A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults

Rossi, Alessio; Pozzo, Eleonora Da; Menicagli, Dario; Tremolanti, Chiara; Priami, Corrado; Sîrbu, Alina; Clifton, David A.; Martini, Claudia; Morelli, Davide

doi:10.3390/data5040091

Cited by 25 publications

(29 citation statements)

References 31 publications

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“…Descriptive statistics of all the participants’ psycho-physiological features provided in MMASH dataset are provided in Rossi et al’s data descriptor paper [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we used the Multilevel Monitoring of Activity and Sleep in Healthy people (MMASH) dataset [ 29 , 30 , 31 ] providing 24 h of continuous Inter-Beat Intervals data ( IBI ), triaxial accelerometer data, sleep quality, physical activity, and psychological characteristics (i.e., anxiety status, stress events, and emotions) for 22 healthy young males (age = 27.29 ± 4.21 yrs; height = 179.91 ± 8.22 cm; weight = 75.05 ± 12.79 kg). Participants’ anthropomorphic characteristics (i.e., age, height, and weight) were recorded at the start of the data recording.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Finally, twice a day (i.e., before going to bed and when they woke up), the subjects collected saliva samples at home in appropriate vials in order to assess Melatonin and Corisol saliva concentration. More details about the experimental setup of the MMASH dataset are provided in the data descriptor paper written by Rossi et al and published on MDPI Data journal in 2020 [ 29 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

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Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts

Rossi

Pedreschi

Clifton

et al. 2020

Sensors

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Application of ultra–short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people’s daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.

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“…Descriptive statistics of all the participants’ psycho-physiological features provided in MMASH dataset are provided in Rossi et al’s data descriptor paper [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

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Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts

Rossi

Pedreschi

Clifton

et al. 2020

Sensors

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“…Subjects included 8 men and 2 women; gender is not known for the remaining 21 subjects. mmash: Multilevel Monitoring of Activity and Sleep in Healthy people (MMASH) dataset [ 27 , 28 ] provides 24 h of continuous beat-to-beat heart data, triaxial accelerometer data, sleep quality, physical activity and psychological characteristics (i.e., anxiety status, stress events and emotions) for 22 healthy participants. …”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we used two PhysioNet datasets, providing about 23 [27,28] provides 24 h of continuous beat-to-beat heart data, triaxial accelerometer data, sleep quality, physical activity and psychological characteristics (i.e., anxiety status, stress events and emotions) for 22 healthy participants.…”

Section: Dataset and Feature Engineeringmentioning

confidence: 99%

SDNN24 Estimation from Semi-Continuous HR Measures

Morelli

Rossi

Bartoloni³

et al. 2021

Sensors

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The standard deviation of the interval between QRS complexes recorded over 24 h (SDNN24) is an important metric of cardiovascular health. Wrist-worn fitness wearable devices record heart beats 24/7 having a complete overview of users’ heart status. Due to motion artefacts affecting QRS complexes recording, and the different nature of the heart rate sensor used on wearable devices compared to ECG, traditionally used to compute SDNN24, the estimation of this important Heart Rate Variability (HRV) metric has never been performed from wearable data. We propose an innovative approach to estimate SDNN24 only exploiting the Heart Rate (HR) that is normally available on wearable fitness trackers and less affected by data noise. The standard deviation of inter-beats intervals (SDNN24) and the standard deviation of the Average inter-beats intervals (ANN) derived from the HR (obtained in a time window with defined duration, i.e., 1, 5, 10, 30 and 60 min), i.e., ANN=60HR (SDANNHR24), were calculated over 24 h. Power spectrum analysis using the Lomb-Scargle Peridogram was performed to assess frequency domain HRV parameters (Ultra Low Frequency, Very Low Frequency, Low Frequency, and High Frequency). Due to the fact that SDNN24 reflects the total power of the power of the HRV spectrum, the values estimated from HR measures (SDANNHR24) underestimate the real values because of the high frequencies that are missing. Subjects with low and high cardiovascular risk show different power spectra. In particular, differences are detected in Ultra Low and Very Low frequencies, while similar results are shown in Low and High frequencies. For this reason, we found that HR measures contain enough information to discriminate cardiovascular risk. Semi-continuous measures of HR throughout 24 h, as measured by most wrist-worn fitness wearable devices, should be sufficient to estimate SDNN24 and cardiovascular risk.

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