Intelligent Biological Alarm Clock for Monitoring Autonomic Nervous Recovery During Nap

Xie, Jialan; Wang, Wen; Liu, Guangyuan; Li, Yongtao

doi:10.2991/ijcis.d.190304.001

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…After acquiring the original ECG data, we took the following steps to obtain the IBI data samples. First, the peaks of R waves in the ECG were located by wavelet decomposition and reconstruction and an adaptive-length running window algorithm [12] . Second, after locating the R peaks, we calculated the R wave to R wave (RR) intervals as the IBI data and removed the abnormal RR interval values not within the range of mean ±3 times standard deviation.…”

Section: Methodsmentioning

confidence: 99%

Intervention effect of physical activity on mental fatigue: from the view of autonomic nervous pattern analysis

Sun,

zhang,

Wang

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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This article explored the effect of physical activity time and that of the heart rate during physical activity on mental fatigue intervention. First, we acquired electrocardiogram from 104 subjects, calculated the inter-beat intervals (IBI), removed the abnormal values in the IBI series, segmented and divided the IBI series into training and validation datasets and application datasets according to the inclusion criteria of mental fatigued and non-fatigued states. Second, we extracted 39 linear and non-linear RR parameters as fatigue physiological features, and applied Leave-One-Subject-Out cross test and Sequential Backward Selection algorithm for feature selection while training some traditional classifiers. Third, we applied the best trained classifier to detect mental fatigue before and after physical activity. Finally, we analyzed the change of mental fatigue status before and after physical activity and obtained the following three findings: (1) physical activity did not have intervention effect on mental fatigue for 62% observed cases; (2) for 38% observed cases, physical activity could aggravate or relieve mental fatigue; (3) for the observed cases that the physical activity time was in the range of 5-15 minutes, the average heart rate in 100-140 beat per minute during physical activity was more likely to relieve mental fatigue

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Section: Methodsmentioning

confidence: 99%

Intervention effect of physical activity on mental fatigue: from the view of autonomic nervous pattern analysis

Sun,

zhang,

Wang

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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show abstract

“…In order to quantify the ANS activity, we extracted 27 linear and nonlinear parameters from the RR interval series as features of cognitive load conditions. These parameters were commonly applied to HRV analysis in literature [ 30 , 31 , 32 , 33 ]. Besides, six commonly used EEG parameters which measured the central nervous system (CNS) activity, e.g., powers of sub-band brainwaves [ 34 ], were also applied as features of cognitive load conditions.…”

Section: Methodsmentioning

confidence: 99%

“…The original 27 HRV parameters and 6 EEG parameters were commonly applied to the analysis of ANS and CNS activities in literature [ 30 , 31 , 32 , 33 , 34 ]. However, they are not specific to the pattern recognition of cognitive load conditions.…”

Section: Methodsmentioning

confidence: 99%

Pattern Recognition of Cognitive Load Using EEG and ECG Signals

Xiong

Kong

Yang

et al. 2020

Sensors

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The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.

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“…The total uctuation in small scales Fluctuation of RR interval series under any beat lag scale n, re ecting the complexity of ANS activities [35].…”

Section: F(n)mentioning

confidence: 99%

The Effect of Active break on Autonomic Nervous Pattern Recognition of Learning States

Sun

zhang

Wang

et al. 2022

Preprint

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Purpose: This paper explores the effect of active break on the distinguishability of autonomic nervous patterns of learning states through machine learning. Method: First, we collected electrocardiogram (ECG) data of 77 subjects before and after active break, and accurately located the R-wave peaks from the ECG signal to calculate the RR interval series. Second, the RR interval samples were segmented according to the inclusion criteria of certain learning states. The initial 39 ECG features were empirically calculated, and the optimal feature combination for learning states recognition was selected through sequential backward selection and leave-one-subject-out cross test. Finally, we established binary-classification models of pairs of learning states and compared their performance of learning states recognition before and after active break. Discussion: Active break can promote or inhibit the students’ academic performance. Besides statistical analysis shows the stability of the cognitive ability. What’s more active break increases the physiological response to fatigue Conclusion: (1) The autonomic nervous patterns of knowledge input-processing and retrieval-processing and those of cognitive load matching and mismatching in knowledge retrieval processing became less distinguishable with the effect of active break. (2) The autonomic nervous patterns of mental fatigue and no fatigue states became more distinguishable with the effect of active break. (3) Stronger parasympathetic nervous activities make students achieve better academic performance during using new knowledge to solve problem stage.

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Intelligent Biological Alarm Clock for Monitoring Autonomic Nervous Recovery During Nap

Cited by 8 publications

References 20 publications

Intervention effect of physical activity on mental fatigue: from the view of autonomic nervous pattern analysis

Intervention effect of physical activity on mental fatigue: from the view of autonomic nervous pattern analysis

Pattern Recognition of Cognitive Load Using EEG and ECG Signals

The Effect of Active break on Autonomic Nervous Pattern Recognition of Learning States

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