Jannatun Noor; Ishrak, Mohammad Shadman; Lubecke, Victor M; Boric-Lubecke, Olga (January 2024, IEEE Transactions on Microwave Theory and Techniques)

   Heart rate variability (HRV) analysis using Doppler radar (DR) is a promising method for noninvasive health and stress assessment. However, the respiration signal harmonic content typically limits HRV parameter estimation accuracy. While several harmonic reduction techniques have been used to improve the average HR estimation accuracy, achieving high beat-to-beat interval (BBI) accuracy is still challenging. This article demonstrates that arctangent demodulation (AD) with wavelet-based signal processing enhanced with template matching is effective to estimate HRV parameters with accuracy on the order of 10 ms with 2.4 GHz DR. Moreover, it was theoretically and experimentally demonstrated that the cases where AD provides limited improvement due to phase delay between thorax and abdomen motion are easily identifiable, and can be alternatively processed using a single-channel data. 

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