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The objective of this study was to develop a computational algorithm capable of locating artifacts and identifying epileptic seizures, which specifically implementing in clinical stereoelectroencephalography (SEEG) recordings. Based on the nonstationary nature and broadband features of SEEG signals, a comprehensive strategy combined with the complex wavelet transform (CWT) and multi-layer thresholding method was implemented for both noise reduction and seizure detection. The artifacts removal pipeline integrated edge artifact removal, discrete spectrum analysis, and peak density evaluation. For automatic seizure detection, integrated power analysis and multi-dynamic thresholding were applied. The F1score was applied to evaluate overall performance of the algorithm. The algorithm was tested using expert-marked, double-blinded, clinical SEEG data from seven patients undergoing presurgical evaluation. This approach achieved the F1 score of 0.86 for noise reduction and 0.88 for seizure detection. This offline-approach method with minimum parameter tuning procedures and no prior information required, proved to be a feasible and solid solution for clinical SEEG data evaluation. Moreover, the algorithm can be improved with additional tuning and implemented with machine learning postprocessing pipelines.Despite prevention efforts, the prevalence of workrelated upper extremity musculoskeletal disorders (WRUED) is increasing. A limit in the development of preventive interventions is the lack of devices that can measure and process sEMG signals in order to provide real-time reliable information on muscular fatigue of the upper limb in relation to the physical demands of the work. In this paper, the development and evaluation of a real-time muscle fatigue detection algorithm based on sEMG will be presented. The proposed algorithm uses the median frequency of sEMG power spectrum density (PSD) obtained with the Continuous Wavelet Transform (CWT) as an indicator of the muscle fatigue level. To extend the algorithm's efficiency to dynamic tasks, a muscle contraction detection module is added in order to remove the segments when the muscle is not contracting. To assess the algorithm's performance, eight healthy adults performed simple static and dynamic shoulder tasks using different loads. The results of the proposed time-frequency method (i.e. CWT) were first compared to those of the traditional Short Time Fourier Transform (STFT). It was shown that the CWT performs better than the STFT in both static and dynamic loading conditions. The validity of the algorithm's output as a muscle fatigue indicator was verified by comparing the output's decrease rate with different loads. As expected, the algorithm's fatigue indicator decreased faster over time with heavier loads. It was also shown that the initial muscle fatigue estimation output is independent of the load. Finally, we studied the proposed muscle contraction detection module's efficiency to overcome issues associated with dynamic tasks. We observed a substantial improvement of the smoothness of the fatigue indicator's evolution by using of the muscle contraction detection module.Much of our understanding of experience-dependent plasticity originates from the level of single cells and synapses through the well-established techniques of whole-cell recording and calcium imaging. The study of cortical plasticity of neural oscillatory networks remains largely unexplored. Cross-frequency coupling has become an emerging tool to study the underlying mechanisms for synchronization and interaction between local and global processes of cortical networks. The phase of low-frequency oscillations modulates the amplitude of high-frequency oscillations through a phase-amplitude coupling. Recent studies found that gamma-band oscillations associate with critical period plasticity. The existence of such mechanisms in ocular dominance plasticity is yet to be fully demonstrated. In this study, in-vivo electrophysiological methods for recording local field potentials in the primary visual cortex (V1) of anesthetized mice are employed. Our results reveal the mechanisms of neuronal oscillatory activities for the experience-dependent plasticity of developing visual cortical circuits.Fraction and decimal magnitude processing are crucial for mathematic achievement. Previous neuroimaging results showed that fraction and decimal processing activated both overlapping and distinct neural substrates, but temporal dissociations between fraction and decimal processing remained unknown. This event-related potential (ERP) study explored differences in neural activities between magnitude processing of fractions and decimals, by examining the notation effect (fraction vs. decimal) and distance effect (far vs. close) on early components of P1/N1, P2 and N2. Results showed that decimals elicited larger N1 and smaller P1 than fractions at the parietal region. Fractions demonstrated the significant distance effect on fronto-central P2 while decimals showed the distance effect on left anterior N2. ERP results reflect distinct processing of identification and semantic access stages between fractions and decimals. Identification is located at the visual-related region with enhanced perception acuity and identification efficiency for decimals. Semantic access activates the fronto-central region associated with elaborative magnitude manipulation for fractions, while semantic access reflects automatic phonological retrieval for decimals. check details Our findings disintegrate the magnitude processing of fractions and decimals from identification to magnitude processing. It reveals that temporal discrepancies between fraction and decimal magnitude processing appear as early as post-stimulus 100 ms.Electrocardiogram (ECG)-based identification systems have been widely studied in the literature. Usually, an ECG trace needs to be segmented according to the detected R peaks to enable feature extraction from the ECGs of duration equal to nearly one cardiac cycle. Beat averaging should also be applied to reduce the influence of inter-beat variation on the extracted features and identification accuracy. Either detecting R peaks or collecting extra heartbeats for averaging will inevitably lead to a delay in the identification process. This paper proposes a deep learning-based ECG biometric identification scheme that allows identity recognition using a random ECG segment without needing R-peak detection and beat averaging. Moreover, the problem of being vulnerable to unregistered subjects in an identification system is also addressed. Experimental results demonstrated that an identification rate of 99.1% for an identification system having 235 enrollees with an equal error rate of 8.08% was achieved.Heart rate variability (HRV) has been extensively investigated as a noninvasive marker to evaluate the functionality of the autonomic nervous system (ANS). Many studies have provided photoplethysmography (PPG) as a surrogate for electrocardiogram (ECG) signal HRV measurements. Remote PPG (rPPG) has been also investigated for pulse rate variability (PRV) estimation but in controlled conditions. We remotely extracted PRV using a smartphone camera for subjects in static and lateral motion while their respiratory rate was set to three breathing rates in an indoor illumination environment. PRV was compared with ECG-based HRV as a gold standard. We tested our algorithms on five healthy subjects. The results showed high correlation for rPPG-based HRV by presenting means of standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive heartbeat interval differences (RMSSD) correlation coefficient greater than 0.95 in rest and greater than 0.87 in motion. The error of mean low frequency over high frequency (LF/HF) ratio estimated from PRV was 0.13 in rest and 0.25 in lateral motion. Moreover, a statistically significant correlation was obtained between HRV and PRV power spectra and temporal signals for all performed tasks. The obtained results contributed to confirm that remote imaging measurement of cardiac parameters is a promising, convenient, and low-cost alternative to specialized biomedical sensors in a diversity of relevant experimental maneuver.Large annotated lung sound databases are publicly available and might be used to train algorithms for diagnosis systems. However, it might be a challenge to develop a well-performing algorithm for small non-public data, which have only a few subjects and show differences in recording devices and setup. In this paper, we use transfer learning to tackle the mismatch of the recording setup. This allows us to transfer knowledge from one dataset to another dataset for crackle detection in lung sounds. In particular, a single input convolutional neural network (CNN) model is pre-trained on a source domain using ICBHI 2017, the largest publicly available database of lung sounds. We use log-mel spectrogram features of respiratory cycles of lung sounds. The pre-trained network is used to build a multi-input CNN model, which shares the same network architecture for respiratory cycles and their corresponding respiratory phases. The multi-input model is then fine-tuned on the target domain of our self-collected lung sound database for classifying crackles and normal lung sounds. Our experimental results show significant performance improvements of 9.84% (absolute) in F-score on the target domain using the multi-input CNN model and transfer learning for crackle detection.Clinical relevance- Crackle detection in lung sounds, multi-input convolutional neural networks, transfer learning.Patients undergoing mechanical lung ventilation are at risk of lung injury. A noninvasive bedside lung monitor may benefit these patients. The Inspired Sinewave Test (IST) can measure cardio-pulmonary parameters noninvasively. We propose a lung simulation to improve the measurement of pulmonary blood flow using IST. The new method was applied to 12 pigs' data before lung injury (control) and after lung injury (ARDS model). Results using the lung simulation shown improvements in correlation in both simulated data (R2 increased from 0.98 to 1) and pigs' data (R2 increased from less then 0.001 to 0.26). Paired blood flow measurements were performed by both the IST (noninvasive) and thermodilution (invasive). In the control group, the bias of the two methods was negligible (0.02L/min), and the limit of agreement was from -1.20 to 1.18 L/min. The bias was -0.68 L/min in the ARDS group and with a broader limit of agreement (-2.49 to 1.13 L/min).Clinical Relevance- the inspired sinewave test can be used to measure cardiac output noninvasively in mechanically ventilated subjects with and without acute respiratory distress syndrome.The cyclical and progressively decreasing dynamics of electroencephalogram (EEG) based slow-wave activity (SWA) during sleep reflects the homeostatic component of sleep-wake regulation. The dynamic changes of heart rate (HR) and heart rate variability (HRV) indices during sleep also exhibit quasi-cyclic trends that appear to correlate with SWA. This article proposes a model to characterize the relationship between SWA, HR and HRV in the polar-coordinate (r-θ) domain. Polar coordinates are particularly well-suited to model cyclic shapes with simple (linear) equations in the r-θ plane. Group-level analyses and individual-level ones of the correlations between the polar-coordinate transformations of SWA and HR reveal R2 values of 0.99 and 0.95 respectively. Given that, HR and HRV can be estimated in less obtrusive ways compared to EEG. This research offers relevant options to conveniently monitor sleep SWA.Clinical Relevance- Slow wave activity is a marker of sleep restoration that most prominently manifests in the EEG.
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