On the use of inertial sensors and machine learning for automatic recognition of fainting and epileptic seizure

Ribeiro, Erick Elisson Hosana; Bentes, Larissa; Cruz, Anderson; Leitão, Gabriel; Barreto, Raimundo; Silva, Vandermi J.; Primo, Tiago Thompsen; Koch, Fernando

doi:10.1109/healthcom.2016.7749420

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…The authors [62] presented a method of machine learning for people with epileptic problems. A Wearable device was used to carry out the study considering F-Score and Accuracy metrics.…”

Section: Other Diseasesmentioning

confidence: 99%

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

et al. 2020

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Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.

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“…The authors [62] presented a method of machine learning for people with epileptic problems. A Wearable device was used to carry out the study considering F-Score and Accuracy metrics.…”

Section: Other Diseasesmentioning

confidence: 99%

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

et al. 2020

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“…Helmy & Helmy, (2015) [32] created the first mobile application which was called Seizario, This app offered a set of useful properties that help to provide assistance to epilepsy patients. Seizario app.…”

Section: Researches Based On Acmmentioning

confidence: 99%

“…Whilst Ribeiro et al (2016) [33] used the wearable device from ACM and embedded sensors by applying machine learning algorithms obtained KNN 99% and C4.5 & PART 98% and there was no valuable variation at the results between KNN, PART, and C4.5… An experiment showed that the user of wearable accelerometer sensor was found very efficiently with a limited number of false alarms (Tonpe et al, 2017) [34]. Also Kusmakar et al (2017) [35] used wearable ACM sensors and their results showed that the sensitivity was 23% with 0:72=24h.…”

Section: Researches Based On Acmmentioning

confidence: 99%

Wearable Detection Systems for Epileptic Seizure: A review

Alwindawi

Uçan

Morad³

2020

alkej

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The seizure epilepsy is risky because it happens randomly and leads to death in some cases. The standard epileptic seizures monitoring system involves video/EEG (electro-encephalography), which bothers the patient, as EEG electrodes are attached to the patient’s head. Seriously, helping or alerting the patient before the seizure is one of the issue that attracts the researchers and designers attention. So that there are spectrums of portable seizure detection systems available in markets which are based on non-EEG signal. The aim of this article is to provide a literature survey for the latest articles that cover many issues in the field of designing portable real-time seizure detection that includes the use of multiple body signals, new algorithm methods, and detection devices that are commercially available. As a result, the reviewing process shows that there are many research articles that have covered wearable seizure detection systems that based on body signals. The more effective monitoring and detection seizure system is the system that uses multi-body signals, is highly comfortable and has low power consumption.

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On the use of inertial sensors and machine learning for automatic recognition of fainting and epileptic seizure

Cited by 2 publications

References 12 publications

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Wearable Detection Systems for Epileptic Seizure: A review

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