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Jun 18, 2006

Seizure Detection Algorithm

Via Medgadget


 

A New Zealand based medical device company is seeking FDA approval for their seizure detection algorithm. According to the company's press release:

The data supporting the seizure detection algorithm has been presented at multiple international medical conferences over the past 18 months. The data presented indicated that the BrainZ seizure detection algorithm had higher sensitivity, higher positive predictive value, higher correlation, and a lower level of false positive detection than two other recognized seizure detection algorithms. The latest presentation was made to the Pediatric Academic Societies' meeting in San Francisco in May 2006.
 

Company's technology in a nutshell:

 

 
The BRM2 Brain Monitor provides bilateral aEEG (amplitude-integrated EEG) displays to allow easy recognition of background EEG patterns, and EEG Waveform displays to show the raw EEG signal from each hemisphere.

Amplitude-integrated EEG (aEEG) provides a compressed display of the level (amplitude) of EEG activity.

It is useful for continuous monitoring of background EEG activity and for discriminating between normal and abnormal EEG traces.

Abnormal aEEG traces can be used to identify patients who require further neurological workup and investigations. Normal traces may be used to reassure families of the likelihood of good long term neurological outcome for their infant.

Studies show marked changes in the level and frequency of EEG activity after ischemic injury. These changes can be predictive of the extent of neurological deficit. The pathophysiologic EEG changes associated with brain injury evolve through latent and delayed phases, over several days. Prolonged monitoring over the first week after birth can be valuable, as normalization of aEEG recording is associated with an improved outcome compared to a persistently abnormal recording. The longer the period of monitoring the more accurately the severity of brain injury can be assessed.

Seizure activity has often been monitored by clinical assessment alone, however a large proportion of seizure activity is either difficult to assess by examination or has no clinical manifestation. Bedside monitoring with aEEG traces can be used to identify seizure-like events in real time, with review of the raw EEG trace recommended for event validation. EEG monitoring can be used to guide the affect of anticonvulsant therapy.

aEEg can also be used to help identify those patients who are most likely to benefit from new hypothermia therapies. These therapies may improve outcomes in infants exposed to hypoxic ischaemic encephalopathies.

 

18:30 Posted in Research tools | Permalink | Comments (0) | Tags: research tools

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