Blood pressure monitoring is one avenue to monitor people’s health conditions. Early detection of abnormal blood can help patients get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it very valuable have a mechanism perform real-time for changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) photoplethysmogram (PPG) the estimation systolic (SBP) diastolic (DBP) values. We use bidirectional...

Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies over days, minutes, or even seconds for short-term cases. Most of photoplethysmography (PPG)-based estimation methods are susceptible to noise and only provides systolic (SBP) diastolic (DBP) prediction. Here, instead estimating a discrete value, we focus on different perspectives estimate the whole waveform BP. We propose novel deep learning model learn how perform signal-to-signal translation from PPG...

Due to the growing public awareness of cardiovascular disease (CVD), blood pressure (BP) estimation models have been developed based on physiological parameters extracted from both electrocardiograms (ECGs) and photoplethysmograms (PPGs). Still, in order enhance usability as well reduce sensor cost, researchers endeavor establish a generalized BP model using only PPG signals. In this paper, we propose deep neural network capable extracting 32 features exclusively signals for estimation. The...

Continuous blood pressure (BP) acquisition is critical to health monitoring of an individual. Photoplethysmography (PPG) one the most popular technologies in last decade used for measuring noninvasively. Several approaches have been carried out various ways utilize features extracted from PPG. In this study, we develop a continuous systolic and diastolic (SBP DBP) estimation mechanism without need any feature engineering. The raw PPG signal only got preprocessed before being fed our model...

In this paper, we consider the problem of forgery and misuse signatures that happen oftentimes. We propose a framework for offline signature verification using pyramid histogram oriented gradient (PHOG) as feature. The PHOG feature is extracted from binary image same size does not have much noise therefore before it extracted, preprocessing performed. There are various parameters may affect extraction characteristics such number bin, level, angular range, amount training data used. Using...

Diabetic retinopathy (DR) is the kind of diabetes complication that affects eyes and can damage blood vessels inside retina. To diagnose strength DR disease based on examination Nowadays, common diagnosis process asks for experienced ophthalmologists to inspect both fundus image OCT (optical coherence tomography) images, which time-consuming not very convenient remote rural inhabitants. The research purpose in this paper propose a new paradigm automatic by using artificial intelligence cloud...

The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data takes on an added importance especially for some pattern recognition tasks which classes are rare difficult to collect. In iris dataset, instance, minority class samples include images eyes with glasses, oversized or undersized pupils, misaligned locations, occluded contaminated by eyelids, eyelashes, lighting reflections. Such class-imbalanced...

The evaluation of baroreflex sensitivity (BRS) has proven to be critical for medical applications. use α indices by spectral methods been the most popular approach BRS estimation. Recently, an algorithm termed Gaussian average filtering decomposition (GAFD) proposed serve same purpose. GAFD adopts a three-layer tree structure similar wavelet but is only constructed windows in different cutoff frequency. Its computation more efficient than that conventional methods, and there no need specify...

Accurate detection of faults in sensor data is essential for monitoring and controlling industrial processes, environmental conditions, infrastructure to ensure reliability enable informed decision-making. Resolving these ensures measurement quality unlocks process optimization opportunities, resulting improved performance, energy efficiency, cost savings. We present a transformer-based fault model which adopts the anomaly-attention mechanism. Experiments have been performed on benchmark...