PrismatoidPatNet54: An Accurate ECG Signal Classification Model Using Prismatoid Pattern-Based Learning Architecture

Background and objective: Arrhythmia is a widely seen cardiologic ailment worldwide, and is diagnosed using electrocardiogram (ECG) signals. The ECG signals can be translated manually by human experts, but can also be scheduled to be carried out automatically by some agents. To easily diagnose arrhythmia, an intelligent assistant can be used. Machine learning-based automatic arrhythmia detection models have been proposed to create an intelligent assistant. Materials and Methods: In this work, we have used an ECG dataset. This dataset contains 1000 ECG signals with 17 categories. A new hand-modeled learning network is developed on this dataset, and this model uses a 3D shape (prismatoid) to create textural features. Moreover, a tunable Q wavelet transform with low oscillatory parameters and a statistical feature extractor has been applied to extract features at both low and high levels. The suggested prismatoid pattern and statistical feature extractor create features from 53 sub-bands. A neighborhood component analysis has been used to choose the most discriminative features. Two classifiers, k nearest neighbor (kNN) and support vector machine (SVM), were used to classify the selected top features with 10-fold cross-validation. Results: The calculated best accuracy rate of the proposed model is equal to 97.30% using the SVM classifier. Conclusion: The computed results clearly indicate the success of the proposed prismatoid pattern-based model

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds.

COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds

Spatial Frequency-Based Analysis of Mean Red Blood Cell Speed in Single Microvessels: Investigation of Microvascular Perfusion in Rat Cerebral Cortex

BACKGROUND: Our previous study has shown that prenatal exposure to X-ray irradiation causes cerebral hypo-perfusion during the postnatal development of central nervous system (CNS). However, the source of the hypo-perfusion and its impact on the CNS development remains unclear. The present study developed an automatic analysis method to determine the mean red blood cell (RBC) speed through single microvessels imaged with two-photon microscopy in the cerebral cortex of rats prenatally exposed to X-ray irradiation (1.5 Gy). METHODOLOGY/PRINCIPAL FINDINGS: We obtained a mean RBC speed (0.9±0.6 mm/sec) that ranged from 0.2 to 4.4 mm/sec from 121 vessels in the radiation-exposed rats, which was about 40% lower than that of normal rats that were not exposed. These results were then compared with the conventional method for monitoring microvascular perfusion using the arteriovenous transit time (AVTT) determined by tracking fluorescent markers. A significant increase in the AVTT was observed in the exposed rats (1.9±0.6 sec) as compared to the age-matched non-exposed rats (1.2±0.3 sec). The results indicate that parenchyma capillary blood velocity in the exposed rats was approximately 37% lower than in non-exposed rats. CONCLUSIONS/SIGNIFICANCE: The algorithm presented is simple and robust relative to monitoring individual RBC speeds, which is superior in terms of noise tolerance and computation time. The demonstrative results show that the method developed in this study for determining the mean RBC speed in the spatial frequency domain was consistent with the conventional transit time method

Ytterbium‐Based Bioprobes for Near‐Infrared Two‐Photon Scanning Laser Microscopy Imaging

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