Evidence map›Paper›PMID 37461102›Full record

ArticleBMC biomedical engineering2023

A novel wearable device for automated real-time detection of epileptic seizures.

Mikael Habtamu, Keneni Tolosa, Kidus Abera, Lamesgin Demissie, Samrawit Samuel, Yeabsera Temesgen, Elbetel Taye Zewde, Ahmed Ali Dawud

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Mikael HabtamuSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
Keneni TolosaSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
Kidus AberaSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
Lamesgin DemissieSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
Samrawit SamuelSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
Yeabsera TemesgenSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
Elbetel Taye ZewdeSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
Ahmed Ali DawudSchool of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia. ahme8002@gmail.com.ORCID http://orcid.org/0000-0002-7365-1950
Jimma University · ET

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEpilepsy is a neurological disorder that has a variety of origins. It is caused by hyperexcitability and an imbalance between excitation and inhibition, which results in seizures. The World Health Organization (WHO) and its partners have classified epilepsy as a major public health concern. Over 50 million individuals globally are affected by epilepsy which shows that the patient's family, social, educational, and vocational activities are severely limited if seizures are not controlled. Patients who suffer from epileptic seizures have emotional, behavioral, and neurological issues. Alerting systems using a wearable sensor are commonly used to detect epileptic seizures. However, most of the devices have no multimodal systems that increase sensitivity and lower the false discovery rate for screening and intervention of epileptic seizures. Therefore, the objective of this project was, to design and develop an efficient, economical, and automatically detecting epileptic seizure device in real-time.

methodsOur design incorporates different sensors to assess the patient's condition such as an accelerometer, pulsoxymeter and vibration sensor which process body movement, heart rate variability, oxygen denaturation, and jerky movement respectively. The algorithm for real-time detection of epileptic seizures is based on the following: acceleration increases to a higher value of 23.4 m/s

resultsThe prototype was built and subjected to different tests and iterations. The proposed device was tested for accuracy, cost-effectiveness and ease of use. An acceptable accuracy was achieved for the accelerometer, pulsoxymeter, and vibration sensor measurements, and the prototype was built only with a component cost of less than 40 USD excluding design, manufacturing, and other costs. The design is tested to see if it fits the design criteria; the results of the tests reveal that a large portion of the scientific procedures utilized in this study to identify epileptic seizures is effective.

conclusionThis project is objectively targeted to design a medical device with multimodal systems that enable us to accurately detect epileptic seizures by detecting symptoms commonly associated with an episode of epileptic seizure and notifying a caregiver for immediate assistance. The proposed device has a great impact on reducing epileptic seizer mortality, especially in low-resource settings where both expertise and treatment are scarce.

Indexed as

AccelerationEpileptic seizureJerky movementOxygen denaturationReal-time detectionWearable sensors

Identifiers

PMID37461102
PMCPMC10353099
OpenAlexW4384521909

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.