Category Archives: Myths/Misconceptions

Myths and Misconceptions

Myths/Misconceptions, , ,

Assessment 2 – Objective seizure detection with biosensors

My first assessment for epilepsy evaluation concluded that persons with epilepsy (PWE) unfortunately have little to no ability to reproducibly predict the onset of their own seizures.  Therefore, subjective impressions are of marginal value.  Given that, there is a vital need for devices or instruments with ability to objectively predict a seizure. 

Many devices called biosensors are already available  (see reviews Nagaraj et al., 2015; Ulate-Campos et al., 2016).  The capability of existing biosensors to predict seizures varies with the specific device.  Unfortunately detection and prevention are generally not part of the same device.  Despite the fact that current biosensors (with possibly one exception) are only able to detect seizures, nevertheless, they represent a significant first step to development of something more sophisticated with therapeutic value.  Furthermore, present day biosensors supply information on seizure frequency, important information that PWE are unable to provide due to an inability to recall the seizure (Hoppe et al., 2015).  As a note of caution, biosensors to date are useful only in certain types of seizures and so to be of benefit, appropriate biosensor selection requires accurate seizure type identification (Ulate-Campos et al., 2016).  

How biosensors work

There are numerous biosensors in use that detect seizures but generally they do not as yet predict seizures within a reasonable time frame for adequate intervention.  Some biosensors record muscle movements e.g. surface electromyography (Conradsen et al., 2012), measure change in muscle acceleration called accelerometry  (Beniczky et al., 2013), or measure movement via mattress sensors (Poppel et al., 2013).  Other biosensors record effects produced by activation of the autonomic nervous system.  This includes measurement of sweating (electrodermal activity, EDA), heart rate (electrocardiogram, EKG), respiration, body temperature and combinations of these with sophisticated computer programs (Ulate-Campos et al., 2016). 

The aforementioned biosensors target seizures in which muscle and autonomic nervous system changes are evident and occur before a seizure.  Other biosensors use near-infrared to detect changes in brain blood flow prior to a seizure (Tewolde et al., 2015).  Ulate-Campos et al., 2016 lists the more than 40 available biosensors and their web sites. 

EEG and intracranial electrodes  – important contributions

However, for all seizures, the gold-standard for seizure detection is the electroencephalogram (EEG).  The EEG records the summation of brain wave activity via topically attached electrodes positioned around the scalp.  Unfortunately, everyday use of the EEG is incompatible with daily life. 

Representative EEG brain wave activity

Another approach, intracranial electrode implantation (iEEG), provides continuous long term measurement of brain activity in the seizure prone region.  Results of small studies and clinical trials with the iEEG (Iasemidis et al., 2003; Cook et al., 2013; Spencer et al., 2016; Karoly et al.,2018; Baud et al., 2018) have confirmed the  presence of

a) unique neurological activity termed epileptiform discharges that occur with a regularity of 24 hours (circadian) or over several days (multi-day), and

b) the relation of aspects of these rhythms (phases) with seizures in PWE with low to moderate seizure frequency rate (Baud et al., 2018).

NeuroVista Advisory System is an example of the iEEG that has undergone clinical evaluation. NeuroVista Advisory System employs an assessment system using sophisticated algorithms.  In clinical trials (Cook et al., 2013; Bergey et al., 2015) it performed with reasonable success.  In several cases where seizure prediction fell below pre-designated criteria, optimization of the computer system i.e. its algorithms, dramatically improved predictability of the seizure.  Thus it appears possible to tailor the algorithm to each patient to achieve the best result (Kuhlmann et al., 2018). 

Although the iEEG successfully analyzes prodrome data (see Assessment 1) to predict a seizure and hence supplies information in a sufficient time frame to avert a seizure, it is not without serious concerns.  An implantation of intracranial electrodes is an invasive technique requiring major surgery and carries the risk of infection and related brain problems.

Biofeedback Biosensors – hope for the future

The most desirable biosensor is one that employs a biofeedback loop with both

1) reliable seizure detection and

2) appropriate and effective therapeutic intervention that prevents the seizure. 

Neuropace Responsive Neurostimulation (RNS) (http://www.neuropace.com)  device is an example of a biosensor with a biofeedback loop.  The device is implanted in the brain and detects epileptiform discharges and delivers neurostimulation to suppress the seizure.  Results of a two year clinical trial with 191 medication- resistant PWE, showed significant decline in seizure frequency (Heck et al., 2014).  The PWE enrolled in this trial had intractable partial-onset epilepsy.  The development of the Neuropace RNS is a significant achievement for select PWE.  However, the biggest limitation is the requirement of major surgery for implantation of this device.

Characteristics of the ideal biosensor

The ideal biosensor would monitor seizure-related external biological phenomena such as EKG, EDA, muscle movement blood flow etc., as discussed above and accurately assess the data to predict a seizure.  Additionally, seizure detection would be linked with effective seizure prevention measures.  This could take the form of a) an alert  to a caretaker to give appropriate therapy, b)  neurostimulation or c) minipump infusion of appropriate mediation (Ulate-Campos et al., 2016).  Additional characteristics of the ideal biosensor include safety and efficacy without false alarms, user friendly instrumentation, ease of care, wireless transmission of data and full compatibility with daily life (Hoppe et al., 2015).

Assessment-2-referencesDownload

Questions for readers

Comments on this blog are welcome.  Experience with biosensors would be appreciated.

Myths and Misconceptions

Myths/Misconceptions, , , ,

Assessment 1 – Are seizures predictable? What are the triggers?

Preface 

Epilepsy is a serious neurological disease, usually with an unknown origin.  In the USA population, this disease affects about 1.2% of individuals. It is a disease that produces random seizures of varying duration, magnitude and frequency.  Approximately 30% of persons with epilepsy (PWE) derive no benefit from anti-epileptic drugs. Many others are poorly served by these medications (Thijs et al., 2019).  For this group, the ability to identify the seizure trigger(s), so as to intervene and prevent the seizure, is of the highest priority.  Therefore, it is important to know whether the current science convincingly show seizure predictability with identifiable and reliable initiators.

Science from diverse disciplines

Definitions of seizure symptoms – which ones are important for reliable predictions?

To evaluate epilepsy, it is important to first understand some established terminology. The scientific literature has categorized the “symptoms to anticipate seizures” (Kotwas et al., 2016) as falling into 3 distinct categories as follows: 

     a) auras,

     b) premonitory or prodromes, and

     c) precipitating factors. 

If reliable, each provides a different degree of potential benefit for PWE.

Auras

First, auras are symptoms that occur in some PWE immediately prior to the seizure.  In the presence of an aura, seizure events are already in play and the ability to prevent the seizure is nil.  However, it could allow for PWE to seek safety e.g. lying down to prevent a fall or calling for assistance (Schulz-Bonhage and Haut, 2011). 

Prodrome

Secondly, the prodrome symptoms occur 30 minutes to 6-12 hours prior to a seizure.  Examples of reported symptoms are :”mood disorders; symptoms such as irritability, anxiety, depression, fear, anger, excitability and reduced tolerance” and “non-specific ‘funny feeling’, headache, and cognitive disturbances; bradypsychia, speech disturbances and attentional deficits” (Scaramelli et al., 2009; MacKay et al., 2017).  The prodrome permits intervention with  potential to negate a seizure and has been examined repeatedly in drug-resistant PWE.

Precipitating Factors

Thirdly, precipitating factors are symptoms that occur 24-12 hours prior to a seizure and if correctly identified could enable the PWE to change behavior, location, activity etc.  Symptoms include “stress, stressful events, sleep deprivation, symptoms of depression, anxiety, fatigue (Kotwas et al., 2016).

Of the three categories, the second category, the prodrome, offers the best time frame to prevent a seizure and it is the time frame of interest of many investigators (MacKay et al., 2017). 

Seizure prodrome predictions with subjective analysis lack credibility

There exists numerous studies (see reviews Illingworth et al., 2014; Kotwas et al., 2016; Mackay et al., 2017)  and several clinical trials (Privitera et al., 2019; Jeppesen et al., 2019) that have investigated the extent of subjective seizure prediction in PWE resistant to anti-seizure medications.  Studies employed questionnaires, interviews and electronic diaries to assess the ability of PWE to predict their seizures.  The results suggest that a small subset of PWE have the ability of seizure prediction.  However, these studies lack scientific rigor.  This means that the data were collected retrospectively (as a recall) and the time between “predictive” symptom(s) and seizure was unknown.  The most meticulous assessments use electronic diaries (see review MacKay et al., 2017).  However, even with this approach, multiple choice answers were supplied for each question relating to events prior to a seizure.  PWE reported their seizures without electronic verification (such as with an electroencephalograph (EEG) recording).  Results were further honed by selecting epileptics who had a high degree of confidence in their ability to predict their seizures and of these, only 9 out of 20 achieved this with their electronic responses.  Additionally, many studies were faced with a diversity of epilepsy disorders that confound results.  Despite the high level of interest in this topic and urgent need to know definitively the predictability of a seizure, results of studies to date with a subjective approach of surveys, questionnaires and diaries are unconvincing.

References for this assessment can be found in the PDF download.

Assessment 2 will examine seizure prediction with biosensors and what the future holds for this approach.

Assessment-1-ReferencesDownload