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Remote mobile heath monitoring frameworks and mobile applications: Taxonomy, open challenges, motivation, and recommendations
Advancements in mobile technology have propelled the rapid progress of remote health
monitoring, particularly in the domain of mobile health (mHealth). This progress is …
monitoring, particularly in the domain of mobile health (mHealth). This progress is …
Wearable sensors for monitoring vital signals in sports and health: progress and perspective
J Zhao, S Feng, X Cao, H Zheng - Sensor Review, 2024 - emerald.com
Purpose This paper aims to concentrate on recent innovations in flexible wearable sensor
technology tailored for monitoring vital signals within the contexts of wearable sensors and …
technology tailored for monitoring vital signals within the contexts of wearable sensors and …
Modified bi-directional long short-term memory and hyperparameter tuning of supervised machine learning models for cardiovascular heart disease prediction in …
In recent decades, cardiovascular heart disease (CHD) has emerged as the predominant
cause of mortality on a global scale. Numerous risk factors for cardiovascular disease …
cause of mortality on a global scale. Numerous risk factors for cardiovascular disease …
A smart IoT-enabled heart disease monitoring system using meta-heuristic-based Fuzzy-LSTM model
A continuous heart disease monitoring system is one of the significant applications specified
by the Internet of Things (IoT). This goal might be achieved by combining sophisticated …
by the Internet of Things (IoT). This goal might be achieved by combining sophisticated …
Comparative analysis of machine learning algorithms with advanced feature extraction for ECG signal classification
Electrocardiogram is a heartbeat signal that can be used for the application of Human-
computer interaction. Electrocardiography (ECG) is a prominent way to analyze heart rate …
computer interaction. Electrocardiography (ECG) is a prominent way to analyze heart rate …
A joint cross-dimensional contrastive learning framework for 12-lead ECGs and its heterogeneous deployment on SoC
The utilization of unlabeled electrocardiogram (ECG) data is always a critical topic in
artificial intelligence healthcare, as the manual annotation for ECG data is a time-consuming …
artificial intelligence healthcare, as the manual annotation for ECG data is a time-consuming …
[HTML][HTML] Internet of Things-based ECG and vitals healthcare monitoring system
Health monitoring and its associated technologies have gained enormous importance over
the past few years. The electrocardiogram (ECG) has long been a popular tool for assessing …
the past few years. The electrocardiogram (ECG) has long been a popular tool for assessing …
Early prediction of sudden cardiac death risk with Nested LSTM based on electrocardiogram sequential features
Electrocardiogram (ECG) signals are very important for heart disease diagnosis. In this
paper, a novel early prediction method based on Nested Long Short-Term Memory (Nested …
paper, a novel early prediction method based on Nested Long Short-Term Memory (Nested …
ECGencode: Compact and computationally efficient deep learning feature encoder for ECG signals
The visual interpretation of electrocardiogram (ECG) data is driven by human pattern
recognition and requires in-depth medical knowledge. Although state-of-the-art deep …
recognition and requires in-depth medical knowledge. Although state-of-the-art deep …
Continuous sepsis trajectory prediction using tensor-reduced physiological signals
Abstract The quick Sequential Organ Failure Assessment (qSOFA) system identifies an
individual's risk to progress to poor sepsis-related outcomes using minimal variables. We …
individual's risk to progress to poor sepsis-related outcomes using minimal variables. We …