A multimodal approach combining ECG and ABP signals improves heartbeat detection accuracy compared to unimodal methods, particularly when ECG is noisy.
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Multimodal heartbeat detection may improve robustness when ECG is noisy; leaves open prospective validation before clinical use.
In this paper, a technique is proposed for detection of heartbeats in multimodal data. Recording of multiple physiological signals from the same subject is common practice nowadays. Multiple physiological signals are generally available but are processed separately without taking into consideration information from other relevant signals. The heartbeats are generally detected from R peaks in electrocardiogram (ECG) signal, however, if ECG is noisy, other signals reflecting the cardiac activity may be used for identifying heartbeats. This paper describes a new method for detection of heartbeats using ECG and arterial blood pressure (ABP) signals. The physiological data are segmented into various fragments and signal quality is determined using the judgment of noise level. If the ECG data fragment is noisy, heartbeats are computed from the ABP fragment. The evaluation was performed on training data set of computing in cardiology challenge 2014. The proposed methodology has resulted in better detection accuracy as compared to the unimodal methods.
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Singh et al. (2018) studied this question.
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