A method for intelligent support to medical diagnosis in emergency cardiac care

Neto, Luis A. Souto Maior; Pequeno, Robson; Almeida, Carlos; Galdino, Kátia Elizabete; Martins, Fabricia; Moura, Antonio V. de

doi:10.1109/ijcnn.2017.7966438

Cited by 1 publication

(2 citation statements)

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“…Section A illustrates the wearable devices connected to patients to collect vital signs such as heart rate, pulse rate, respiratory rate, breathing rate, body temperature, and so on. In Section B, the collection will be stored in cloud services (Neto et al, 2017; Shao et al, 2020; Shi et al, 2020) for further analysis using machine learning methodologies that could predict or classify the patient data. The process could then estimate any abnormal events in the near future based on known threshold values of the vital signs and update medical staff or healthcare professionals (Ankita et al, 2021; Bekiri et al, 2020; C. Liu et al, 2019; Lin et al, 2018; Devi & Kalaivani, 2019; Efat et al, 2020; Shao et al, 2020) in Section C of the architecture.…”

Section: Remote Patient Monitoring Architecturesmentioning

confidence: 99%

“…Neto et al (2017) designed an RPM system with a portable ECG device to assist remote electrocardiographic diagnosis and send the data to cloud service, where an intelligent arrhythmia detector (IDAH-ECG) detected abnormal heartbeats and informed physicians. Here, discrete wavelet transforms feature extraction, and principal component analysis (PCA) dimensionality reduction was performed as part of data preprocessing.…”

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confidence: 99%

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Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

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The adoption of artificial intelligence (AI) in healthcare is growing rapidly.Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI-enabled RPM. This review explores the benefits and challenges of patient-centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI-enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends.

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