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An epilepsy seizure marks around 1% of the population and approximately 21–31% of patients. Based on further study, it is observed that they have at least a single seizure excess on monthly basis in case of refractory-epilepsy (Ulate-Campos et al., 2016). EEG or seizure recognition hardware allows a neutral assessment of seizure-frequency and the best possible solution customized to every affected or influenced cases. An early detection and treatment of epilepsy-seizures through closed loop systems can possibly reduce illness and chances of death in case of epileptic seizures. Single recognition hardware cannot detect every type of seizure. The selection of seizure-detection hardware should be considered with the seizure-semiology concept specific to any patient.
The researchers concern to the situational impact of the observers is of very less to negligible importance whereas the psychological impact is of vital nature during the previous decades (Forsgren, 1992). The unforeseen nature of seizures degrades the quality-of-life (QOL) of the epilepsy patient and his family members (Jacoby, 1992). Healthcare treatment customized to each and every seizure patient may ultimately reduce the rate of mortality and improve the QOL, but for this, both seizures and response to treatment must be accurately acknowledged. Presently, EEG or seizure detection levels based on individual patient and family incidents are as per narrative recalls of the understanding during the incident of seizures. This may be inappropriately measured by the capacity to understand the seizures. The author (Camfield and Camfield, 2015) tested a series of 451 kids with absence of epilepsy seizures. It was observed that up to 29% patients never noticed seizures clinically; however, they had detected a seizure in one hour of EEG recording samples during the process of hyperventilation.
While in patient analysis, the quantum of epileptic seizures indicates that according to the EEG it was 28 times greater with respect to the actual numbers indicated by the family members of the patients. It was also observed to be 9 times greater with respect to the quantum that was medically experienced by the medical attendants (Nijsen et al., 2005). In various studies, patients reported exactly 50% of the actual seizures, and still lesser while in their sleeping tenure (Forsgren et al., 2005). The recognition hardware of seizures provides extra sensitive device of recording epileptic seizures, which allows the medical experts in customizing the line of action with increasing accuracy. Further to the precast of epileptic seizures, this hardware signal and alarm in context to the nature of the expected seizure and might improve patients’ and family assurance in QOL (Hoppe et al., 2007).
During the detection and prediction of any type of seizures, the EEG graphic signals & sound recordings are of significant prominence for upholding a secured time span of its recurrence. Electronic gadgets are extra favorable towards eliminating the basic reasons as compared to the curative action towards the epileptic seizure situations (Epilepsy Foundation, 2022). This auto-generated presumption reflects to be of inaccurate nature, it is yet consider as precautionary information based on analysis. Therefore, the importance on alertness in the clinical systems and research oriented studies towards a precautionary instrument for tapering the Sudden Unexpected or Unexplained Death in Epilepsy (SUDEP) becomes a vital necessity (Shankar et al., 2013). Considering the aforementioned facts, the proposed system is designed and developmental approach of ambulatory EEG cap with portable wireless data logger is suggested. This ensures easy-to-handle the EEG data sharing with concerned persons for better data analysis and improvement in tailored treatment to every individual patient. This will lead to contribution in seizure research by centrally sharing data for further research in public domain with validity and privacy preservation of the recorded data. This approach is to design an end to end solution and not a specific module in the domain.