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This paper presents a novel wearable biomedical Network on Chip (NoC) concept development to monitor and predict irregular brain waves as advanced sensitive portable for an electroencephalogram (EEG) analysis device. The proposed device will monitor brain’s spontaneous electrical activity in normal and abnormal situations for specific patients suffering from different types of epilepsy...
Epilepsy is a group of neurological diseases characterized by epileptic seizures. It affects millions of people worldwide, with 80% of cases occurring in developing countries. This can result in accidents and sudden, unexpected death. Seizures can happen undetectably in newborns, comatose, or motor impaired patients, especially due to the fact that many medical personnel are not qualified for EEG...
Seizures affect each patient differently, so personalization is a vital part of developing a reliable nonEEG based seizure detection system. This personalization must be done while the patient is undergoing video EEG monitoring in an epilepsy monitoring unit (EMU) because seizure detection by EEG is considered to be the ground truth. We propose the use of confidence interval analysis for determining...
Epilepsy is the disease of brain with more than 60 million (approx) cases worldwide. The cure for epilepsy is surgery and medication. Some patients are not cured with surgery and medicine. One third of the cases still remain with unrestrained epilepsy. There is an existing need of constant monitoring for epileptic seizures in this kind of cases. Treatment can be better provided by the doctors if seizure...
Epilepsy is a neurological disorder that affects a significant percentage of the population. Currently, electroencephalogram exams (EEG) are considered a valuable tool to support epilepsy diagnosis. In order to obtain a more trustworthy diagnosis, it is frequently necessary to submit patients to long monitoring periods. This fact, beyond causing discomfort to the patients and their relatives, implies...
Epilepsy is one of the commonest, serious and divesting brain disorders. Although it is still an incurable disorder in most cases its symptoms can be ameliorated by lifelong pharmaceutical treatment. Depending on the type of epilepsy and due to its multifactorial causes, different brain and body parameters need to be assessed continuously over a long period. This allows clinicians to have a better...
In this paper, the classification of epileptic and non-epileptic events from multi-channel EEG data is investigated using a large number of time and frequency domain features. In contrast to most of the evaluations found in the literature, in this paper the non-epileptic class consists of two types of paroxysmal episodes of loss of consciousness namely the psychogenic non epileptic seizure (PNES)...
The surgical resection of the epileptogenic zone (EZ) is the only effective treatment for many drug-resistant epilepsy (DRE) patients, but the pre-surgical identification of the EZ is challenging. This study investigates whether the EZ exhibits a computationally identifiable signature during seizures. In particular, we compute statistics of the brain network from intracranial EEG (iEEG) recordings...
A video electroencephalogram (EEG) is the gold standard test for the monitoring of long term epilepsy, differentiating types of epilepsy and investigations of non-epileptic seizures. The use of video EEG in current practice is significantly limited by cost and non-availability of resources, causing delays for patients. This development addresses whether the limitations can be overcome by mobile technology...
Study of epilepsy in free moving animals provides the accurate information of this disease. While it has been proved that hemodynamic changes are greatly involved in epileptic seizures, methods for hemodynamic detection on free moving animals is limited. In this work, we integrated a photoacsoutic sensor and an EEG system into a small device that can be attached on the rat head, and for the first...
Detecting epileptic electroencephalography (EEG) signals, both automatically and accurately, is significant in ambulatory long-term monitoring patients with epilepsy. In this study, it is presented a novel epileptic-like event detection algorithm based on a mixture of amplitude, frequency and spatial analysis with rule-based decision. In this work, EEG signals from 6 different subjects were searched...
Epilepsy is a brain disorder that affects millions of adults and children in America. It is characterized by abnormal neuronal signaling and can cause strange sensations, emotions, behavior, loss of consciousness, muscle spasms, and convulsions. These episodes are very difficult to predict with the current technologies available, and almost impossible to accurately record outside clinical settings...
Epilepsy is a neurological disorder which affects the nervous system. Epileptic seizures are due to hyperactivity in certain parts of the brain. Automatic seizure detection helps in diagnosis and monitoring of epilepsy especially during long term recordings of EEG. This paper presents the bispectrum analysis of electroencephalogram (EEG) for the detection of epilepsy. Bispectrum is a higher order...
Rhythms analysis is a quantitative analysis tool that detects only the spectral patterns of rhythmical activity present on the electroencephalography (EEG) and therefore is very important on seizure detection. In the hospital of S. João, a high amount of EEG records are performed both in the routine EEG and intensive care units (ICU). On November 2009, began in this hospital the study of the impact...
This paper proposes a markerless video analytic system for quantifying body part movements in pediatric epilepsy monitoring. The system utilizes colored pajamas worn by a patient in bed to extract body part movement trajectories, from which various features can be obtained for seizure detection and analysis. Hence, it is non-intrusive and it requires no sensor/marker to be attached to the patient's...
An online seizure detection algorithm for long-term EEG monitoring is presented, which is based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient-specific EEG properties. The algorithm was evaluated using 4.300 hours of unselected EEG recordings from 48 patients with temporal lobe epilepsy. For 66% of the patients...
In this paper, we propose a multichannel monitoring system intended for chronic intracerebral EEG (icEEG) recording and automatic seizure onset detection for epilepsy presurgical evaluation. We present a method of recording, a noise reduction circuit design, followed by implementation, and validation of the icEEG monitoring system. The later includes the recording module and a seizure onset detection...
Some patients with refractory epilepsy can benefit from surgical removal of the brain region responsible for causing the epileptic seizures, i.e. the epileptogenic focus. In several cases the patient is admitted for Invasive Video Electroencephalography Monitoring (IVEM) to delineate the ictal onset zone based on intracranial electroencephalographic (IEEG) signals. However, in clinical practice this...
Automatic seizure detection is becoming popular in modern epilepsy monitoring units since it assists diagnostic monitoring and reduces manual review of large volumes of EEG recordings. In this paper, we describe the application of machine learning algorithms for building patient-specific seizure detectors on multiple frequency bands of intra-cranial electroencephalogram (iEEG) recorded by a dense...
Autonomous sensor networks that provide patient monitoring are growing in popularity due to the prospects of lower cost, and the ease of supervision by the physician. Physiological signals monitoring could result in large volume of data being either transmitted or stored which can then be directly related with the energy consumption for the system. This paper presents an EEG compression scheme that...
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