Early identification of declining balance in higher functioning older adults, an inertial sensor based method.

Higher functioning older adults rarely have their balance assessed clinically and as such early decline in balance is not captured. Early identification of declining balance would facilitate earlier intervention and improved management of the ageing process. This study sought to determine if (a) a once off inertial sensor measurement and (b) changes in inertial sensor measurements one year apart can identify declining balance for higher functioning older adults. One hundred and nineteen community dwelling older adults (58 males; 72.5±5.8 years) completed a timed up and go (TUG) instrumented with inertial sensors and the Berg balance scale (BBS) at two time points, one year apart. Temporal and spatio-temporal gait parameters as well as angular velocity and turn parameters were derived from the inertial sensor data. A change in balance from baseline to follow-up was determined by sub-components of the BBS. Changes in inertial sensor parameters from baseline to follow-up demonstrated strong association with balance decline in higher functioning older adults (e.g. mean medial-lateral angular velocity odds ratio=0.2; 95% CI: 0.1-0.5). The area under the Receiver operating characteristic curve (AUC) ranged from 0.8 to 0.9, a marked improvement over change in TUG time alone (AUC 0.6-0.7). Baseline inertial sensor parameters had a similar association with declining balance as age and TUG time. For higher functioning older adults, the change in inertial sensor parameters over time may reflect declining balance. These measures may be useful clinically, to monitor the balance status of older adults and facilitate earlier identification of balance deficits.

EEG in the healthy term newborn within 12 hours of birth.

OBJECTIVE: To characterise and quantify the EEG during sleep in healthy newborns in the early newborn period. METHODS: Continuous multi-channel video-EEG data was recorded for up to 2 hours in normal newborns within 12 hours of birth. The total amount of active (AS) and quiet sleep (QS) was calculated in the first hour of recording. The EEG signal was quantitatively analysed for symmetry and synchrony. Spectral edge frequency (SEF), spectral entropy (H) and relative delta power (delta(R)) were calculated for a ten-minute segment of AS and QS in each recording. Paired t-test and Wilcoxon rank sum test were used for data analysis. RESULTS: Thirty normal newborn babies were studied, 10 within 6 hours of birth and 20 between 6 and 12 hours. All babies showed continuous symmetrical and synchronous EEG activity and well-developed sleep-wake cycling (SWC) with the median percentage of AS--48.5% and QS--36.6%. Quantitative EEG analysis of sleep epochs showed that SEF and H were significantly higher (p<0.0001) and delta(R) was significantly lower (p<0.0001) in AS than in QS. CONCLUSION: The normal newborn EEG shows symmetrical and synchronous continuous activity and well-developed SWC as early as within the first 6 hours of birth. Quantitative analysis of the EEG in the early postnatal period reveals differences in SEF, H and delta(R) for AS and QS periods. SIGNIFICANCE: These findings may have implications for quantitative analysis of the newborn EEG, including the EEG of babies with hypoxic ischaemic encephalopathy.

Can state or response entropy be used as a measure of sleep depth?

SUMMARY: In this prospective observational study we examined the potential of the spectral entropy measures 'state' and 'response' entropy (Entropy monitor), as measures of sleep depth in 12 healthy adult subjects. Both median state and response entropy values varied significantly with sleep stage (p = 0.017 and p = 0.014 respectively; ANOVA). Median state or response entropy did not decrease significantly during the transition from awake to stage I sleep (p > 0.017). State entropy values decreased significantly between sleep stages I and II (p < 0.001). Both state and response entropy values were significantly less (40 and 45 arbitrary units respectively) in stage III (slow wave sleep) vs stage II sleep (p = 0.008). We conclude that state and response entropy values, when expressed as a function of time, may be a useful means of quantifying aspects of sleep.

Classifier models and architectures for EEG-based neonatal seizure detection.

Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with a poor long-term outcome. Early detection and treatment may improve prognosis. This paper aims to develop an optimal set of parameters and a comprehensive scheme for patient-independent multi-channel EEG-based neonatal seizure detection. We employed a dataset containing 411 neonatal seizures. The dataset consists of multi-channel EEG recordings with a mean duration of 14.8 h from 17 neonatal patients. Early-integration and late-integration classifier architectures were considered for the combination of information across EEG channels. Three classifier models based on linear discriminants, quadratic discriminants and regularized discriminants were employed. Furthermore, the effect of electrode montage was considered. The best performing seizure detection system was found to be an early integration configuration employing a regularized discriminant classifier model. A referential EEG montage was found to outperform the more standard bipolar electrode montage for automated neonatal seizure detection. A cross-fold validation estimate of the classifier performance for the best performing system yielded 81.03% of seizures correctly detected with a false detection rate of 3.82%. With post-processing, the false detection rate was reduced to 1.30% with 59.49% of seizures correctly detected. These results represent a comprehensive illustration that robust reliable patient-independent neonatal seizure detection is possible using multi-channel EEG.

Behaviour of spectral entropy, spectral edge frequency 90%, and alpha and beta power parameters during low-dose propofol infusion.

BACKGROUND: In this study we analyse the behaviour, potential clinical application and optimal cortical sampling location of the spectral parameters: (i) relative alpha and beta power; (ii) spectral edge frequency 90%; and (iii) spectral entropy as monitors of moderate propofol-induced sedation. METHODS: Multi-channel EEG recorded from 12 ASA 1 (American Society of Anesthesiologists physical status 1) patients during low-dose, target effect-site controlled propofol infusion was used for this analysis. The initial target effect-site concentration was 0.5 microg ml(-1) and increased at 4 min intervals in increments of 0.5 to 2 microg ml(-1). EEG parameters were calculated for 2 s epochs in the frequency ranges 0.5-32 and 0.5-47 Hz. All parameters were calculated in the channels: P4-O2, P3-O1, F4-C4, F3-C3, F3-F4, and Fp1-Fp2. Sedation was assessed clinically using the OAA/S (observer's assessment of alertness/sedation) scale. RESULTS: Relative beta power and spectral entropy increased with increasing propofol effect-site concentration in both the 0.5-47 Hz [F(18, 90) = 3.455, P<0.05 and F(18, 90) = 3.33, P<0.05, respectively] and 0.5-32 Hz frequency range. This effect was significant in each individual channel (P<0.05). No effect was seen of increasing effect-site concentration on relative power in the alpha band. Averaged across all channels, spectral entropy did not outperform relative beta power in either the 0.5-32 Hz [Pk=0.79 vs 0.814 (P>0.05)] or 0.5-47 Hz range [Pk=0.81 vs 0.82 (P>0.05)]. The best performing indicator in any single channel was spectral entropy in the frequency range 0.5-47 Hz in the frontal channel F3-F4 (Pk=0.85). CONCLUSIONS: Relative beta power and spectral entropy when considered over the propofol effect-site range studied here increase in value, and correlate well with clinical assessment of sedation.

A comparison of quantitative EEG features for neonatal seizure detection.

OBJECTIVE: This study was undertaken to identify the best performing quantitative EEG features for neonatal seizures detection from a test set of 21. METHODS: Each feature was evaluated on 1-min, artefact-free segments of seizure and non-seizure neonatal EEG recordings. The potential utility of each feature for neonatal seizure detection was determined using receiver operating characteristic analysis and repeated measures t-tests. A performance estimate of the feature set was obtained using a cross-fold validation and combining all features together into a linear discriminant classifier model. RESULTS: Significant differences between seizure and non-seizure segments were found in 19 features for 17 patients. The best performing features for this application were the RMS amplitude, the line length and the number of local maxima and minima. An estimate of the patient independent classifier performance yielded a sensitivity of 81.08% and specificity of 82.23%. CONCLUSIONS: The individual performances of 21 quantitative EEG features in detecting electrographic seizure in the neonate were compared and numerically quantified. Combining all features together into a classifier model led to superior performance than that provided by any individual feature taken alone. SIGNIFICANCE: The results documented in this study may provide a reference for the optimum quantitative EEG features to use in developing and enhancing neonatal seizure detection algorithms.

Seizure detection in neonates: Improved classification through supervised adaptation.

The goal of neonatal seizure detection is the development of a patient independent system to alert staff in the neonatal intensive care unit of ongoing seizures. This study demonstrates the potential in adapting a patient independent classifier using patient specific data. Supervised adaptation is investigated using the basic gradient descent algorithm and least mean squares procedures. An increase in mean ROC area of 3% is obtained for the best performing learning algorithm, yielding an increase in mean accuracy of 7.7% compared to the patient independent algorithm.

Automated single channel seizure detection in the neonate.

Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with poor long-term outcome. EEG is considered the gold standard for identification of all neonatal seizures, reducing the number of EEG electrodes required would reduce patient handling and allow faster acquisition of data. A method for automated neonatal seizure detection based on two carefully chosen cerebral scalp electrodes but trained using multi-channel EEG is presented. The algorithm was developed and tested using a multi-channel EEG dataset containing 411 seizures from 251.9 hours of EEG recorded from 17 full-term neonates. Automated seizure detection using a variety of bipolar channel derivations was investigated. Channel C3-C4 yielded correct detection of 90.77% of seizures with a false detection rate of 9.43%. This compares favourably with a multi-channel seizure detection method which detected 81.03% of seizures with a false detection rate of 3.82%.

Characterisation of heart rate changes and their correlation with EEG during neonatal seizures.

The effect of seizures on instantaneous HR (iHR) in 12 neonates is investigated here. HR can be readily extracted from the ECG and can be employed as an additional signal in seizure detection algorithms. The change in instantaneous HR and its correlation with the change in RMS EEG amplitude were examined. Two methods were employed to classify significant iHR changes. Significant correlation (p 0.05) during seizure was observed in 100% of patients (83.33% of seizures). Overall, significant iHR changes (classified by either method) were found in 83% of patients (50% of seizures). It was found that a markedly higher iHR was observed in patients whose seizures were not classified as having significant iHR changes.

The effect of frequency band on quantitative EEG measures in neonates with hypoxic-ischaemic encephalopathy.

The effect of frequency ranges on three quantitative EEG measures as related to neurodevelopmental outcome at 12-24 months is reported here. Thirteen EEG records from term neonates with moderate hypoxic-ischaemic encephalopathy (HIE) were analyzed. The spectral entropy, spectral edge frequency and relative power were calculated for each EEG channel. 4 separate frequency ranges were employed and their respective variations examined. Graphical and statistical analysis was carried out on the results. Statistical separation between the mean distributions of SEF, H(s) and RP was not observed. The optimal frequency band is dependent on the qEEG measure in question.

Automated estimation of sedation depth from the EEG.

A method is presented for the automatic determination of a patient's level of sedation from the EEG. Six bipolar channels of EEG recorded from 12 adult patients sedated with low-dose propofol (2, 6-disopropylphenol) were used to develop a linear discriminant based system for depth of sedation monitoring using a number of quantitative EEG measures. A cross fold validation estimate of the performance of the algorithm as a patient independent system yielded a sensitivity of 74.70% and a specificity of 81.67%. It is hoped that the methodology reported here could lead to fully automated systems for depth of sedation monitoring.

The American College of Medical Practice Executives' competency study.

This article is the first of two studies conducted by the American College of Medical Practice Executives (ACMPE) that examines the perceived roles of medical practice executives. (Founded in 1956, the American College of Medical Practice Executives is the professional development and credentialing arm of the Medical Group Management Association (MGMA)). This study asked groups of physicians and nonphysician administrators to identify the competencies and associated skills and knowledge for administering group practices in today's changing environment. Those surveyed included administrators who are Fellows in ACMPE and 795 physicians who comprise the Society of Physician Administrators of the Medical Group Management Association. The responses were examined through a framework provided by the Managed Care Process Model. In this model, the focus is on the administrative and clinical processes required by different levels of managed care market penetration. The model progresses from a focus on relatively traditional practice management functions to those activities that are more complex with a greater focus on the integration of both clinical and business processes aimed at the health of populations. The analysis of the perceived competencies indicated that while both executive types perceived the importance of managing the health of populations, that task is not yet being incorporated into their professional roles.

Benchmarking medical group practices using claims data: methodological and practical problems.

As claims data for physicians and groups of physicians has improved in quality and quantity, health information vendors have begun marketing information about medical groups' productivity, utilization, and quality. Based on interviews with product developers and our understanding of the evolution of their products, several methodological and practical issues remain. For now and the immediate future, health information vendors will continue to face the limitations of physicians' claims data. Vendors and purchasers should be aware of common data shortcomings such as inadequate monthly enrollment figures, possible physician upcoding to circumvent utilization management restrictions, and incorrect coding when a test is used to rule out a disease. In the longer term, several avenues seem likely to make medical groups' data better and richer because of computer-based medical records and efficiencies possible from the Internet. The field of benchmarking products for group practices is still an immature market. However, several trends suggest such products are highly desirable. Provider organizations which bear medical risk need benchmarking data to help improve their efficiency. There are many important nonprovider organizations that need good information on group practices' utilization patterns and outcomes to help them plan new products and negotiate with physicians.

Building research capacity into a national physician database.

The range of physician financial arrangements with managed care and insurers, as well as practice arrangements, is becoming increasingly complex. Little is known systematically about these changes, yet there is growing evidence that financial arrangements, utilization management, and other practice characteristics make a substantial difference in treatment patterns, patient mix, and costs of care. Current data systems and surveys frequently do not capture the new information needed to track these changes. New elements of information should be included in national surveys and in a national physician database. A list of recommended data items for a national data base is provided as a starting point for identifying a minimal data set to be included in national statistical systems.

Understanding the forces driving medical group practice activities: an overview.

This article provides the historical context for examining the current forces driving medical group practice organizations. Prepayment has been around for some groups since the 1930s and the dual objectives of lowering costs and improving the quality of patient care were among the original reasons for forming group practices. Some of the basic issues for group practices today are discussed in light of evolution of this model of service delivery and the intensity of today's changing environment.

Ambulatory care groups and the profiling of primary care physician resource use: examining the application of case mix adjustments.

A variety of profiling models and tools is utilized by payers, providers, and regulators to evaluate physician work, performance, and resource utilization. In physician profiling, the provider's pattern of practice is expressed as a rate of service or outcome. The article by Tucker, Weiner, Honigfeld, and Parton (this issue) compares the practice-based norms of primary care physicians by adjusting for case mix using ambulatory care groups (ACGs), a population-based classification method. Once the case mix is adjusted, the actual use of resources, as measured by overall charges, is compared with the expected value of resource use. In the Center for Research in Ambulatory Health Care Administration (CRAHCA) Physician Profiling Project, funded by The Robert Wood Johnson Foundation, physicians learn which services other physicians in their specialties perform. Physicians are able to compare their profiles with state and national level medians. The profiling project is one of the first demonstration projects in the field to profile ambulatory care practice patterns and collect patient demographics. An aspect of the project is to test the ACG classification system to data selected from 130 nonacademic practices representing over 5,000 physicians.

Graduate education in health services administration. Integrating business with clinical perspectives.

Health services administration continues to evolve in response to environmental changes in reimbursement, technology, demographics, and health care reform. These changes encourage further integration of business skills in health services, an emphasis which often conflicts with the perspectives of clinicians. The balance between business and clinical perspectives must be developed such that administrators and clinicians foster the survival and growth of their organizations while assuring comprehensive and quality health services to patients and the community. This paper describes education in health services administration, and uses a survey of one program's graduates to assess the usefulness of the current educational model in balancing business and clinical perspectives.