Stress assessment by means of heart rate derived from functional near-infrared spectroscopy

Abstract

Many studies have been carried out in order to detect and quantify the level of mental stress by means of different physiological signals. From the physiological point of view, stress promptly affects brain and cardiac function; therefore, stress can be assessed by analyzing the brain- and heart-related signals more efficiently. Signals produced by functional near-infrared spectroscopy (fNIRS) of the brain together with the heart rate (HR) are employed to assess the stress induced by the Montreal Imaging Stress Task. Two different versions of the HR are used in this study. The first one is the commonly used HR derived from the electrocardiogram (ECG) and is considered as the reference HR (RHR). The other is the HR computed from the fNIRS signal (EHR) by means of an effective combinational algorithm. fNIRS and ECG signals were simultaneously recorded from 10 volunteers, and EHR and RHR are derived from them, respectively. Our results showed a high degree of agreement [r > 0.9, BAR (Bland Altman ratio) <5 % ] between the two HR. A principal component analysis/support vector machine-based algorithm for stress classification is developed and applied to the three measurements of fNIRS, EHR, and RHR and a classification accuracy of 78.8%, 94.6%, and 62.2% were obtained for the three measurements, respectively. From these observations, it can be concluded that the EHR carries more useful information with regards to the mental stress than the RHR and fNIRS signals. Therefore, EHR can be used alone or in combination with the fNIRS signal for a more accurate and real-time stress detection and classification.

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