Wireless physiological monitoring system for psychiatric patients

Rademeyer, A. J.; Blanckenberg, Mike; Scheffer, Cornie

doi:10.1109/iembs.2009.5334576

Cited by 6 publications

(2 citation statements)

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“…AF and PVC are detected implementing the method developed by Rademeyer [ 67 ] and embedded in a wireless device to monitor psychiatric patients. The Rademeyer algorithm receives as input the electrocardiogram R-R time interval series, and in the present work, they have been replaced by the pulse-to-pulse time instants (under the assumption that the R-R time series is a surrogate of the pulse rate time series [ 39 ]).…”

Section: Methodsmentioning

confidence: 99%

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

Lazazzera

Laguna

Gil

et al. 2021

Sensors

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The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO2) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.

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Section: Methodsmentioning

confidence: 99%

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

Lazazzera

Laguna

Gil

et al. 2021

Sensors

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“…In psychiatric wards, monitoring is done by tracking patients’ vital signs and behaviors. Rademeyer et al 6 tracked psychiatric patients’ vital signs, oxygen saturation, and electrocardiograms using a wearable device that transmitted data to health care providers using a Bluetooth connection. Psychiatric patients need continuous monitoring because they may be at risk of death or injury when sedated or secluded.…”

Section: Related Workmentioning

confidence: 99%

Physical factors that influence patients’ privacy perception toward a psychiatric behavioral monitoring system: a qualitative study

Zakaria¹,

Ramli

2017

NDT

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BackgroundPsychiatric patients have privacy concerns when it comes to technology intervention in the hospital setting. In this paper, we present scenarios for psychiatric behavioral monitoring systems to be placed in psychiatric wards to understand patients’ perception regarding privacy. Psychiatric behavioral monitoring refers to systems that are deemed useful in measuring clinical outcomes, but little research has been done on how these systems will impact patients’ privacy.MethodsWe conducted a case study in one teaching hospital in Malaysia. We investigated the physical factors that influence patients’ perceived privacy with respect to a psychiatric monitoring system. The eight physical factors identified from the information system development privacy model, a comprehensive model for designing a privacy-sensitive information system, were adapted in this research. Scenario-based interviews were conducted with 25 patients in a psychiatric ward for 3 months.ResultsPsychiatric patients were able to share how physical factors influence their perception of privacy. Results show how patients responded to each of these dimensions in the context of a psychiatric behavioral monitoring system.ConclusionSome subfactors under physical privacy are modified to reflect the data obtained in the interviews. We were able to capture the different physical factors that influence patient privacy.

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MonEco: a Novel Health Monitoring Ecosystem to Predict Respiratory and Cardiovascular Disorders

Lazazzera¹,

Carrault²

2023

IRBM

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Wireless physiological monitoring system for psychiatric patients

Cited by 6 publications

References 10 publications

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

Physical factors that influence patients’ privacy perception toward a psychiatric behavioral monitoring system: a qualitative study

MonEco: a Novel Health Monitoring Ecosystem to Predict Respiratory and Cardiovascular Disorders

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Wireless physiological monitoring system for psychiatric patients

Cited by 6 publications

References 10 publications

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

Physical factors that influence patients&rsquo; privacy perception toward a psychiatric behavioral monitoring system: a qualitative study

MonEco: a Novel Health Monitoring Ecosystem to Predict Respiratory and Cardiovascular Disorders

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Physical factors that influence patients’ privacy perception toward a psychiatric behavioral monitoring system: a qualitative study