A new approach for identification of heartbeats in multimodal physiological signals

Aviation and neurophysiology: A systematic review

This paper systematically reviews 20 years of publications (N = 54) on aviation and neurophysiology. The main goal is to provide an account of neurophysiological changes associated with flight training with the aim of identifying neurometrics indicative of pilot's flight training level and task relevant mental states, as well as to capture the current state-of-art of (neuro)ergonomic design and practice in flight training. We identified multiple candidate neurometrics of training progress and workload, such as frontal theta power, the EEG Engagement Index and the Cognitive Stability Index. Furthermore, we discovered that several types of classifiers could be used to accurately detect mental states, such as the detection of drowsiness and mental fatigue. The paper advances practical guidelines on terminology usage, simulator fidelity, and multimodality, as well as future research ideas including the potential of Virtual Reality flight simulations for training, and a brain-computer interface for flight training.

Hidden Markov model-based heartbeat detector using electrocardiogram and arterial pressure signals

The automatic detection of a heartbeat is commonly performed by detecting the QRS complex in the electrocardiogram (ECG), however, various noise sources and missing data can jeopardize the reliability of the ECG. Therefore, there is a growing interest in combining the information from many physiological signals to accurately detect heartbeats. To this end, hidden Markov models (HMMs) are used in this work to jointly exploit the information from ECG, arterial blood pressure (ABP) and pulmonary arterial pressure (PAP) signals in order to conceive a heartbeat detector. After preprocessing the physiological signals, a sliding window is used to extract an observation sequence to be passed through two HMMs (previously trained on a training dataset) in order to obtain the log-likelihoods of observation and signals a detection if the difference of log-likelihoods exceeds an adaptive threshold. Several HMM-based heartbeat detectors were conceived to exploit the information from the ECG, ABP and PAP signals from the MIT-BIH Arrhythmia, PhysioNet Computing in Cardiology Challenge 2014, and MGH/MF Waveform databases. A grid search methodology was used to optimize the duration of the observation sequence and a multiplicative factor to form the adaptive threshold. Using the optimal parameters found on a training database through 10-fold cross-validation, sensitivity and positive predictivity above 99% were obtained on the MIT-BIH Arrhythmia and PhysioNet Computing in Cardiology Challenge 2014 databases, while they are above 95% in the MGH/MF waveform database using ECG and ABP signals. Our detector approach showed detection performances comparable with the literature in the three databases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-021-00192-x.

Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review

This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out.