A Comparative Study between ECG-based and PPG-based Heart Rate Monitors for Stress Detection

Pinge, Anuja; Bandyopadhyay, Sambaran; Ghosh, Surjya; Sen, Sougata

doi:10.1109/comsnets53615.2022.9668342

Cited by 10 publications

(1 citation statement)

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“…Many strategies of data rescaling have been proposed in the literature to normalize the signals of each person in a similar range of values [47]. A min-max normalization was applied to the filtered signal of each participant to rescale the heart rate data in the range [0, 1] in [29,48,49]. Similarly, in [5], the amplitude of each subject's PPG signal was rescaled in the range [0, 1000] using a min-max normalization strategy followed by the multiplication by a constant factor α = 1000.…”

Section: State-of-the-artmentioning

confidence: 99%

Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition

Gasparini

Grossi

Giltri

et al. 2022

Signals

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Physiological responses are currently widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity was considered in the case of Photoplethysmography (PPG), which was successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is herein proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject’s heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization was evaluated in comparison to other normalization procedures in a binary classification task, where cognitive load and relaxed state were considered. The results obtained on two different datasets available in the literature confirmed that applying the proposed normalization strategy permitted increasing the classification performance.

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