Freezing of gait and fall detection in Parkinson’s disease using wearable sensors:a systematic review

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson’s disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73–100% for sensitivity and 67–100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets

Estimating economies of scale and scope with flexible technology

The final publication is available at Springer via http://dx.doi.org/10.1007/s11123-016-0467-1Economies of scope are typically modelled and estimated using a cost function that is common to all firms in an industry irrespective of their type, e.g. whether they specialize in a single output or produce multiple outputs. Instead, we estimate a flexible technology model that allows for type-specific technologies and show how it can be estimated using linear parametric forms including the translog. A common technology remains a special case of our model and is testable econometrically. Our sample, of publicly owned US electric utilities, does not support a common technology for integrated and specialized firms. Our empirical results therefore suggest that assuming a common technology might bias estimates of economies of scale and scope. Thus, how we model the production technology clearly influences the policy conclusions we draw from its characteristics

Rational Cost Inefficiency in Chinese Banks

According to a frequently cited finding by Berger et al (1993), X-inefficiency contributes 20% to cost-inefficiency in western banks. Empirical studies of Chinese banks tend to place cost-inefficiency in the region of 50%. Such estimates would suggest that Chinese banks suffer from gross cost inefficiency. Using a nonparametric bootstrapping method, this study decomposes cost-inefficiency in Chinese banks into X-inefficiency and allocative-inefficiency. It argues that allocative inefficiency is the optimal outcome of input resource allocation subject to enforced employment constraints. The resulting analysis suggests that allowing for rational allocative inefficiency; Chinese banks are no better or worse than their western counterparts

Comparative analysis of temporal dynamics of EEG and phase synchronization of EEG to localize epileptic sites from high density scalp EEG interictal recordings

Our objective was to examine if the high-density, 256 channel, scalp interictal EEG data can be used for localizing the epilepsy areas in patients. This was done by examining the long-range temporal correlations (LRTC) of EEGs and also that of the phase synchronization index (SI) of EEGs. It was found that the LRTC of scalp SI plots were better in localizing the seizure areas as compared with the LRTC of EEGs alone. The EEG data of one minute duration was filtered in the low Gamma band of 30-50 Hz. A detrended fluctuation analysis (DFA) was used to find LRTC of the scalp EEG data. Contour plots were constructed using a montage of the layout of 256 electrode positions. The SI was computed after taking Hilbert transform of the EEG data. The SI between a pair of channel was inferred from a statistical tendency to maintain a nearly constant phase difference over a given period of time even though the analytic phase of each channel may change markedly during that time frame. The SI for each electrode was averaged over with the nearby six electrodes. LRTC of the SI was computed and spatial plots were made. It was found that the LRTC of SI was highest at the location of the epileptic sites. A similar pattern was not found in the LRTC of EEGs. This provides a noninvasive way to localize seizure areas from scalp EEG data

Comparative analysis of temporal dynamics of EEG and phase synchronization of EEG to localize epileptic sites from high density scalp EEG interictal recordings.

Our objective was to examine if the high-density, 256 channel, scalp interictal EEG data can be used for localizing the epilepsy areas in patients. This was done by examining the long-range temporal correlations (LRTC) of EEGs and also that of the phase synchronization index (SI) of EEGs. It was found that the LRTC of scalp SI plots were better in localizing the seizure areas as compared with the LRTC of EEGs alone. The EEG data of one minute duration was filtered in the low Gamma band of 30-50 Hz. A detrended fluctuation analysis (DFA) was used to find LRTC of the scalp EEG data. Contour plots were constructed using a montage of the layout of 256 electrode positions. The SI was computed after taking Hilbert transform of the EEG data. The SI between a pair of channel was inferred from a statistical tendency to maintain a nearly constant phase difference over a given period of time even though the analytic phase of each channel may change markedly during that time frame. The SI for each electrode was averaged over with the nearby six electrodes. LRTC of the SI was computed and spatial plots were made. It was found that the LRTC of SI was highest at the location of the epileptic sites. A similar pattern was not found in the LRTC of EEGs. This provides a noninvasive way to localize seizure areas from scalp EEG data