Development of Patient Remote Monitoring System for Epilepsy Classification

Prabhakar, Sunil; Rajaguru, Harikumar

doi:10.1007/978-981-10-4220-1_16

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…Application area AIenabled? Nguyen and Silva [2] Cardiovascular illnesses monitoring No Szydlo and Konieczny in [3] Heart-related diseases monitoring No Lanata et al in [4], and Kozlovszky et al [5] Cardiovascular ailments analysis No Ramesh et al [6] Blood [22] Cognitive fatigue for brain injury monitoring No Prabhakar and Rajaguru [23] Classifying epilepsy No Adams et al [24] Psychoanalysis monitoring No Lakshminarayanan et al [25] Eyecare Yes…”

Section: Authorsmentioning

confidence: 99%

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Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring

Malche

Tharewal

Tiwari

et al. 2022

Journal of Healthcare Engineering

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Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic’s beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient’s activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient’s breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient’s respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices.

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Section: Authorsmentioning

confidence: 99%

“…Price et al in [ 22 ] developed a system to monitor cognitive fatigue for brain injury people. Prabhakar and Rajaguru proposed the RPM system to classify epilepsy in their study [ 23 ]. In [ 24 ], Adams et al proposed another remote patient monitoring system for psychoanalysis.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring

Malche

Tharewal

Tiwari

et al. 2022

Journal of Healthcare Engineering

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“…A recent study has developed a remote patient monitoring system for epilepsy classification (Prabhakar and Rajaguru 2017). The main purpose of the paper is to use dimensionality reduction on collected EEG data.…”

Section: Monitoring System For Brain Neurological System Related Dismentioning

confidence: 99%

Remote patient monitoring: a comprehensive study

Malasinghe

Ramzan

Dahal

2017

J Ambient Intell Human Comput

384

180

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“…Many authors have presented several solutions to overcome the need for more efficient interaction between patients and health professionals. Still, the solutions that have been proposed are not easy to implement, and most of the time, they did not have the result that was expected, in a way makes these solutions unfeasible (Archip et al, 2016;Prabhakar and Rajaguru, 2017;Varma et al, 2021). On the other hand, the process of implementing the omnichannel interaction strategy in the different health services has been growing at a breakneck pace and with visible results both for the hospitals that implemented this strategy as well as for the patients who started to take advantage of this strategy in their interactions with health professionals.…”

Section: Introductionmentioning

confidence: 99%

Omnichannel Adoption in Health Information Systems for Patient Follow-Up

2023

Proceedings of the 16th International Conference Information Systems 2023

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The evolution of technology and health information systems has allowed patients in home-based care to receive continuous medical monitoring without needing to be in-person at the care unit. This evolution discusses many challenges the care provider faces and must overcome. Still, on the other side, it brings many benefits to patients, health professionals, care providers, and society in general. Omnichannel strategy in healthcare services is in its early adoption, but it has the potential to overcome many burdens that care providers currently face regarding remote patient follow-up. This article presents the adoption of an omnichannel strategy in health information systems regarding remote patient follow-up in home-based care. The omnichannel approach can potentially increase the organization's social footprint, as this type of interaction aims to fill the gap between organizations and their peers.

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Development of Patient Remote Monitoring System for Epilepsy Classification

Cited by 12 publications

References 19 publications

Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring

Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring

Remote patient monitoring: a comprehensive study

Omnichannel Adoption in Health Information Systems for Patient Follow-Up

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