Constructing bodily emotion maps based on high-density body surface potentials for psychophysiological computing.

Emotion is a complex physiological and psychological activity, accompanied by subjective physiological sensations and objective physiological changes. The body sensation map describes the changes in body sensation associated with emotion in a topographic manner, but it relies on subjective evaluations from participants. Physiological signals are a more reliable measure of emotion, but most research focuses on the central nervous system, neglecting the importance of the peripheral nervous system. In this study, a body surface potential mapping (BSPM) system was constructed, and an experiment was designed to induce emotions and obtain high-density body surface potential information under negative and non-negative emotions. Then, by constructing and analyzing the functional connectivity network of BSPs, the high-density electrophysiological characteristics are obtained and visualized as bodily emotion maps. The results showed that the functional connectivity network of BSPs under negative emotions had denser connections, and emotion maps based on local clustering coefficient (LCC) are consistent with BSMs under negative emotions. in addition, our features can classify negative and non-negative emotions with the highest classification accuracy of 80.77%. In conclusion, this study constructs an emotion map based on high-density BSPs, which offers a novel approach to psychophysiological computing.

A novel physiological feature selection method for emotional stress assessment based on emotional state transition.

The connection between emotional states and physical health has attracted widespread attention. The emotional stress assessment can help healthcare professionals figure out the patient's engagement toward the diagnostic plan and optimize the rehabilitation program as feedback. It is of great significance to study the changes of physiological features in the process of emotional change and find out subset of one or several physiological features that can best represent the changes of psychological state in a statistical sense. Previous studies had used the differences in physiological features between discrete emotional states to select feature subsets. However, the emotional state of the human body is continuously changing. The conventional feature selection methods ignored the dynamic process of an individual's emotional stress in real life. Therefore, a dedicated experimental was conducted while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), Galvanic Skin Resistance (GSR), and Blood Volume Pulse (BVP), were continuously acquired. This paper reported a novel feature selection method based on emotional state transition, the experimental results show that the number of physiological features selected by the proposed method in this paper is 13, including 5 features of ECG, 4 features of PPG and 4 features of GSR, respectively, which are superior to PCA method and conventional feature selection method based on discrete emotional states in terms of dimension reduction. The classification results show that the accuracy of the proposed method in emotion recognition based on ECG and PPG is higher than the other two methods. These results suggest that the proposed method can serve as a viable alternative to conventional feature selection methods, and emotional state transition deserves more attention to promote the development of stress assessment.

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.

Spectrum power and brain functional connectivity of different EEG frequency bands in attention network tests.

There have been many previous studies on brain electrical activity and attention function, but the research on observing the cognitive function of attention from frequency brain electrical indicators remains insufficient. This study proposed an attentional network test (ANT) of Chinese version and used frequency analysis methods to observe the power spectrum activity and functional connectivity of delta (δ), theta (θ), alpha (α) bands of EEG signals to further understand their relationship with attention networks. The attentional network test was composed of alerting network, orienting network and execute conflict network, and these networks were compared with the resting state in different frequency bands. The results showed that α band activity was significantly suppressed in all three attentional states, and the power of θ band activity dramatically increased for the execute conflict network. The negative connection of α band in the long distance (frontal lobe to parietal lobe or occipital lobe) might be a sign of resting state network, and the positive connections between δ and θ band in similar areas could be an indicator of execute conflict network. This pilot study suggests that the frequency domain analysis of EEG signals could be a great tool to visualize the brain activities in response to different attentional networks.Clinical Relevance- This pilot study proved that the frequency bands activity might be suitable objective neuro-markers to distinguish different attention states.

A Pilot Study on Long-term Physiological Signal Monitoring using Anhydrous Viscoplastic Electrodes.

Electrocardiography (ECG) and Electromyogram (EMG) are widely used to help physicians to diagnose various diseases. Besides, long-term physiological signals monitoring is of great significance for circumstances where certain diseases may not be observed in short-term monitoring. At present, wet electrodes are widely used in the clinic and are considered as a standard method to acquire physiological signals in high fidelity. However, current wet electrodes achieve high-quality signal acquisition by using conductive gel which will dry up as time elapses and finally leads to degradation of the signal quality. Therefore, an anhydrous viscoplastic electrode was proposed in this paper to solve the abovementioned problem. The proposed electrode, which is anhydrous and viscoplastic, enables high quality physiological signal acquisition with firm contact with the skin and it will not dry up within a long period of time. The results showed that the impedance of the proposed viscoplastic electrode could maintain relative stability after two days while that of the gel electrodes would increase significantly due to the gel dried up. Besides, the proposed electrode obtained physiological signals with high quality in both ECG and EMG tasks. After 24 hours of monitoring, the signal quality of the proposed electrode remained unchanged, indicated by the clearly recognizable time-domain signals. However, the signal waveform completely submerged in noise after the gel dried up. Moreover, the superior performance of the viscoplastic electrodes could be confirmed by the SNR difference between the two days, SNR further confirmed the superiority of the, with -2.03±2.10 dB and -3.40±8.27 dB for ECG and EMG respectively, and the SNR difference of gel electrodes were -7.59 ± 5.70 dB and -35.39±15.71 dB respectively. The proposed electrodes could be a great candidate for long-term physiological signal monitoring in risk management of healthcare.Clinical Relevance- The proposed electrode could achieve long-term physiological signals monitoring with high quality.

The effects of calcium channel blocker benidipine and calmodulin antagonist W7 on GDP-binding capacity of brown adipose tissue in mice.

It has been suggested that increased dietary calcium intake can attenuate obesity. Calcium antagonists, such as benidipine, also have been shown to have an anti-obesity effect. However, the mechanism for calcium-related anti-obesity effect has not yet been established. A defective brown adipose tissue thermogenesis has been shown in obese rodents. This study was designed to examine the direct effects of calcium channel blocker benidipine and calmodulin antagonist W7 administration on the adaptive thermogenesis in brown adipose tissue taken from the genetically obese mice and their lean controls. The GDP binding to brown-fat cell mitochondria was used as a brown adipose tissue thermogenic index. The results show that benidipine treatment had no marked effect on brown-fat cell GDP-binding capacities in both obese and lean mice. However, GDP-binding capacities were significantly reduced in both obese and lean mice after the W7 administration. The results of this study support the previous finding that benidipine did not have direct thermogenic effect on brown adipose tissue and suggest that the change in intracellular calmodulin availability might contribute to the adaptive thermogenesis in brown adipose tissue.

[Reperfusion arrhythmias in acute myocardial infarction do not enhance myocardial injury].

OBJECTIVE: To investigate the clinical implications of reperfusion arrhythmias during primary percutaneous coronary intervention (PCI) for patients with acute myocardial infarction (AMI). METHODS: Data from 228 AMI patients in whom the infarct-related artery (IRA) were successfully recanalized by primary PCI were retrospectively analyzed. The 228 patients were divided into 2 groups: myocardial ischemia-reperfusion injury (MIRI) group (n=119) in whom MIRI events occurred within minutes after successful recanalization of IRA, and non-MIRI group (n=109). The 119 patients in MIRI group were further divided into 3 subgroups: severe bradycardia with hypotension (brady-arrhythmia subgroup), lethal ventricular arrhythmias requiring electrical cardioversion (tachy-arrhythmia subgroup), and IRA antegrade flow less than or equal to TIMI 2 grade without angiographic evidence of abrupt closure (no-reflow subgroup). RESULTS: (1) Clinical and angiographic data: Compared with non-MIRI group, MIRI group was characterized by more inferior infarct location, shorter ischemic duration, more frequently right coronary artery as IRA, more diseased vessels, more often TIMI 0 grade of initial antegrade flow in IRA, less pre-infarction angina, more renal insufficiency, and higher in-hospital mortality (13.4% vs. 4.6%, P=0.021). (2) The peak CK level was remarkably lower in brady-arrhythmia subgroup than that in non-MIRI group (2010 IU/L vs. 2521 IU/L, P=0.039). The peak CK or CK-MB level was notably higher in no-reflow subgroup than in non-MIRI group (4573 IU/L, 338 IU/L, respectively, P=0.000). (3) Left ventricular ejection fraction in no-reflow subgroup was significantly lower than in non-MIRI group (38.7% +/- 8.3% vs. 51.2% +/- 8.1%, P=0.000), left ventricular end-diastolic volume in no-reflow subgroup was greater than that in tachy-arrhythmia subgroup [(135 +/- 32) ml vs. (105 +/- 19) ml, P=0.029]. CONCLUSION: Reperfusion arrhythmias may imply the existence of much survived myocardium and do not enhance myocardial damage, while no-reflow increases myocardial injury and induces permanent impairment of cardiac function.

[Analysis on correlative factors for occurrence of myocardial ischemia-reperfusion injury during primary percutaneous coronary intervention for acute myocardial infarction].

OBJECTIVE: To explore the risk and protective factors for the occurrence of myocardial ischemia-reperfusion injury (MIRI) during primary percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI). METHODS: Clinical and angiographic data of 228 AMI patients in whom the infarct-related arteries (IRA) were successfully revascularized by primary PCI were analyzed retrospectively. MIRI was defined if the following conditions existed after PCI: severe bradycardia with hypotension, or lethal ventricular arrhythmias requiring electrical cardioversion, or IRA antegrade flow < or = TIMI 2 grade flow without angiographic evidence of thrombus, emboli, dissection or spasm. Multivariate logistic regression was used to identify independent relative factors among 18 clinical and angiographic factors for occurrence of MIRI. RESULTS: Multivariate logistic regression analysis showed that independent risk factors for MIRI were the time intervals from AMI onset to IRA reflow < or = 6 h (P = 0.014), inferior infarction localization (P = 0.006), IRA antegrade flow prior to PCI < or = TIMI 1 grade (P = 0.028), multivessel lesions (P = 0.063) and renal insufficiency (P = 0.067). Pre-infarction angina was found to be an independent protective factor (P = 0.005). CONCLUSIONS: Short time intervals from AMI onset to IRA revascularization, inferior wall infarction location, low IRA antegrade flow prior to PCI, multivessel lesions and renal insufficiency may promote the occurrence of MIRI during primary PCI, whereas pre-infarction angina may be a cardioprotective factor attenuating MIRI.