As a scenario overview the patient is in home and he suddenly loses his consciousness. The patient has similar episodes frequently. These episodes cannot be properly defined, whether they are epilepsy or a NEPE, by simply testing his clinical history and conventional EEG studies. The patient may present frequent episodes of loss of consciousness. The nature of these episodes cannot be ascertained by the clinical history and conventional EEG studies. In such a scenario respective cyber-physical system (CPS) should offer both clinic and home setups.

In the former case offline analysis could significantly help leading to a decision on whether the seizure belongs to Epilepsy or NEPE and possibly also on what type of Epilepsy or which NEPE. If possible online analysis may be useful in terms of rapid assistance provided either by family members or caregivers. In that context selected EEG electrodes with their number and position defined according to the specific epilepsy/seizure type that has been hypothesized from the clinical history. The minimum non-EEG electrodes sufficient for confident differentiation between epileptic seizures and NEPE must be used based on previous research on different patients groups (i.e. epileptic, vasovagal, psychogenic).

Considering corresponding risks in this scenario data acquisition and aggregation may be performed either by wireless transmission or local storage. In both cases, the former presenting the most significant challenges, data privacy and authentication must be assured. In order for private medical data to be acquired, analyzed, processed and in any way utilized, adequate conditions must be met concerning technical issues as well as soft issues including, but not limited to, which entities have access to which data, under what conditions, how data are acquired, how consent is acquired etc.

This scenario focuses on cases where during the day, the patient may present seizures which are defined as epileptic but the available clinic and EEG evidence is insufficient to delineate the specific type of epilepsy. This insufficiency may lead to incorrect selection of antiepileptic drug or other management. The patient presents seizures which have been documented as epileptic, but the available clinical and EEG evidence is insufficient to delineate the particular type of epilepsy/epilepsy syndrome. As the choice of antiepileptic drugs depends on the type of epilepsy, long term monitoring using novel CPS platforms at home before initiation of treatment, could provide invaluable insights using a configuration dictated by the patient’s profile including video as mandatory.

In this scenario it is expected that novel CPS systems will provide full information on clinical semiology (video) and all constituents of the particular seizure(s) (including EEG, various autonomic changes, and muscle activity and tone) and their timing/sequence. Furthermore, in such scenarios offline analysis will lead to diagnosis of the type of seizures and type of epilepsy the patient is suffering from, and selection of the appropriate antiepileptic drug/other management. To achieve this insight selected EEG electrodes with their number and position defined according to the specific epilepsy/seizure type that has been hypothesized from the clinical history. Additionally, the minimum non-EEG electrodes sufficient for full identification of the most pertinent autonomic functions that will have been identified by the previous research on patients with epileptic seizures.

Following the process of diagnosis, a patient who has been diagnosed with a particular type of epilepsy may indicate some nuisances and complains about his drugs to be insufficient or that types of seizures have also become different. After the appropriate follow up and the medication evaluation, it is decided to change the treatment accordingly. This may be caused for reasons such as, the existing medication has become less effective but seizures have not changed in type, or the existing medication has become less effective because seizures may have changed in type.

In order to decide whether or not is needed to change the treatment according to the condition, long term monitoring using novel CPS’s at home based on a configuration dictated by the patient’s personalized profile could be extremely helpful. Respective offline analysis will lead to a decision on whether a change in drug is needed and which one that may be.

In the case of a patient who is diagnosed with a potentially life-threatening type of epilepsy (mainly status epilepticus) monitoring will help detect (mainly EEG) early signs of these seizure and through feedback allow efforts to protect the patient from serious consequences of such seizure. The patient or his attending person is alerted to prevent serious consequences of the episode. Assume a case where a patient is diagnosed with a type of epilepsy which either

In the former case, a CPS system can offer online data analysis aiming to prevent and protect against serious consequences of seizures (mainly status epilepticus). Selected EEG sensors as well as non-EEG electrodes will be dictated by the patient’s personalized profile. In the latter setup case the number of sensors is expected to be small. Once again online analysis support is crucial to prevent and protect against serious consequences of seizures (mainly status epilepticus).

Finally in this scenario for a respective CPS to be truly useful and efficient, it is critical to take into consideration significant risk factors such as: False Negative Seizure Identification, False Positive Seizure Identification, Delayed Seizure Identification, and Insecure handling of sensitive and private data.

This scenario aims to address the goal of treatment efficacy improvement through the use of CPS assuming a patient who is diagnosed with idiopathic generalized epilepsy (IGE) but responds poorly to the indicated treatment. About a third of patients with IGE are resistant to the appropriate antiepileptic medication. Novel CPS aim to extend and complement respective studies by collecting a multitude of local EEG signs and their correlates from the autonomic nervous system.

A clinic setup would require offline data analysis support of selected EEG electrodes with their number and position according to IGE, based on magnetoencephalographic (MEG)/EEG/magnetic resonance imaging (MRI) analysis. Additionally, minimum non-EEG electrodes that pick up changes of autonomic function and muscle activity, relevant to IGE. In a home setup case, selected EEG electrodes with their number and position according to the specific epilepsy/seizure type, and based on MEG/EEG/MRI analysis while non-EEG electrodes that pick up autonomic changes, relevant to the particular patient’s profile.

Besides diagnosis and medication epilepsy monitoring plays a critical role in the case of positional surgical treatment. In such a context a CPS system is expected to be able to provide the wealth of clinical and neurophysiology data from many seizure events needed in order to decide (along with brain imaging, neuropsychology etc.) on whether to operate or not and with which surgical approach. For example, focal epilepsy is intractable to all antiepileptic medication and usually a surgical approach is adopted. One of the essential aims of pre-surgical evaluation is to record all habitual seizure types of the particular patient and identify the localization of their onset in the brain.

In this scenario either a clinic or a home setup can envisioned based on a wide range of sensors as well as on the personalized profile of the patient. Respective offline support is required according to the particular focal epilepsy type (lobe of origin, i.e. temporal, frontal or parietal occipital), and based on MEG/EEG/MRI analysis. At the same time non-EEG sensors are required so as to pick up autonomic changes, relevant to the particular lobe of onset, ECG vital.

In this scenario a respective CPS system is expected to offer significant advantages in monitoring a patient who presents seizures mainly or solely at sleep or to particular levels so as to delineate between several types of seizures which demand different types of treatment. It is also expected that it will enable differentiation between seizures and non-epileptic paroxysmal events that also result in electrographic or clinical arousals. Such non-epileptic paroxysmal events may include periodic leg movement disorder or obstructive sleep apnea, and can occur in patients whose clinical history gave no hint of the problem. Some people have numerous spontaneous arousals from sleep with no clear physiological precipitants. Data will be collected to understand the nature of seizures and their association to sleep macro- and microstructure. EEG electrodes configuration should comply with comfortable wearing at night.

In this case both clinic setup and home setup are to be supported based on offline line data analysis algorithms.