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Background And Objective: Detection and analysis of QRS-complex as well as the processing of electrocardiogram (ECG) signal using computers are being practiced for over the last fifty-eight years, approximately, and yet the thirst of designing superior ECG processing and recognition algorithms still captures researchers' attention around the globe. A saliency detection-based technique for the processing of one-dimensional biomedical signals such as ECG is proposed here for the first time, to the best or our knowledge.
Methods And Results: In this proposed research work, first, a trigonometric threshold-based technique is used to identify the QRS-complexes from the ECG signal. Motion-artifact (MA) and sudden-change-in-baseline (SCB) types of noises are considered to be the toughest among others to filter out from the ECG signals as the bandwidths of these two types of noises overlap with that of the ECG. Only one feature is extracted from each of the QRS-complex-intervals, and the normalised values of this feature are arranged in the form of a gray-scale image. Then, a saliency detection-based technique is applied iteratively on the gray-scale image to detect those regions of the ECG signals, which are highly corrupted with MA and (or) SCB noises. Next, three unique geometric-features are extracted from the rest of the QRS-complexes, which are not corrupted with MA or SCB noises, and the normalised values of these three features are arranged in the form of an Red-Green-Blue (RGB) image. Again, the saliency detection-based technique is applied to identify the abnormal QRS-complexes from the RGB image.
Conclusions: The technique is tested on long-term ECG signals; totaling a duration of 17.54 days, and its performance is evaluated through both quantitative and qualitative measures. The applicability, scope of implement in real-time scenarios, advantage of the proposed technique over the existing ones are discussed with a group of clinicians and cardiologists, and very affirmative and encouraging responses are received from them.
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http://dx.doi.org/10.1016/j.cmpb.2021.106518 | DOI Listing |
PeerJ Comput Sci
December 2024
School of Information Technology, Yancheng Institute of Technology, Yancheng, JiangSu, China.
Salient object detection aims to identify the most prominent objects within an image. With the advent of fully convolutional networks (FCNs), deep learning-based saliency detection models have increasingly leveraged FCNs for pixel-level saliency prediction. However, many existing algorithms face challenges in accurately delineating target boundaries, primarily due to insufficient utilization of edge information.
View Article and Find Full Text PDFSensors (Basel)
September 2024
Science and Technology on Space Physics Laboratory, Beijing 100076, China.
Multi-object tracking tasks aim to assign unique trajectory codes to targets in video frames. Most detection-based tracking methods use Kalman filtering algorithms for trajectory prediction, directly utilizing associated target features for trajectory updates. However, this approach often fails, with camera jitter and transient target loss in real-world scenarios.
View Article and Find Full Text PDFClin Neurophysiol
October 2024
NeuroPain Lab, Lyon Neuroscience Research Centre, CRNL - Inserm U 1028/CNRS UMR 5292, University of Saint-Etienne, University of Lyon, France.
Objective: To assess the value of combining brain and autonomic measures to discriminate the subjective perception of pain from other sensory-cognitive activations.
Methods: 20 healthy individuals received 2 types of tonic painful stimulation delivered to the hand: electrical stimuli and immersion in 10 Celsius degree (°C) water, which were contrasted with non-painful immersion in 15 °C water, and stressful cognitive testing. High-density electroencephalography (EEG) and autonomic measures (pupillary, electrodermal and cardiovascular) were continuously recorded, and the accuracy of pain detection based on combinations of electrophysiological features was assessed using machine learning procedures.
Entropy (Basel)
April 2024
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China.
Finding the most interesting areas of an image is the aim of saliency detection. Conventional methods based on low-level features rely on biological cues like texture and color. These methods, however, have trouble with processing complicated or low-contrast images.
View Article and Find Full Text PDFSensors (Basel)
October 2023
Key Laboratory of Signal Detection and Processing, College of Computer Science and Technology, Xinjiang University, Urumqi 830046, China.
In recent saliency detection research, too many or too few image features are used in the algorithm, and the processing of saliency map details is not satisfactory, resulting in significant degradation of the salient object detection result. To overcome the above deficiencies and achieve better object detection results, we propose a salient object detection method based on feature optimization by neutrosophic set (NS) theory in this paper. First, prior object knowledge is built using foreground and background models, which include pixel-wise and super-pixel cues.
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