Hidden Markov model-based heartbeat detector using different transformations of ECG and ABP signals

Nelson F. Monroy; Miguel Altuve

doi:10.1117/12.2546602

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3 January 2020 Hidden Markov model-based heartbeat detector using different transformations of ECG and ABP signals

Nelson F. Monroy, Miguel Altuve

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Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 113300S (2020) https://doi.org/10.1117/12.2546602
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia

Abstract

The detection of the heartbeat from electrocardiographic (ECG) and arterial blood pressure (ABP) signals, either exploited individually or jointly, has been carried out successfully using different approaches that range from the use of simple digital signal processing techniques until the use of more advanced techniques based on machine learning. In this paper, we employed a heartbeat detector that uses two hidden Markov models (HMM) to characterize the dynamics of the presence and the absence of heartbeats in ECG and ABP signals. The HMM-based detector can exploit univariate observations (ECG or ABP signals) or bivariate observations (ECG an ABP signals jointly, in a centralized manner). Two transformations of the signals were applied as a preprocessing step: absolute value and squared functions. In this sense, six detectors based on univariate observations and nine detectors based on bivariate observations were conceived and validated in ten records of the MGH/MF Waveform Database. The detection performance when the absolute value of ECG and the absolute value of ABP are jointly exploited by the HMM produced T P = 58736, FN = 631, FP = 788, sensitivity = 98.73%, positive predictivity = 98.22%).

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Nelson F. Monroy and Miguel Altuve "Hidden Markov model-based heartbeat detector using different transformations of ECG and ABP signals", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300S (3 January 2020); https://doi.org/10.1117/12.2546602

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