New horizons in ambulatory electroencephalography

Ambulatory EEG Usefulness in Epilepsy Management

SUMMARY

Long-term video-EEG monitoring has been the gold standard for diagnosis of epileptic and nonepileptic events. Medication changes, safety, and a lack of recording EEG in one's habitual environment may interfere with diagnostic representation and subsequently affect management. Some spells defy standard EEG because of ultradian and circadian times of occurrence, manifest nocturnal expression of epileptiform activity, and require classification for clarifying diagnostic input to identify optimal treatment. Some patients may be unaware of seizures, have frequent events, or subclinical seizures that require quantification before optimal management. The influence on antiseizure drug management and clinical drug research can be enlightened by long-term outpatient ambulatory EEG monitoring. With recent governmental shifts to focus on mobile health, ambulatory EEG monitoring has grown beyond diagnostic capabilities to target the dynamic effects of medical and nonmedical treatment for patients with epilepsy in their natural environment. Furthermore, newer applications in ambulatory monitoring include additional physiologic parameters (e.g., sleep, detection of myogenic signals, etc.) and extend treatment relevance to patients beyond seizure reduction alone addressing comorbid conditions. It is with this focus in mind that we direct our discussion on the present and future aspects of using ambulatory EEG monitoring in the treatment of patients with epilepsy.

Readiness among people with epilepsy to carry body-worn monitor devices in everyday life: A qualitative study

OBJECTIVE

There have been intensive efforts to design and develop new wearable technology for epileptic seizure detection. Several studies have focused on the technical aspects, but the readiness of patients with epilepsy (PWEs) to use wearables in everyday life, which is crucial, remains relatively unexplored.

METHODS

We conducted a qualitative interview study involving eight PWEs. The study was designed to provide insights into patient readiness to use wearables for home monitoring of epilepsy.

RESULTS

Three themes were identified: 1) making invisible situations visible, 2) having companionship within a troubled everyday life, and 3) sharing ownership of no recognizable moments. The analysis and interpretation revealed that the expectations of the participants for wearables were rooted in aspects that had a significant impact on their lives and self-image.

CONCLUSION

Patients with epilepsy disclosed that their readiness to use technology, specifically wearables, in everyday life relied on the assumption that they would provide an existential and comforting experience, in which the voids of their individual needs would be addressed in a more patient-friendly manner. Wearable design should consider the valuable insight that technology should be more than just technical tools that monitor symptoms; wearables are expected to be existential and esthetic artifacts that provide PWEs with meaningful experience.

Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented

The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function.

High similarity between EEG from subcutaneous and proximate scalp electrodes in patients with temporal lobe epilepsy

Subcutaneous recording using electroencephalography (EEG) has the potential to enable ultra-long-term epilepsy monitoring in real-life conditions because it allows the patient increased mobility and discreteness. This study is the first to compare physiological and epileptiform EEG signals from subcutaneous and scalp EEG recordings in epilepsy patients. Four patients with probable or definite temporal lobe epilepsy were monitored with simultaneous scalp and subcutaneous EEG recordings. EEG recordings were compared by correlation and time-frequency analysis across an array of clinically relevant waveforms and patterns. We found high similarity between the subcutaneous EEG channels and nearby temporal scalp channels for most investigated electroencephalographic events. In particular, the temporal dynamics of one typical temporal lobe seizure in one patient were similar in scalp and subcutaneous recordings in regard to frequency distribution and morphology. Signal similarity is strongly related to the distance between the subcutaneous and scalp electrodes. On the basis of these limited data, we conclude that subcutaneous EEG recordings are very similar to scalp recordings in both time and time-frequency domains, if the distance between them is small. As many electroencephalographic events are local/regional, the positioning of the subcutaneous electrodes should be considered carefully to reflect the relevant clinical question. The impact of implantation depth of the subcutaneous electrode on recording quality should be investigated further.

NEW & NOTEWORTHY This study is the first publication comparing the detection of clinically relevant, pathological EEG features from a subcutaneous recording system designed for out-patient ultra-long-term use to gold standard scalp EEG recordings. Our study shows that subcutaneous channels are very similar to comparable scalp channels, but also point out some issues yet to be resolved.

Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring

OBJECTIVE

Ear-EEG is recording of electroencephalography from a small device in the ear. This is the first study to compare ictal and interictal abnormalities recorded with ear-EEG and simultaneous scalp-EEG in an epilepsy monitoring unit.

METHODS

We recorded and compared simultaneous ear-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal spikes.

RESULTS

There were no differences in sensitivity or specificity for seizure detection. Mean correlation coefficient between ear-EEG and nearest scalp electrode was above 0.6 with a statistically significant decreasing trend with increasing distance away from the ear. Ictal morphology and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance.

CONCLUSIONS

Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe seizures. Interictal spike morphology from sufficiently large temporal spike sources can be sampled using ear-EEG.

SIGNIFICANCE

Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis.

Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved.

An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression

In recent years, there has been a growing interest in the compression of electroencephalographic (EEG) signals for telemedical and ambulatory EEG applications. Data compression is an important factor in these applications as a means of reducing the amount of data required for transmission. Allowing for a carefully controlled level of loss in the compression method can provide significant gains in data compression. Quantisation is easy to implement method of data reduction that requires little power expenditure. However, it is a relatively simple, non-invertible operation, and reducing the bit-level too far can result in the loss of too much information to reproduce the original signal to an appropriate fidelity. Other lossy compression methods allow for finer control over compression parameters, generally relying on discarding signal components the coder deems insignificant. SPIHT is a state of the art signal compression method based on the Discrete Wavelet Transform (DWT), originally designed for images but highly regarded as a general means of data compression. This paper compares the approaches of compression by changing the quantisation level of the DWT coefficients in SPIHT, with the standard thresholding method used in SPIHT, to evaluate the effects of each on EEG signals. The combination of increasing quantisation and the use of SPIHT as an entropy encoder has been shown to provide significantly improved results over using the standard SPIHT algorithm alone.

Multimodal detection of head-movement artefacts in EEG

Artefacts arising from head movements have been a considerable obstacle in the deployment of automatic event detection systems in ambulatory EEG. Recently, gyroscopes have been identified as a useful modality for providing complementary information to the head movement artefact detection task. In this work, a comprehensive data fusion analysis is conducted to investigate how EEG and gyroscope signals can be most effectively combined to provide a more accurate detection of head-movement artefacts in the EEG. To this end, several methods of combining these physiological and physical signals at the feature, decision and score fusion levels are examined. Results show that combination at the feature, score and decision levels is successful in improving classifier performance when compared to individual EEG or gyroscope classifiers, thus confirming that EEG and gyroscope signals carry complementary information regarding the detection of head-movement artefacts in the EEG. Feature fusion and the score fusion using the sum-rule provided the greatest improvement in artefact detection. By extending multimodal head-movement artefact detection to the score and decision fusion domains, it is possible to implement multimodal artefact detection in environments where gyroscope signals are intermittently available.

Outpatient ambulatory EEG as an option for epilepsy surgery evaluation instead of inpatient EEG telemetry

Outpatient ambulatory EEG is more cost-effective than inpatient EEG telemetry and may provide adequate seizure localization in a presurgical evaluation. A 51-year-old right-handed male had been unable to work or drive since the age of 35 due to intractable partial onset epilepsy. A 72-hour outpatient ambulatory EEG recorded 18 seizures from the right temporal region. No epileptiform activity was seen in the left hemisphere. Magnetic resonance imaging showed right mesial temporal sclerosis as well as an area of encephalomalacia at the medial inferior right temporal lobe. Neuropsychological assessment found that the patient was a good neurosurgery candidate. At this point, the patient was considered to be a candidate for a right temporal lobectomy. A standard right temporal lobectomy was performed. The patient has been seizure-free for 10 months after the surgery. Follow-up EEGs show no epileptiform activity. The patient is preparing to go back to work, and his driver's license was reinstated 9 months postsurgery. Neuropsychological reassessment is pending, but no apparent change in cognition has been noticed by the patient or his family. Cases with a high congruence between diagnostic imaging and the EEG abnormalities identified in the portable EEG may provide enough information regarding seizure frequency and localization to eliminate the need for inpatient EEG telemetry in the evaluation of patients for epilepsy surgery. We believe that the use of aEEG in preoperative planning should be restricted to cases of TLE and to patients with a high frequency of seizures.

A review on epilepsy in the horse and the potential of Ambulatory EEG as a diagnostic tool

Epilepsy in the horse is diagnosed based on clinical signs, but diagnosing can be difficult if a grand mal is not present. The future prospects of the horse and potentially the safety of the owner depend on an accurate diagnosis. This review presents information on epilepsy and focuses on the diagnostic potential of (Ambulatory) electroencephalography ((A) EEG). An epileptic seizure is a brain disorder, which expresses itself as a recurrent episode of involuntary abnormal behaviour. The aetiology can originate from inside or outside the brain or is idiopathic. Besides those categories, seizures can be classified as generalised or partial. A typical generalised tonic-clonic seizure is characterised by the prodrome, the ictus and the post-ictal phase. EEG is the graphic recording of rhythmic bioelectric activity which originates predominantly from the cerebral cortex. In human medicine, the 10/20 international basis system for electrode placement is used. This makes comparison more reliable and consistent. The normal human brainwaves recorded are alpha, beta, theta and delta waves. In the horse, fewer descriptions of normal signals are available. In humans suffering from epilepsy, spikes, complexes, spike-and-wave discharges and rhythmical multi-spike activity are seen. In horses suffering from epilepsy, spikes, sharp waves and spike-and-wave discharges are seen. In humans, AEEG has numerous advantages above short-duration EEG in diagnosing epilepsy or intracranial pathology. In future, AEEG might be useful to record brain signals in awake horses expressing their behaviour under natural circumstances.

Remote hearing aid fitting: Tele-audiology in the context of Brazilian Public Policy

INTRODUCTION

 Currently, the Brazilian government has certificated nearly 140 specialized centers in hearing aid fittings through the Brazilian National Health System (SUS). Remote fitting through the Internet can allow a broader and more efficient coverage with a higher likelihood of success for patients covered by the SUS, as they can receive fittings from their own homes instead of going to the few and distant specialized centers.

AIM

 To describe a case of remote fitting between 2 cities, with revision of the literature.

METHOD

Computer gears, a universal interface, and hearing aids were used.

CASE STUDY

An audiologist located in a specialized center introduced a new hearing aid and its fitting procedure to a remote center (200 km away). The specialized center helped the remote center in fitting a hearing aid in 2 patients, and performed fitting in one of its own patients. The whole process was done through the Internet with audio and video in real time.

RESULTS

Three patients were fitted remotely. Three audiologists were remotely trained on how to fit the hearing aids.

CONCLUSIONS

 Remote fitting of hearing aids is possible through the Internet, as well as further supplying technical training to a remote center about the fitting procedures. Such a technological approach can help the government advance public policies on hearing rehabilitation, as patients can be motivated about maintaining their use of hearing aids with the option to ask for help in the comfort of their own homes.

Automatic detection of childhood absence epilepsy seizures: toward a monitoring device

Automatic detections of paroxysms in patients with childhood absence epilepsy have been neglected for several years. We acquire reliable detections using only a single-channel brainwave monitor, allowing for unobtrusive monitoring of antiepileptic drug effects. Ultimately we seek to obtain optimal long-term prognoses, balancing antiepileptic effects and side effects. The electroencephalographic appearance of paroxysms in childhood absence epilepsy is fairly homogeneous, making it feasible to develop patient-independent automatic detection. We implemented a state-of-the-art algorithm to investigate the performance of paroxysm detection. Using only a single scalp electroencephalogram channel from 20 patients with a total of 125 paroxysms >2 seconds, 97.2% of paroxysms could be detected with no false detections. This result leads us to recommend further investigations of tiny, one-channel electroencephalogram systems in an ambulatory setting.

Artifact removal in physiological signals--practices and possibilities

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare "time bomb" has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.

A 160 μW 8-Channel Active Electrode System for EEG Monitoring

This paper presents an active electrode system for gel-free biopotential EEG signal acquisition. The system consists of front-end chopper amplifiers and a back-end common-mode feedback (CMFB) circuit. The front-end AC-coupled chopper amplifier employs input impedance boosting and digitally-assisted offset trimming. The former increases the input impedance of the active electrode to 2 GΩ at 1 Hz and the latter limits the chopping induced output ripple and residual offset to 2 mV and 20 mV, respectively. Thanks to chopper stabilization, the active electrode achieves 0.8 μVrms (0.5-100 Hz) input referred noise. The use of a back-end CMFB circuit further improves the CMRR of the active electrode readout to 82 dB at 50 Hz. Both front-end and back-end circuits are implemented in a 0.18 μm CMOS process and the total current consumption of an 8-channel readout system is 88 μA from 1.8 V supply. EEG measurements using the proposed active electrode system demonstrate its benefits compared to passive electrode systems, namely reduced sensitivity to cable motion artifacts and mains interference.

Intelligent artifact classification for ambulatory physiological signals

Connected health represents an increasingly important model for health-care delivery. The concept is heavily reliant on technology and in particular remote physiological monitoring. One of the principal challenges is the maintenance of high quality data streams which must be collected with minimally intrusive, inexpensive sensor systems operating in difficult conditions. Ambulatory monitoring represents one of the most challenging signal acquisition challenges of all in that data is collected as the patient engages in normal activities of everyday living. Data thus collected suffers from considerable corruption as a result of artifact, much of it induced by motion and this has a bearing on its utility for diagnostic purposes. We propose a model for ambulatory signal recording in which the data collected is accompanied by labeling indicating the quality of the collected signal. As motion is such an important source of artifact we demonstrate the concept in this case with a quality of signal measure derived from motion sensing technology viz. accelerometers. We further demonstrate how different types of artifact might be tagged to inform artifact reduction signal processing elements during subsequent signal analysis. This is demonstrated through the use of multiple accelerometers which allow the algorithm to distinguish between disturbance of the sensor relative to the underlying tissue and movement of this tissue. A brain monitoring experiment utilizing EEG and fNIRS is used to illustrate the concept.

Compressive sensing: from "compressing while sampling" to "compressing and securing while sampling"

In a traditional signal processing system sampling is carried out at a frequency which is at least twice the highest frequency component found in the signal. This is in order to guarantee that complete signal recovery is later on possible. The sampled signal can subsequently be subjected to further processing leading to, for example, encryption and compression. This processing can be computationally intensive and, in the case of battery operated systems, unpractically power hungry. Compressive sensing has recently emerged as a new signal sampling paradigm gaining huge attention from the research community. According to this theory it can potentially be possible to sample certain signals at a lower than Nyquist rate without jeopardizing signal recovery. In practical terms this may provide multi-pronged solutions to reduce some systems computational complexity. In this work, information theoretic analysis of real EEG signals is presented that shows the additional benefits of compressive sensing in preserving data privacy. Through this it can then be established generally that compressive sensing not only compresses but also secures while sampling.

Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection

Fractal dimension (FD) is a natural measure of the irregularity of a curve. In this study the performances of three waveform FD estimation algorithms (i.e. Katz's, Higuchi's and the k-nearest neighbour (k-NN) algorithm) were compared in terms of their ability to detect the onset of epileptic seizures in scalp electroencephalogram (EEG). The selection of parameters involved in FD estimation, evaluation of the accuracy of the different algorithms and assessment of their robustness in the presence of noise were performed based on synthetic signals of known FD. When applied to scalp EEG data, Katz's and Higuchi's algorithms were found to be incapable of producing consistent changes of a single type (either a drop or an increase) during seizures. On the other hand, the k-NN algorithm produced a drop, starting close to the seizure onset, in most seizures of all patients. The k-NN algorithm outperformed both Katz's and Higuchi's algorithms in terms of robustness in the presence of noise and seizure onset detection ability. The seizure detection methodology, based on the k-NN algorithm, yielded in the training data set a sensitivity of 100% with 10.10 s mean detection delay and a false positive rate of 0.27 h(-1), while the corresponding values in the testing data set were 100%, 8.82 s and 0.42 h(-1), respectively. The above detection results compare favourably to those of other seizure onset detection methodologies applied to scalp EEG in the literature. The methodology described, based on the k-NN algorithm, appears to be promising for the detection of the onset of epileptic seizures based on scalp EEG.

A biomedical sensor system for real-time monitoring of astronauts' physiological parameters during extra-vehicular activities

OBJECTIVE

To design and test an embedded biomedical sensor system that can monitor astronauts' comprehensive physiological parameters, and provide real-time data display during extra-vehicle activities (EVA) in the space exploration.

METHODS

An embedded system was developed with an array of biomedical sensors that can be integrated into the spacesuit. Wired communications were tested for physiological data acquisition and data transmission to a computer mounted on the spacesuit during task performances simulating EVA sessions.

RESULTS

The sensor integration, data collection and communication, and the real-time data monitoring were successfully validated in the NASA field tests.

CONCLUSIONS

The developed system may work as an embedded system for monitoring health status during long-term space mission.

EEG compression using JPEG2000: how much loss is too much?

Compression of biosignals is an important means of conserving power in wireless body area networks and ambulatory monitoring systems. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and hence, higher power savings, at the expense of some degradation of the reconstructed signal. In this paper, a variant of the lossy JPEG2000 algorithm is applied to Electroencephalogram (EEG) data from the Freiburg epilepsy database. By varying compression parameters, a range of reconstructions of varying signal fidelity is produced. Although lossy compression has been applied to EEG data in previous studies, it is unclear what level of signal degradation, if any, would be acceptable to a clinician before diagnostically significant information is lost. In this paper, the reconstructed EEG signals are applied to REACT, a state-of-the-art seizure detection algorithm, in order to determine the effect of lossy compression on its seizure detection ability. By using REACT in place of a clinician, many hundreds of hours of reconstructed EEG data are efficiently analysed, thereby allowing an analysis of the amount of EEG signal distortion that can be tolerated. The corresponding compression ratios that can be achieved are also presented.

On data reduction in EEG monitoring: comparison between ambulatory and non-ambulatory recordings

OBJECTIVE

To compare the performance of an EEG data selection/reduction algorithm for epileptic EEGs on ambulatory and non-ambulatory recorded data to confirm that acceptable performance is achievable in ambulatory recordings despite the presence of overt artifacts.

METHODS

A total of 167 hours of EEG data containing 899 marked interictal events is analysed to determine the percentage of events correctly recorded (the sensitivity) and the amount of data reduction achieved.

RESULTS

A better sensitivity-data reduction trade-off is found in the ambulatory recorded data. This may be unexpected as ambulatory recordings are known to contain large numbers of artifacts, but is accounted for by these artifacts being easily detected and discarded, improving the data reduction.

CONCLUSIONS

Satisfactory performance levels are found in both data types, no degradation is present with ambulatory recordings.

SIGNIFICANCE

Demonstrates that the processing of EEG data for wearable EEG applications is feasible without a loss in performance compared to traditional inpatient EEG usage.

Wearable EEG: what is it, why is it needed and what does it entail?

This paper presents a review of wearable EEG technology: the evolution of ambulatory EEG units from the bulky, limited lifetime devices available today to small devices present only on the head that can record the EEG for days, weeks or months at a time. The EEG requirements, application areas and research challenges are highlighted. A survey of neurologists is also carried out clearly indicating the medical desire for such devices.

A review of smart homes- present state and future challenges

In the era of information technology, the elderly and disabled can be monitored with numerous intelligent devices. Sensors can be implanted into their home for continuous mobility assistance and non-obtrusive disease prevention. Modern sensor-embedded houses, or smart houses, cannot only assist people with reduced physical functions but help resolve the social isolation they face. They are capable of providing assistance without limiting or disturbing the resident's daily routine, giving him or her greater comfort, pleasure, and well-being. This article presents an international selection of leading smart home projects, as well as the associated technologies of wearable/implantable monitoring systems and assistive robotics. The latter are often designed as components of the larger smart home environment. The paper will conclude by discussing future challenges of the domain.

Algorithm for AEEG data selection leading to wireless and long term epilepsy monitoring

High quality, wireless ambulatory EEG (AEEG) systems that can operate over extended periods of time are not currently feasible due to the high power consumption of wireless transmitters. Previous work has thus proposed data reduction by only transmitting sections of data that contain candidate epileptic activity. This paper investigates algorithms by which this data selection can be carried out. It is essential that the algorithm is low power and that all possible features are identified, even at the expense of more false detections. Given this, a brief review of spike detection algorithms is carried out with a view to using these algorithms to drive the data reduction process. A CWT based algorithm is deemed most suitable for use and an algorithm is described in detail and its performance tested. It is found that over 90% of expert marked spikes are identified whilst giving a 40% reduction in the amount of data to be transmitted and analysed. The performance varies with the recording duration in response to each detection and this effect is also investigated. The proposed algorithm will form the basis of new a AEEG system that allows wireless and longer term epilepsy monitoring.

Progress in predicting seizure episodes with nonlinear methods

One of the most interesting and significant areas of epileptology has been the prediction of the onset of a seizure episode from preictal activity with nonlinear methods. Not only does this type of study have heuristic value for clinical neurophysiology, but it also has potential utilitarian value for the patient with seizures. In this review, 47 reports from 12 centers with multiple studies are presented in chronological order, as are single reports from 21 other centers. The chronological order was chosen to see if progress in the form of earlier prediction was made over time. Only 21% of these reports could provide specific times for the prediction of seizure onset. The range of values was several minutes to 4 hours, with an average (median) of 6-7 minutes. Some reports (16%) had negative or nonspecific findings that prediction times could not be provided. Thus, only limited progress has been made in predicting a seizure from preictal activity, but many other related phenomena have also been studied with nonlinear methods with some success.

Lifeguard--a personal physiological monitor for extreme environments

Monitoring vital signs in applications that require the subject to be mobile requires small, lightweight, and robust sensors and electronics. A body-worn system should be unobtrusive, noninvasive, and easy-to-use. It must be able to log vital signs data for several hours as well as transmit it on demand in real-time using secure wireless technologies. The NASA Ames Research Center (Astrobionics) and Stanford University (National Center for Space Biological Technologies) are currently developing a wearable physiological monitoring system for astronauts, called LifeGuard, that meets all of the above requirements and is also applicable to clinical, home-health monitoring, first responder and military applications.

Detecting seizure onset in the ambulatory setting: demonstrating feasibility

Ambulatory EEG recorders are commercially available. The majority of these recorders are only capable of capturing and storing EEG for later review by clinicians. A few models are equipped with real-time seizure event detectors, but these detectors make no guarantees on when during a seizure a detection is made. This renders current ambulatory EEG recorders unsuitable for activating alarms or initiating therapies to acutely impact seizure progression in the ambulatory setting. Integrating seizure onset detectors into existing ambulatory recorders will make these applications feasible. Successful integration requires that these detectors be executable on the resource-limited digital signal processors found within ambulatory recorders. In this paper we describe the integration of a patient-specific seizure onset detector with a commercially available ambulatory EEG recorder, and demonstrate how such integration could enable the detection of seizure onset in the ambulatory setting.

Recommendations Regarding the Requirements and Applications for Long-term Recordings in Epilepsy

The purpose of this paper is to update the state of knowledge with respect to long-term monitoring (LTM) in epilepsy and to formulate recommendations regarding the application of LTM in clinical practice. LTM is an established technique in use both in a hospital setting and, increasingly, in an ambulatory and more recently in a community-based setting. There has been sufficient evidence to substantiate the claim that LTM is of crucial importance in documenting electroclinical correlations both in epilepsy and in paroxysmally occurring behavioral changes often mistaken for epilepsy. Internationally recognized neurophysiological equipment standards, data acquisition and data transfer protocols and widely accepted safety standards have made widespread access to LTM facilities in epilepsy possible. Recommendations on efficient and effective use of resources as well as regarding training and competencies for personnel involved in LTM in epilepsy have been formulated. The DMC Neurophysiology Subcommittee of the ILAE recommends use of hospital-based LTM in the documentation of seizures including its application for assessing seizure type and frequency, in the evaluation of status epilepticus, in noninvasive and invasive video/EEG investigations for epilepsy surgery and for the differential diagnosis between epilepsy and paroxysmally occurring nonepileptic conditions, in children and in adults. Ambulatory outpatient and community-based LTM may be used as a substitute for inpatient LTM in cases where the latter is not cost-effective or feasible or when activation procedures aimed at increasing seizure yield are not indicated. However, outpatient ambulatory monitoring may be less informative than is inpatient monitoring in some cases because: (1) reduction of medication to provoke seizures may not be safe as an outpatient; (2) faulty electrode contacts cannot quickly be noticed and repaired; (3) the patient may move out of video surveillance; and (4) duration of ambulatory monitoring can be limited by technical constraints.

Automatic seizure detection based on time-frequency analysis and artificial neural networks

The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%.

A multiparameter wearable physiologic monitoring system for space and terrestrial applications

A novel, unobtrusive and wearable, multiparameter ambulatory physiologic monitoring system for space and terrestrial applications, termed LifeGuard, is presented. The core element is a wearable monitor, the crew physiologic observation device (CPOD), that provides the capability to continuously record two standard electrocardiogram leads, respiration rate via impedance plethysmography, heart rate, hemoglobin oxygen saturation, ambient or body temperature, three axes of acceleration, and blood pressure. These parameters can be digitally recorded with high fidelity over a 9-h period with precise time stamps and user-defined event markers. Data can be continuously streamed to a base station using a built-in Bluetooth RF link or stored in 32 MB of on-board flash memory and downloaded to a personal computer using a serial port. The device is powered by two AAA batteries. The design, laboratory, and field testing of the wearable monitors are described.

A multistage, multimethod approach for automatic detection and classification of epileptiform EEG

An efficient system for detection of epileptic activity in ambulatory electroencephalogram (EEG) must be sensitive to abnormalities while keeping the false-detection rate to a low level. Such requirements could be fulfilled neither by single stage nor by simple method strategy, due to the extreme variety of EEG morphologies and frequency of artifacts. The present study proposes a robust system that combines multiple signal-processing methods in a multistage scheme, integrating adaptive filtering, wavelet transform, artificial neural network, and expert system. The system consists of two main stages: a preliminary screening stage in which data are reduced significantly, followed by an analytical stage. Unlike most systems that merely focus on sharp transients, our system also takes into account slow waves. A nonlinear filter for separation of nonstationary and stationary EEG components is also developed in this paper. The system was evaluated on testing data from 81 patients, totaling more than 800 hours of recordings. 90.0% of the epileptic events were correctly detected. The detection rate of sharp transients was 98.0% and overall false-detection rate was 6.1%. We conclude that our system has good performance in detecting epileptiform activities and the multistage multimethod approach is an appropriate way of solving this problem.