Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly population, associated with a high mortality and morbidity stroke, heart failure, coronary artery disease, systemic thromboembolism, etc. The early detection AF necessary for averting possibility disability or mortality. However, remains problematic due to its episodic pattern. In this paper, multiscaled fusion deep convolutional neural network (MS-CNN) proposed screen out recordings from...

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling underlying temporal dependencies in dynamics. As a result, these models suffer from accuracy decay over long time and thus require frequent calibration. In this work, we address issue by formulating as sequence prediction problem which both target are sequences. We propose novel deep recurrent neural network (RNN) consisting of multilayered...

Continuous blood pressure (BP) estimation using pulse transit time (PTT) is a promising method for unobtrusive BP measurement. However, the accuracy of this approach must be improved it to viable wide range applications. This study proposes novel continuous that combines data mining techniques with traditional mechanism-driven model. First, 14 features derived from simultaneous electrocardiogram and photoplethysmogram signals were extracted beat-to-beat estimation. A genetic algorithm-based...

Continuously monitoring the ECG signals over hours combined with activity status is very important for preventing cardiovascular diseases. A traditional holter often inconvenient to carry because it has many electrodes attached chest and heavy. This work proposes a wearable, low power context-aware system integrated built-in kinetic sensors of smartphone self-designed sensor. The wearable sensor comprised fully analog front-end (AFE), commercial micro control unit (MCU), secure digital (SD)...

Ambulatory blood pressure (BP) provides valuable information for cardiovascular risk assessment. The present cuff-based devices are intrusive long-term BP monitoring, whereas cuff-less measurement methods based on pulse transit time or multi-parameter inferior in robustness and reliability by using electrocardiogram (ECG) photoplethysmogram signals. This study examined a multi-sensor fusion-based platform algorithm systolic (SBP), mean arterial (MAP), diastolic (DBP) estimation. proposed was...

This study aimed to evaluate the performance of cuffless blood pressure (BP) measurement techniques in a large and diverse cohort participants. We enrolled 3077 participants (aged 18-75, 65.16% women, 35.91% hypertensive participants) conducted followed-up for approximately 1 month. Electrocardiogram, pulse wave, multiwavelength photoplethysmogram signals were simultaneously recorded using smartwatches; dual-observer auscultation systolic BP (SBP) diastolic (DBP) reference measurements also...

Identification of different risk factors and early prediction mortality for patients with heart failure are crucial guiding clinical decision-making in Intensive care unit cohorts. In this paper, we developed a comprehensive model predicting high level accuracy using an improved random survival forest (iRSF). Utilizing novel split rule stopping criterion, the proposed iRSF was able to identify more accurate predictors separate survivors nonsurvivors thus improve discrimination ability. Based...

Pulse transit time (PTT) has received considerable attention for noninvasive cuffless blood pressure measurement. However, this approach is inconvenient to deploy in wearable devices because two sensors are required collecting two-channel physiological signals, such as electrocardiogram and pulse wave signals. In study, we investigated the (PPW) signals collected from one piezoelectric-induced sensor located at a single site estimation. Twenty-one features were extracted PPW that radial...

Background Studies have reported the use of photoplethysmography signals to detect atrial fibrillation; however, in classifying multiclass arrhythmias has rarely been reported. Our study investigated feasibility using and a deep convolutional neural network classify arrhythmia types. Methods Results ECG were collected simultaneously from group patients who underwent radiofrequency ablation for arrhythmias. A was developed multiple rhythms based on 10‐second waveforms. Classification...

Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, handcrafted feature-guided CNN transformer network cuffless BP measurement based on devices. By leveraging convolutional operations self-attention mechanisms, design CNN-Transformer hybrid architecture to learn from...

Traditional activity recognition solutions are not widely applicable due to a high cost and inconvenience use with numerous sensors. This paper aims automatically recognize physical the help of built-in sensors widespread smartphone without any limitation firm attachment human body. By introducing method judge whether phone is in pocket, we investigated data collected from six positions seven subjects, chose five signals that insensitive orientation for classification. Decision trees (J48),...

Arterial stiffness is strongly associated with cardiovascular events. Existing devices for evaluating arterial based on ultrasound or pulse wave velocity suffer a lot from complexity and inconvenience in home-care settings. This paper proposed wearable sensor monitoring via machine learning techniques. The comprised of one electrocardiogram (ECG) photoplethysmogram (PPG) module. ECG PPG signals were first simultaneously collected by the sensor, 21 features extracted two evaluation. A genetic...

We developed a ballistocardiography (BCG)-based Internet-of-Medical-Things (IoMT) system for remote monitoring of cardiopulmonary health. The composes BCG sensor, edge node, and cloud platform. To improve computational efficiency stability, the adopted collaborative computing between nodes platforms. Edge undertake signal processing tasks, namely approximate entropy quality assessment, lifting wavelet scheme separating respiration signal, lightweight peaks detection. Heart rate variability...

The security of wireless body sensor network (BSN) is very important to telemedicine and m-healthcare, it still remains a critical challenge. This paper presents novel key distribution solution which allows two sensors in one BSN agree on changeable cryptographic key. A previously published scheme, fuzzy vault, firstly applied secure the random generated from electrocardiographic (ECG) signals. Simulations based ECG data MIT PhysioBank database, produce minimum half total error rate (HTER)...

Existing models for predicting mortality based on traditional Cox proportional hazard approach (CPH) often have low prediction accuracy. This paper aims to develop a clinical risk model with good accuracy 1-year in cardiac arrhythmias patients using random survival forest (RSF), robust analysis. 10,488 available the public MIMIC II database were investigated, 3,452 deaths occurring within followups. Forty factors including demographics and laboratory information antiarrhythmic agents...

Objective: Personalization of hemodynamic modeling plays a crucial role in functional prediction the cardiovascular system (CVS). While reduced-order models one-dimensional (1D) blood vessel with zero-dimensional (0D) and heart have been widely recognized to be an effective tool for reasonably estimating functions whole CVS, practical personalized are still lacking. In this paper, we present novel 0-1D coupled, model CVS that can predict both pressure waveforms flow velocities arteries....

Continuous blood pressure (BP) provides valuable information for the disease management of patients with arrhythmias. The traditional intra-arterial method is too invasive routine healthcare settings, whereas cuff-based devices are inferior in reliability and comfortable long-term BP monitoring during study aimed to investigate an indirect continuous cuff-less estimation based on electrocardiogram (ECG) photoplethysmogram (PPG) signals arrhythmias test its determination using (IBP) as...

Recently, a kind of lightweight and resource-efficient biometrics-based security solutions were proposed for the emerging body sensor network (BSN). In such solutions, physiological characteristics that can be captured by individual sensors BSN to generate entity identifiers (EIs) securing keying materials biometric approach. this study, authors focus on an improved key distribution solution with energy information signals (EDPSs) -based EIs. Firstly, different EDPS-based EI generation...

Cardiovascular disease (CVD) is a widespread and the leading cause of death worldwide. Home care essential for patients with CVD, it involves daily monitoring important CVD-related vital signs using methods including electrocardiography (ECG), heart rate monitoring, pulse oximetry (SpO2), continuous blood pressure measurement. However, wearable device that can monitor these parameters simultaneously remains unavailable; herein, we propose lightweight, highly integrated sensor do so. In this...

The fuzzy vault scheme, which has been most widely used in biometric systems, some weaknesses while applied securing Body Sensor Network (BSN) communications. This is mainly because of the dynamic random characteristics identifiers independently generated by sensor nodes based on self-captured physiological signals. A modified scheme proposed to overcome this problem, aiming for a significant reduction recognition errors. Error-correction encoding/decoding process deployed way different from...

This paper proposes a new method which utilizes the multiple parameters to estimate blood pressure continuously. Five extracted from ECG and PPG are used fit values of systolic (SBP), mean arterial (MAP) diastolic (DBP). 10 healthy subjects were recruited take part in measurement ECG, continuous pressure. Three different equations established above three pressures by using stepwise regression method. The estimated algorithm has been verified on an independent dataset. error estimation is...