AIMC Topic: Photoplethysmography
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Energy-efficient dynamic sensor time series classification for edge health devices.
Computer methods and programs in biomedicine
Jun 4, 2024
BACKGROUND AND OBJECTIVE: Time series data plays a crucial role in the realm of the Internet of Things Medical (IoMT). Through machine learning (ML) algorithms, online time series classification in IoMT systems enables reliable real-time disease dete...
A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit.
Computers in biology and medicine
May 29, 2024
Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized tha...
Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study.
EBioMedicine
May 29, 2024
BACKGROUND: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commerc...
Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.
Sensors (Basel, Switzerland)
May 9, 2024
Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the po...
Compression and Encryption of Heterogeneous Signals for Internet of Medical Things.
IEEE journal of biomedical and health informatics
May 6, 2024
Psychophysiological computing can be utilized to analyze heterogeneous physiological signals with psychological behaviors in the Internet of Medical Things (IoMT). Since IoMT devices are generally limited by power, storage, and computing resources, i...
Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation.
Sensors (Basel, Switzerland)
May 3, 2024
This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared ...
Exploring the Possibility of Photoplethysmography-Based Human Activity Recognition Using Convolutional Neural Networks.
Sensors (Basel, Switzerland)
Mar 1, 2024
Various sensing modalities, including external and internal sensors, have been employed in research on human activity recognition (HAR). Among these, internal sensors, particularly wearable technologies, hold significant promise due to their lightwei...
Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.
Stress and health : journal of the International Society for the Investigation of Stress
Feb 27, 2024
We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardi...
InsightSleepNet: the interpretable and uncertainty-aware deep learning network for sleep staging using continuous Photoplethysmography.
BMC medical informatics and decision making
Feb 14, 2024
BACKGROUND: This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (PPG) signals for sleep monito...
Conventional and deep learning methods in heart rate estimation from RGB face videos.
Physiological measurement
Feb 9, 2024
Contactless vital signs monitoring is a fast-advancing scientific field that aims to employ monitoring methods that do not necessitate the use of leads or physical attachments to the patient in order to overcome the shortcomings and limits of traditi...
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