Search

Robot-assisted bilateral arm training has demonstrated its effectiveness in improving motor function in individuals post-stroke, showing significant enhancements with increased repetitions. However, prolonged training sessions may lead to both mental and muscle fatigue. We conducted two types of robot-assisted bimanual wrist exercises on 16 healthy adults, separated by one week: long-duration, low-resistance workouts and short-duration, high-resistance exercises. Various measures, including surface electromyograms, near-infrared spectroscopy, heart rate, and the Borg Rating of Perceived Exertion scale, were employed to assess fatigue levels and the impacts of exercise intensity. High-resistance exercise resulted in a more pronounced decline in electromyogram median frequency and recruited a greater amount of hemoglobin, indicating increased muscle fatigue and a higher metabolic demand to cope with the intensified workload. Additionally, high-resistance exercise led to increased sympathetic activation and a greater sense of exertion. Conversely, engaging in low-resistance exercises proved beneficial for reducing post-exercise muscle stiffness and enhancing muscle elasticity. Choosing a low-resistance setting for robot-assisted wrist movements offers advantages by alleviating mental and physiological loads. The reduced training intensity can be further optimized by enabling extended exercise periods while maintaining an approximate dosage compared to high-resistance exercises. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convolutions can produce discernible features from ECG through machine learning. Phase space reconstruction (PSR), using a time delay technique, is one of the transformations from ECG to a feature map, without the need of exact R-peak alignment. However, the effects of time delay and grid partition on identification performance have not been investigated. In this study, we developed a PSR-based CNN for ECG biometric authentication and examined the aforementioned effects. Based on a population of 115 subjects selected from the PTB Diagnostic ECG Database, a higher identification accuracy was achieved when the time delay was set from 20 to 28 ms, since it produced a well phase-space expansion of P, QRS, and T waves. A higher accuracy was also achieved when a high-density grid partition was used, since it produced a fine-detail phase-space trajectory. The use of a scaled-down network for PSR over a low-density grid with 32 × 32 partitions achieved a comparable accuracy with using a large-scale network for PSR over 256 × 256 partitions, but it had the benefit of reductions in network size and training time by 10 and 5 folds, respectively. [ABSTRACT FROM AUTHOR]

Fall detection and physical activity (PA) classification are important health maintenance issues for the elderly and people with mobility dysfunctions. The literature review showed that most studies concerning fall detection and PA classification addressed these issues individually, and many were based on inertial sensing from the trunk and upper extremities. While shoes are common footwear in daily off-bed activities, most of the aforementioned studies did not focus much on shoe-based measurements. In this paper, we propose a novel footwear approach to detect falls and classify various types of PAs based on a convolutional neural network and recurrent neural network hybrid. The footwear-based detections using deep-learning technology were demonstrated to be efficient based on the data collected from 32 participants, each performing simulated falls and various types of PAs: fall detection with inertial measures had a higher F1-score than detection using foot pressures; the detections of dynamic PAs (jump, jog, walks) had higher F1-scores while using inertial measures, whereas the detections of static PAs (sit, stand) had higher F1-scores while using foot pressures; the combination of foot pressures and inertial measures was most efficient in detecting fall, static, and dynamic PAs. [ABSTRACT FROM AUTHOR]

Abstract: Most electrocardiogram (ECG) biometrics are based on detection of the QRS wave and comparison of structural features. ECG parameters are extracted from the waveform, but the process is arduous for noisy signals. Comparison based on phase space trajectory from a cardiac cycle as well as waveform comparison avoids the detection of ECG characteristic points, but has an alignment-free advantage. In this paper, we develop a QRS detection-free ECG biometric based on the phase space trajectory of the ECG signal. The multi-loop trajectory from a 5-s ECG epoch is condensed to a single-loop coarse-grained structure. The normalized spatial correlation (nSC), the mutual nearest point match (MNPM), and the mutual nearest point distance (MNPD) are considered as means of quantifying the similarity or dissimilarity between coarse-grained structures. We test our method on a population of 100 subjects. The accuracies of personal identification achieved for a single-lead ECG are 96%, 95%, and 96% for the MNPD, nSC, and MNPM methods respectively. When we analyze the phase space trajectory of a three-lead ECG, the accuracies increase to 99%, 98%, and 98% respectively. The coarse-grained phase space trajectory of an ECG signal is unambiguous and easy to compute, rendering ECGs a practical alternative to other biometrics. [Copyright &y& Elsevier]

Abstract: Alzheimer''s disease (AD) is a neurodegenerative disease, usually diagnosed by neuropsychological tests, and excluded from other cerebral diseases by brain images. An electroencephalogram (EEG) provides a means of disclosing the reduced functional couplings between brain regions that occurs with AD. In the present study, 16 probable AD patients and 15 age-matched, gender-matched normal subjects were enrolled. Spectral coherence and cross mutual information (CMI) were used to analyze EEGs during intermittent photic stimulation (PS). Ocular- and heartbeat-related source components (SCs) obtained from multi-channel EEGs by the independent component analysis were discarded, and the photic-related SCs were reduced using a comb filter. The undisturbed SCs and photic-related SCs before and after photic reduction were used to reconstruct photic-preserved EEGs and photic-reduced EEGs, from which harmonic coherences (direct photic-driving response) and rhythmic coherences and CMI (indirect photic affection) were computed, respectively. Our results indicate that the rhythmic coherences (particularly in the alpha and beta bands) and CMI variables as well as the harmonic coherences (particularly related to 3-Hz PS) were significantly lower in the probable AD than in normal subjects, whereas the variables derived from the resting EEGs were not statistically significant. This finding implied that the variables obtained during PS could be used to disclose impaired intra-brain associations in probable AD. [Copyright &y& Elsevier]

Abstract: The aim of this research is to study bundle branch block (BBB)-related patterns of radial strain in the left ventricles of patients with heart failure by speckle-tracking echocardiography. Twenty-seven left-BBB (LBBB), 10 right-BBB (RBBB), and 11 narrow QRS-complexes (non-BBB) patients and 11 healthy subjects were assessed. Strain fractions used to quantify thickening-during-systole and thinning-during-diastole, and timing parameters defined as time to onset-of-thickening and peak-strain were measured. Principal strain vectors were conducted on the fractions and parameters to analyze mechanical discoordination and dyssynchrony. Heart failure patients show a significantly greater extent of discoordination and dyssynchrony compared with healthy subjects. Significant differences between the LBBB and RBBB groups are demonstrated by deflection, a spatial characteristic of myocardial coordination. New information provided by these findings can provide a better understanding of BBB-related mechanisms of myocardial coordination and may be useful in improving patient selection, electrode placement and subsequent outcomes for cardiac resynchronization therapy. (E-mail: chanhl@mail.cgu.edu.tw) [Copyright &y& Elsevier]

Abstract: The electrophysiological studies of thalamocortical oscillations were mostly done in animal models. Placement of stimulation electrodes at the anterior nucleus of the thalamus (ANT) for seizure reduction enables the study of the thalamocortical interplay in human subjects. Nocturnal sleep electroencephalograms (EEGs) and local field potentials (LFPs) of the left and right thalamus (LT, RT) were recorded in three subjects receiving ANT stimulation. Sleep stages were scored according to American Academy of Sleep Medicine criteria. The whole-night time–frequency coherence maps between EEG (C3, C4) and LFP (LT, RT) showed specific coherence patterns during non-rapid eye movement (NREM) sleep. Pooled coherence in the NREM stage was significant in slow, delta, theta and spindle frequency ranges. The spindle oscillations had the highest coherence (0.17–0.58) in the homolateral hemisphere. Together, these observations indicate that the oscillations were related to thalamocortical circuitry. [ABSTRACT FROM AUTHOR]

Abstract: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been used to alleviate symptoms of Parkinson''s disease. During image-guided stereotactic surgery, signals from microelectrode recordings are used to distinguish the STN from adjacent areas, particularly from the substantia nigra pars reticulata (SNr). Neuronal firing patterns based on interspike intervals (ISI) are commonly used. In the present study, arrival time-based measures, including Lempel-Ziv complexity and deviation-from-Poisson index were employed. Our results revealed significant differences in the arrival time-based measures among non-motor STN, motor STN and SNr and better discrimination than the ISI-based measures. The larger deviations from the Poisson process in the SNr implied less complex dynamics of neuronal discharges. If spike classification was not used, the arrival time-based measures still produced statistical differences among STN subdivisions and SNr, but the ISI-based measures only showed significant differences between motor and non-motor STN. Arrival time-based measures are less affected by spike misclassifications, and may be used as an adjunct for the identification of the STN during microelectrode targeting. [Copyright &y& Elsevier]

Abstract: Spike information is beneficial for correlating neuronal activity to various stimuli, finding target neural areas for deep brain stimulation, and decoding intended motor command for brain–machine interface. Unsupervised classification based on spike features provides a way to separate spikes generated from different neurons. Here, we propose an unsupervised spike sorting method based on specific wavelet coefficients (SWC) and using both a new spike alignment technique based on multi-peak energy comparison (MPEC) and a dynamic codebook-based template-matching algorithm with a class-merging feature. The MPEC alignment reduced inconsistent alignment caused by spike deformation. Using SWC not only reduced the number of features but also performed better in terms of matching a neuronal spike to its own class than relying on spike waveform or whole wavelet coefficients. Moreover, the employed codebook searching and replenishment can be operated in an online, real-time mode. [Copyright &y& Elsevier]

Abstract: Specific patterns of electrocardiogram (ECG), along with other biometrics, have recently been used to recognize a person. Most ECG-based human identification methods rely on the reduced features derived from ECG characteristic points and supervised classification. However, detecting characteristic points is an arduous procedure, particularly at low signal-to-noise ratios. The supervised classifier requires retraining when a new person is included in the group. In the present study, we propose a novel unsupervised ECG-based identification method based on phase space reconstruction of one-lead or three-lead ECG, saving from picking up characteristic points. Identification is performed by inspecting similarity or dissimilarity measure between ECG phase space portraits. Our results in a 100-subject group showed that one-lead ECG reached identification rate at 93% accuracy and three-lead ECG acquired 99% accuracy. [Copyright &y& Elsevier]

Muscle co-contraction during the execution of motor tasks or training is common in poststroke subjects. EMG-derived muscular activation indexes have been used to evaluate muscle co-contractions during movements. In addition, robot-assisted bilateral arm training provides a repetitive and stable training method to improve arm movements. However, quantitative measures of muscle contractions during this training in poststroke subjects have not been described. Seventeen subjects experiencing spastic hemiplegia after a stroke were recruited to perform robot-assisted bilateral wrist flexion and extension movements. The co-contraction index and two new indexes, temporal correlation and cross mutual information, which are derived from the EMGs of working muscles without the need for envelope normalization, are used to quantify intermuscular activation during wrist movements. Higher temporal correlation as well as higher co-contraction index was demonstrated in the affected muscles, implying the recruitment of muscle co-contractions to complete the movement task. On the other hand, a higher value of cross mutual information was exhibited in the unaffected muscles which was attributed to their distinct, rhythmic muscle contractions. The plot of temporal correlation versus cross mutual information further defined affected, unaffected synergistic, and unaffected agonist-antagonist muscular regions. Moreover, with the modified Ashworth scale, multiple regression models based on the co-contraction index and cross mutual information had the highest R-squared value of 0.733. EMG-derived intermuscular activation parameters demonstrated muscle co-contractions in the affected muscles and different types of intermuscular contractions during robot-assisted bilateral arm training. The modified Ashworth scale estimation based on multiple regression analysis of the activation indexes also demonstrated EMG-derived index a valuable method for assessing muscle spasticity in subjects with poststroke hemiplegia. • Electromyogram-derived index to quantify intermuscular activation. • Muscle contraction in robot-assisted bilateral wrist task in poststroke hemiplegia. • Estimation of modified Ashworth scale using intermuscular activation indexes. [ABSTRACT FROM AUTHOR]

Abstract: Neuronal spike information can be used to correlate neuronal activity to various stimuli, to find target neural areas for deep brain stimulation, and to decode intended motor command for brain–machine interface. Typically, spike detection is performed based on the adaptive thresholds determined by running root-mean-square (RMS) value of the signal. Yet conventional detection methods are susceptible to threshold fluctuations caused by neuronal spike intensity. In the present study we propose a novel adaptive threshold based on the max–min spread sorting method. On the basis of microelectrode recording signals and simulated signals with Gaussian noises and colored noises, the novel method had the smallest threshold variations, and similar or better spike detection performance than either the RMS-based method or other improved methods. Moreover, the detection method described in this paper uses the reduced features of raw signal to determine the threshold, thereby giving a simple data manipulation that is beneficial for reducing the computational load when dealing with very large amounts of data (as multi-electrode recordings). [Copyright &y& Elsevier]

Abstract: Efficient electrocardiogram (ECG) compression can reduce the payload of real-time ECG transmission as well as reduce the amount of data storage in long-term ECG recording. In this paper an ECG compression/decompression architecture based on the bit-field preserving (BFP) and running length encoding (RLE)/decoding schemes incorporated with the discrete wavelet transform (DWT) is proposed. Compared to complex and repetitive manipulations in the set partitioning in hierarchical tree (SPIHT) coding and the vector quantization (VQ), the proposed algorithm has advantages of simple manipulations and a feedforward structure that would be suitable to implement on very-large-scale integrated circuits and general microcontrollers. [Copyright &y& Elsevier]

Abstract: Spike information is beneficial to correlate neuronal activity to various stimuli or determine target neural area for deep brain stimulation. Data clustering based on neuronal spike features provides a way to separate spikes generated from different neurons. Nevertheless, some spikes are aligned incorrectly due to spike deformation or noise interference, thereby reducing the accuracy of spike classification. In the present study, we proposed unsupervised spike classification over the reconstructed phase spaces of neuronal spikes in which the derived phase space portraits are less affected by alignment deviations. Principal component analysis was used to extract major principal components of the portrait features and k-means clustering was used to distribute neuronal spikes into various clusters. Finally, similar clusters were iteratively merged based upon inter-cluster portrait differences. [Copyright &y& Elsevier]

Heart rate (HR) variability derived from electrocardiogram (ECG) can be used to assess the function of the autonomic nervous system. HR exhibits various characteristics during different physical activities attributed to the altered autonomic mediation, where it is also beneficial to reveal the autonomic shift in response to physical-activity change. In this paper, the physical-activity-related HR behaviors were delineated using a portable ECG and body acceleration recorder based on a personal digital assistant and the smoothed pseudo Wigner–Ville distribution. The results based upon eighteen subjects performing four sequential 5-min physical activities (supine, sitting, standing and spontaneous walking) showed that the high-frequency heartbeat fluctuations during supine and sitting were significantly larger than during standing, and that the ratio of low-to high-frequency fluctuation during standing was significantly higher than during supine and sitting. This could be linked with the parasympathetic predominance during supine and sitting, and a shift to sympathetic dominance while standing. During spontaneous walking, the high-frequency fluctuation was significant lower than during supine. The low-to high-frequency ratio decreased significantly from standing to spontaneous walking, which may imply an increased vagal predominance (autonomic effect) or an increased respiratory activity (mechanical effect). [Copyright &y& Elsevier]

Abstract: Introduction: Human heart beats in an extremely inhomogeneous and non-stationary manner. Recent study has demonstrated that it belongs to a class of signals called multifractal. Loss of multifractality was also uncovered in patients with congestive heart failure. We investigated whether carvedilol could restore the multifractal properties in a group of patients with advanced congestive heart failure. Methods and results: A Holter ECG recording was obtained before and 1 and 3 months after titrated addition of carvedilol therapy in 10 patients with advanced congestive heart failure. Multifractal spectrum, detrended fluctuation analysis (DFA) as well as the traditional time- and frequency-domain heart rate variability (HRV) parameters were compared before and after carvedilol therapy together with those in age and sex-matched normal control. The results showed that the multifractal spectrum τ(q) vs. q of N–N interval time series returned toward normal during carvedilol treatment. All the tranditional HRV parameters and the short-term DFA improved significantly after 3 months of carvedilol therapy. Conclusion: We concluded that the deteriorated multifractal properties could be reversed by carvedilol treatment in patients with advanced congestive heart failure. [Copyright &y& Elsevier]

Describes a technique for interference-term suppression in the Wigner-Ville distribution (WVD). Assumptions of the technique; How a signal with a spectrum shape is used; Spectral characteristic of the signal.

Abstract: Purpose: Predicting response after cardiac resynchronization therapy (CRT) has been a challenge of cardiologists. About 30% of selected patients based on the standard selection criteria for CRT do not show response after receiving the treatment. This study is aimed to build an intelligent classifier to assist in identifying potential CRT responders by speckle-tracking radial strain based on echocardiograms. Methods and materials: The echocardiograms analyzed were acquired before CRT from 26 patients who have received CRT. Sequential forward selection was performed on the parameters obtained by peak-strain timing and phase space reconstruction on speckle-tracking radial strain to find an optimal set of features for creating intelligent classifiers. Support vector machine (SVM) with a linear, quadratic, and polynominal kernel were tested to build classifiers to identify potential responders and non-responders for CRT by selected features. Results: Based on random sub-sampling validation, the best classification performance is correct rate about 95% with 96–97% sensitivity and 93–94% specificity achieved by applying SVM with a quadratic kernel on a set of 3 parameters. The selected 3 parameters contain both indexes extracted by peak-strain timing and phase space reconstruction. Conclusions: An intelligent classifier with an averaged correct rate, sensitivity and specificity above 90% for assisting in identifying CRT responders is built by speckle-tracking radial strain. The classifier can be applied to provide objective suggestion for patient selection of CRT. [Copyright &y& Elsevier]

Abstract: Phase space reconstruction, which is performed by converting echocardiogram-derived strain data from different ventricular regions into phase space trajectories, is applied in this study to describe nonlinear behaviour of myocardial coordination. A new method was developed to quantify patterns of phase space trajectories. Echocardiograms of 31 healthy individuals and 63 patients with left bundle branch block (LBBB) and different left ventricular ejection fractions (LVEFs) were used to evaluate this method. The LBBB patients were separated into two groups: LBBB with a LVEF≥50% and LBBB with a LVEF<50%. LVEF is used to represent levels of systolic function and disease severity. A classifying map was constructed to separate the reconstructed phase space into three zones and to acquire the parameters Pz1, Pz2 and Pz3 as percentiles of phase points related to the zones. The criteria used to define the zones were cross-tested. Based on these parameters, significant group-related differences in myocardial coordination were observed. Significantly smaller Pz1 and significantly larger Pz2 values were observed in the healthy group, as compared to the patient group, and similar, significant results were obtained for the patients with LVEF≥50%, as compared to the patients with LVEF<50% (p <.05). A significantly larger Pz3 was observed in patients with LVEF<50%, as compared to the other patients (p <.05). In addition, different inter-regional relationships among strain pairs (all, near-side, middle-side and opposite-wall) were examined to create phase space trajectories. Consistent group-related differences were observed when different inter-regional relationships were applied. Expanding the use of the proposed method to assess various pathological factors and therapeutic impacts is promising. [Copyright &y& Elsevier]

Abstract: Excessive synchronization of basal ganglia neuronal activity at ~20Hz is characteristic of patients with untreated Parkinson''s disease (PD). Correlative evidence suggests that this activity may contribute to bradykinesia. Attempts to demonstrate causality through stimulation imposed synchronization at 20Hz in the region of the subthalamic nucleus (STN) have had limited success. Finger-tapping is slowed by about 8% and only in those PD patients that have a relatively normal baseline performance in this task. Here we investigate whether greater performance decrements might be seen in a reaction time grip task. We studied 32 sides in 16 patients with PD after overnight withdrawal of medication. Patients were asked to grip as hard and as fast as possible without STN stimulation and during bilateral stimulation at 5Hz, 10Hz, 20Hz, 50Hz and 130Hz. Stimulation at 20Hz slowed the development of force by 14.7±8.3% (P =0.044) across all patients. Slowing increased by 22±7% (P =0.005) in those patients with the best performance in the task without stimulation. The effect was frequency specific. These data provide direct interventional evidence of a mechanistic link between excessive neuronal synchronization in the beta range and motor impairment in PD. [Copyright &y& Elsevier]

Abstract: Excessive synchronization of the basal ganglia neuronal activity in the 13- to 35-Hz frequency band, so-called beta activity, has been associated with the motor deficits of Parkinson''s disease (PD). Studies have demonstrated that beta activity may be suppressed by treatment with dopaminergic medication and high-frequency stimulation of the subthalamic nucleus (STN), with the degree of suppression correlating with clinical improvement. However, these studies failed to demonstrate any correlation between beta activity of parkinsonism in the resting, untreated state. This argues against a significant relationship between beta activity and motor impairment. Here we use an advanced nonlinear dynamical analysis method based on the Lempel-Ziv estimator to show frequency band and symptom–subset specific correlations between STN local field potential (LFP) complexity and motor impairment in PD patients. Oscillatory activity has a reduced complexity, and we found a strong negative correlation between the complexity of the STN LFP over the 13- to 35-Hz frequency range and akinesia–rigidity. There was no such correlation with tremor. Furthermore, there was no correlation between LFP Lempel-Ziv complexity (LZC) over the 0- to 12-Hz frequency band and any parkinsonian motor impairment. The results strengthen the association between the dynamic structure of synchonised (LFP) activity in the beta frequency band in the STN and akinesia–rigidity. [Copyright &y& Elsevier]

The measurement of anesthetic depth is important in anesthesiology. Although heart rate variability (HRV) is profoundly affected by general anesthesia, it has not yet been commonly used in this field. One of the reasons is the lack of suitable parameters of HRV for short-term observations. In this study, we designed a time domain parameter of HRV named the similarity index. It was based on observing the trend of the distribution of instantaneous heart rates as time moved. Taking epochs of ECG data as short as 64 s can derive the index. We observed the values of this index of 30 patients when they were awake and under isoflurane anesthesia. The values had very little overlapping between the two states and the prediction probability to distinguish the two states was 0.91. We suggest that HRV, if suitably treated, can play more roles in the monitoring of anesthetic depth. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a simple moving average-based computing method for real-time QRS detection is proposed. In addition, for signal preprocessing our detection algorithm also incorporates a wavelet-based denoising procedure to effectively reduce the noise level for electrocardiogram (ECG) data. The overall computational structure of the proposed algorithm allows the QRS detection to be performed and implemented in real-time with high time- and memory-efficiency. Algorithm performance was evaluated against the MIT-BIH Arrhythmia Database. The numerical results indicated that the novel algorithm finally achieved about 99.5% of the detection rate for the standard database, and also, it could function reliably even under the condition of poor signal quality in the measured ECG data. [Copyright &y& Elsevier]

Freezing of gait (FOG) is a disabling clinical phenomenon often found in patients with advanced Parkinson's disease (PD). FOG impairs motor function, causes falls and leads to loss of independence. Whereas dual tasking that distracts patients' attention precipitates FOG, auditory or visual cues ameliorate this phenomenon. The pathophysiology of FOG remains unclear. Previous studies suggest that the basal ganglia are involved in the generation of FOG. Investigation of the modulation of neuronal activities within basal ganglia structures during walking is warranted. To this end, we recorded local field potentials (LFP) from the subthalamic nucleus (STN) while PD patients performed single-task gait (ST) or walked while dual-tasking (DT). An index of FOG (iFOG) derived from trunk accelerometry was used as an objective measure to differentiate FOG-vulnerable gait from normal gait. Two spectral activities recorded from the STN region were associated with vulnerability to freezing. Greater LFP power in the low beta (15–21 Hz) and theta (5–8 Hz) bands were noted during periods of vulnerable gait in both ST and DT states. Whereas the elevation of low beta activities was distributed across STN, the increase in theta activity was focal and found in ventral STN and/or substantia nigra (SNr) in ST. The results demonstrate that low beta and theta band oscillations within the STN area occur during gait susceptible to freezing in PD. They also add to the evidence that narrow band ~18 Hz activity may be linked to FOG. • Narrow band beta ~18 Hz oscillations in subthalamic nucleus were found while FOG was more likely. • Two spectral activities in STN region (15–21 Hz and 5–8 Hz) were associated with FOG. • These results may inform the development of can be used for the adaptive DBS for FOG. [ABSTRACT FROM AUTHOR]

Introduction: The purpose was to investigate the age effects on central versus peripheral sources of strength, fatigue, and central neural excitabilities.Methods: 42 healthy subjects were recruited as young group (23.73 ± 2.15 years; n = 26) and middle-aged group (57.25 ± 4.57 years; n = 16). Maximum voluntary contraction force (MVC), voluntary activation level (VA), and twitch force of quadriceps were evaluated to represent general, central, and peripheral strengths. Central and peripheral fatigue indexes were evaluated using femoral nerve electrical stimulation. Cortical excitabilities were evaluated using transcranial magnetic stimulation (TMS).Results: The middle-aged group had lower MVC and twitch force of quadriceps, but not VA, than young group. No between group differences were found in fatigue indexes. The cortical excitability in middle-aged group was different from young group in paired TMS with inter-stimulus interval of 7 ms.Conclusion: The age-related strength loss at early stage was primarily caused by peripheral muscular strength. The deviation of central neural excitability can be detected but the activation level was not impaired in middle-age adults. [ABSTRACT FROM AUTHOR]

Introduction Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective treatment for advanced Parkinson’s disease (PD). Two electrophysiological techniques aid identification of the optimal target. One is to record neuronal activity with microelectrodes (MER). The other is to record the local field potential (LFP) from the DBS electrode used for chronic stimulation. The relative predictive value of the two techniques is yet to be established. Methods We combined MER and LFP recordings during STN electrode implantation in 18 PD patients. The LFP was recorded from the DBS electrode as it descended in 1 mm steps through the STN. The upper border of the STN identified by MER and the depth of the highest beta peak (HBP) recorded by the DBS electrode were correlated with the depth of chronic stimulation and with stimulation efficacy determined later. Results The HBP in the LFP recorded with the DBS electrode predicted the optimal contact for chronic stimulation slightly better than the upper border of the STN identified by MER, and the prediction remained after accounting for the depth of the upper border of the STN identified by MER. In addition, only the difference in depth between the HBP and the site of chronic stimulation significantly predicted the therapeutic efficacy of DBS with respect to contralateral bradykinesia-rigidity hemibody scores. Conclusion Our results confirm the predictive value of the DBS electrode recorded LFP and strengthen the argument that LFP patterns may help aid DBS targeting and programming. [ABSTRACT FROM AUTHOR]

In this study, we defined a new parameter, referred to as the cardiac stress index (CSI), using a nonlinear detrended fluctuation analysis (DFA) of heart rate (HR). Our study aimed to incorporate the CSI into a cycling based fatigue monitoring system developed in our previous work so the muscle fatigue and cardiac stress can be both continuously and quantitatively assessed for subjects undergoing the cycling exercise. By collecting electrocardiogram (ECG) signals, the DFA scaling exponent α was evaluated on the RR time series extracted from a windowed ECG segment. We then obtained the running estimate of α by shifting a one-minute window by a step of 20 seconds so the CSI, defined as the percentage of all the less-than-one α values, can be synchronously updated every 20 seconds. Since the rating of perceived exertion (RPE) scale is considered as a convenient index which is commonly used to monitor subjective perceived exercise intensity, we then related the Borg RPE scale value to the CSI in order to investigate and quantitatively characterize the relationship between exercise-induced fatigue and cardiac stress. Twenty-two young healthy participants were recruited in our study. Each participant was asked to maintain a fixed pedaling speed at a constant load during the cycling exercise. Experimental results showed that a decrease in DFA scaling exponent α or an increase in CSI was observed during the exercise. In addition, the Borg RPE scale and CSI were positively correlated, suggesting that the factors due to cardiac stress might also contribute to fatigue state during physical exercise. Since the CSI can effectively quantify the cardiac stress status during physical exercise, our system may be used in sports medicine, or used by cardiologists who carried out stress tests for monitoring heart condition in patients with heart diseases. [ABSTRACT FROM AUTHOR]

Heart Rate Turbulence in Congestive Heart Failure . Introduction: Heart rate turbulence (HRT) is a powerful novel predictor for cardiovascular mortality. Chronic congestive heart failure is associated with abnormal HRT. Whether antiadrenergic beta-blocker therapy can restore control of HRT in patients with chronic congestive heart failure is unknown. Methods and Results: A 24-hour Holter ECG recording was obtained before and 1 and 3 months after titrated addition of atenolol therapy in 10 consecutive patients with advanced congestive heart failure. Two parameters derived from HRT, turbulence slope (TS) and turbulence onset (TO), and time- and frequency-domain heart rate variability (HRV) parameters (SDNN, RMSSD, VLF, LF, HF) from 24-hour ECG were compared before and after beta-blocker therapy, together with the same parameters in age-matched normal control. Results showed that TS (3.1 ± 2.2 vs 6.2 ± 3.0; P = 0.001) and all HRV parameters were increased after 3 months of atenolol treatment. No changes in TO were evident (0.6 ± 0.5 vs −0.2 ± 1.3; P = 0.13). The improvement of TS and the vagally mediated parameters of mean R-R interval, RMSSD, and the HF component of HRV were positively correlated. Conclusion: Abnormal HRT caused by chronic congestive heart failure can be restored by beta-blocker therapy. The evolution of TS was positively correlated with measures of vagal modulation of heart rate. (J Cardiovasc Electrophysiol, Vol. 15, pp. 752-756, July 2004) [ABSTRACT FROM AUTHOR]

Background: Action semantics have been investigated in relation to context violation but remain less examined in relation to the meaning of gestures. In the present study, we examined tool–gesture incongruity by event-related potentials (ERPs) and hypothesized that the component N400, a neural index which has been widely used in both linguistic and action semantic congruence, is significant for conditions of incongruence. Methods: Twenty participants performed a tool–gesture judgment task, in which they were asked to judge whether the tool–gesture pairs were correct or incorrect, for the purpose of conveying functional expression of the tools. Online electroencephalograms and behavioral performances (the accuracy rate and reaction time) were recorded. Results: The ERP analysis showed a left centro-parieto-temporal N300 effect (220–360 ms) for the correct condition. However, the expected N400 (400–550 ms) could not be differentiated between correct/incorrect conditions. After 700 ms, a prominent late negative complex for the correct condition was also found in the left centro-parieto-temporal area. Conclusions: The neurophysiological findings indicated that the left centro-parieto-temporal area is the predominant region contributing to neural processing for tool–gesture incongruity in right-handers. The temporal dynamics of tool–gesture incongruity are: (1) firstly enhanced for recognizable tool–gesture using patterns, (2) and require a secondary reanalysis for further examination of the highly complicated visual structures of gestures and tools. The evidence from the tool–gesture incongruity indicated altered brain activities attributable to the N400 in relation to lexical and action semantics. The online interaction between gesture and tool processing provided minimal context violation or anticipation effect, which may explain the missing N400. [ABSTRACT FROM AUTHOR]