Poincaré Plot Indexes of Heart Rate Variability: Pattern II Responses and Mental Workload

Kurosaka, Chie; Kuraoka, Hiroyuki; Miyake, Shinji

doi:10.1007/978-3-030-23528-4_33

Cited by 1 publication

(1 citation statement)

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“…As mentioned in Section 1 , cardiovascular activity is known to relate to human cognitive function, and ECG-derived HRV parameters have previously been used to quantify the cognitive workload of drivers and pilots. In general, HRV can be analyzed either in the time domain or the frequency domain [ 35 , 36 ]. In this study, we pursued time domain analysis as it (1) facilitates translation from the ECG-derived metrics to the MCG-derived ones, (2) empowers evaluation with limited recording time and (3) has been widely used in prior works [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor

Wang

Zhu

Kaur

et al. 2022

Sensors

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Quantifying cognitive workload, i.e., the level of mental effort put forth by an individual in response to a cognitive task, is relevant for healthcare, training and gaming applications. However, there is currently no technology available that can readily and reliably quantify the cognitive workload of an individual in a real-world environment at a seamless way and affordable price. In this work, we overcome these limitations and demonstrate the feasibility of a magnetocardiography (MCG) sensor to reliably classify high vs. low cognitive workload while being non-contact, fully passive and low-cost, with the potential to have a wearable form factor. The operating principle relies on measuring the naturally emanated magnetic fields from the heart and subsequently analyzing the heart rate variability (HRV) matrix in three time-domain parameters: standard deviation of RR intervals (SDRR); root mean square of successive differences between heartbeats (RMSSD); and mean values of adjacent R-peaks in the cardiac signals (MeanRR). A total of 13 participants were recruited, two of whom were excluded due to low signal quality. The results show that SDRR and RMSSD achieve a 100% success rate in classifying high vs. low cognitive workload, while MeanRR achieves a 91% success rate. Tests for the same individual yield an intra-subject classification accuracy of 100% for all three HRV parameters. Future studies should leverage machine learning and advanced digital signal processing to achieve automated classification of cognitive workload and reliable operation in a natural environment.

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Section: Methodsmentioning

confidence: 99%