This study introduces two novel hybrid machine-learning architectures for multilabel anomaly detection in electrocardiograms (EKGs): a 1D modified ResNet combined with a transformer encoder and an equivalent 2D ResNet...
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ISBN:
(数字)9798331513269
ISBN:
(纸本)9798331513276
This study introduces two novel hybrid machine-learning architectures for multilabel anomaly detection in electrocardiograms (EKGs): a 1D modified ResNet combined with a transformer encoder and an equivalent 2D ResNet-Transformer hybrid. This work is among the first to utilize two separate CNN-transformer architectures tailored specifically for temporal and spatial features in multilabel EKG data. Our models address the challenges of imbalanced data and multilabel classification by leveraging the PTB-XL dataset, containing over 21,000 annotated samples across five diagnostic superclasses, namely myocardial infarction, conduction disturbances, hypertrophy, ST-T wave changes, and normal EKGs. We applied advanced data augmentation techniques to mitigate class imbalance, including the Multilabel Synthetic Minority Over-Sampling Technique (ML-SMOTE). Additionally, we employed digital signal processing to denoise the EKG signals and convert time-series data into time-frequency representations for 2D modeling. Experimental results demonstrate the effectiveness of our approach, with the 1D model achieving an area under the curve (AUC) of 91.5% and the 2D model achieving an AUC of 87.2%. These findings demonstrate the potential of specialized architectures for comprehensive multilabel EKG anomaly detection.
Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the cl...
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Dyslexia is a learning disability that negatively impacts an individual's ability to read, write, spell, and sometimes speak. It results in difficulties in recognizing and decoding words and patterns, despite norm...
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We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, th...
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Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique used for the treatment of depression, as well as various neurological and psychiatric disorders. There has been ongoing interest in...
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This paper discusses a method for classification of breast cancer imaging data through the application of an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) for hyperparameter optim...
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ISBN:
(数字)9798331513269
ISBN:
(纸本)9798331513276
This paper discusses a method for classification of breast cancer imaging data through the application of an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) for hyperparameter optimization of the ANFIS system. A robust parameter tuning method is used to select the optimal configuration for the ANFIS and PSO components without expert knowledge of the dataset. Using these methods, high classification accuracies can be achieved for both the original and diagnostic versions of the Wisconsin Breast Cancer Dataset. These results demonstrate the flexibility and potential of a joint ANFIS-PSO system for automated diagnosis while retaining system simplicity and linguistic interpretability to support clinical decision-making.
We present Reusable Motion prior (ReMP), an effective motion prior that can accurately track the temporal evolution of motion in various downstream tasks. Inspired by the success of foundation models, we argue that a ...
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In this study, we aim to enhance the efficiency of p-type tunnel oxide passivated contact (p-TOPCon) solar cells by improving charge carrier extraction while achieving higher fill factor (FF) and open circuit voltage ...
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Vehicle-to-grid (V2G) technology supporting bidirectional power transfer allows electric vehicles (EVs) to contribute and consume energy bidirectionally. Because the specific properties and requirements of V2G, Khan e...
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Individuals exhibit a propensity to move faster toward more rewarding stimuli. Although this phenomenon has been observed in movements, the effect of reward on implicit control of isometric actions, such as gripping o...
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Individuals exhibit a propensity to move faster toward more rewarding stimuli. Although this phenomenon has been observed in movements, the effect of reward on implicit control of isometric actions, such as gripping or grasping, is relatively unknown. How reward-related invigoration generalizes to other effortful actions is an important question. Reward invigorates reaching movements and saccades, supporting the idea that reward pays the additional effort cost of moving faster. Effort in isometric force generation is less understood, so here we ask whether and how reward-related invigoration generalizes to isometric force gripping. And if so, what implicit characteristics of gripping change when there is a prospect of reward? Participants (n = 19) gripped a force transducer and the force applied was mapped to radial position of an onscreen cursor. Each trial, a target appeared in one of four locations;increasing grip force moved the cursor toward the target. The gripping action was interchangeable for all target positions. In each block of 100 trials, one target was consistently rewarded, whereas the other targets were not. When gripping to acquire the rewarded target, participants reacted faster, generated force more rapidly and to a greater extent, without increasing variance and without increasing the rising force-time integral. These findings support the generalization of reward-related invigoration in isometric force tasks, and that the brain exquisitely trades-off reward and effort costs to obtain reward more rapidly without increasing variance and without more effort costs than necessary. NEW & NOTEWORTHY Gripping actions are important for day-to-day tasks, for medical diagnostics like strength and force control, and for choice selection in decision-making experiments. Comparing isometric gripping responses to reward and nonreward cues, we observed reward-based invigoration mediated by selective increases in effort. These findings can be leveraged to provide
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