Search Results - "2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)"

Comparison of WPD, DWT and DTCWT for Multi-Class Seizure Type Classification by Albaqami, H., Hassan, G., Datta, A.

“…Epilepsy is characterized by recurrent seizures that come in diverse types which are treated in a variety of ways. Electroencephalography (EEG) is a technique…”
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Low Latency Real-Time Seizure Detection Using Transfer Deep Learning by Khalkhali, V., Shawki, N., Shah, V., Golmohammadi, M., Obeid, I., Picone, J.

“…Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial…”
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Automatic Report-Based Labelling of Clinical EEGs for Classifier Training by Western, D., Weber, T., Kandasamy, R., May, F., Taylor, S., Zhu, Y., Canham, L.

“…Machine learning classifiers for detection of abnormal clinical electroencephalography (EEG) signals have advanced signficantly in recent years, largely…”
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Towards a Domain-Specific Neural Network Approach for EEG Bad Channel Detection by Kumaravel, V., Paissan, F., Farella, E.

“…Electroencephalogram (EEG) is prone to several artifacts that often lead to misclassification of neural features in Brain-Computer Interfaces (BCI) [1]. In…”
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SCORE-IT: A Machine Learning Framework for Automatic Standardization of EEG Reports by Rawal, Samarth, Varatharajah, Yogatheesan

“…Machine learning (ML)-based analysis of electroencephalograms (EEGs) is playing an important role in advancing neurological care. However, the difficulties in…”
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U-EEG: A Deep Learning Autoencoder for the Detection of Ocular Artifact in EEG Signal by Onners, J., Alam, M., Cichy, B., Wymbs, N., Lukos, J.

“…Current methods to remove blink artifacts from electroencephalography (EEG) brain signals, such as independent component analysis (ICA), have reduced…”
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Combining Deep Learning with Traditional Machine Learning to Improve Phonocardiography Classification Accuracy by Chowdhury, M., Li, C., Poudel, K.

“…Phonocardiography (PCG) is a widely used technique to detect and diagnose cardiovascular diseases. We have combined the advantages of traditional machine…”
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On the Reliability of Frequency-Domain Features for fNIRS BCIs in the Presence of Pain by Subramanian, A., Shamsi, F., Najafizadeh, L.

“…In this paper, we study the effects of the presence of pain on the classification accuracy of mental arithmetic tasks in functional near infrared spectroscopy…”
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Wavelet-Based Convolutional Neural Network for Parkinson's Disease Detection in Resting-State Electroencephalography by Cahoon, S., Khan, F., Polk, M., Shaban, M.

“…Electroencephalography (EEG) is not commonly used for Parkinson's Disease (PD) detection and diagnosis. However, it has been recently indicated in the…”
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Spatial Distribution of Seismocardiographic Signal Clustering by Ahdy, S., Azad, M., Sandler, R., Raval, N., Mansy, H.

“…Seismocardiographic (SCG) signals are chest wall vibrations that correlate with cardiac activity and often measured using accelerometers on the chest surface…”
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IMLD: A Python-Based Interactive Machine Learning Demonstration by Cap, T., Kreitzer, A., Miranda, M., Vadimsky, D., Picone, J.

“…The related fields of machine learning and pattern recognition have enjoyed significant success in recent years due to the impact of deep learning algorithms…”
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A Multimodal Monitoring Approach to Predicting the Onset of Physiological Incidents Using Machine Learning by Moyer, E., Isozaki, I., Moberg, D.

“…Traumatic Brain Injury (TBI) is a complex, heterogeneous disease affecting millions of people in the U.S. each year [1]. Multimodal monitoring (MMM) is a…”
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The Identification of Respiratory Phase Using Support Vector Machines and Extreme Gradient Boosting by Rahman, B., Hassan, T., Azad, M., Sandler, R. H., Mansy, H. A.

“…Respiration is often monitored by direct airflow measurement. Indirect approaches of monitoring breathing can be used when direct airflow access is…”
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Recent Advances in the TUH EEG Corpus: Improving the Interrater Agreement for Artifacts and Epileptiform Events by Buckwalter, G., Chhin, S., Rahman, S., Obeid, I., Picone, J.

“…The Temple University Hospital EEG Corpus (TUEG) [1] is the largest publicly available EEG corpus of its type and currently has over 5,000 subscribers (we…”
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Cryptocurrency Analysis using Machine Learning and Deep Learning by Thomas, C., Watson, Z., Kim, M, Baidya, A., Lamsal, M., Chowdhury, M. H., Basnet, M., Poudel, K. N

“…Unlike typical banking transactions, blockchain-assisted cryptocurrencies are touted as the currency of the future, allowing peer-to-peer transactions without…”
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A Novel Computer Aided Detection System for Detection of Focal and Non-Focal EEG Signals using Optimized Deep Neural Network by Saminu, S., Xu, G., Zhang, S., Isselmou, A. E. K., Jabire, A. H., Ahmed, Y. K., Aliyu, H. A., Adamu, M. J., Iliyasu, A. Y., Umar, F. A.

“…Epilepsy is a neurological disorder affecting people of all ages. This disorder is reported to affect over 50 million people, with a majority residing in…”
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Effect of Normal Breathing and Breath Holding on Seismocardiographic Signals and Heart Rate by Hassan, T., Rahman, B., Sandler, R. H., Mansy, H. A.

“…Seismocardiography signals (SCG) are acoustic vibrations generated by heart activity and measured non-invasively on the surface of the chest. SCG may be used…”
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Feature Reconstruction Based Channel Selection for Emotion Recognition Using EEG by Msonda, J. R., He, Z., Lu, C.

“…There has been a surge in the use of consumer grade wearable Electroencephalogram (EEG) devices for emotion discrimination tasks in various research…”
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Laplace Beltrami Based Formulation of Corpus Callosum to Ventricle Ratio for the Analysis of Alzheimer's Condition in T1-Weighted MR Images by Manuskandan, S.R., Anandh, K. R.

“…Alzheimer's Disease (AD) is a fatal neurological condition predominated by atrophic changes in brain sub-anatomic regions. Enlargement of lateral ventricle…”
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Big EEG Data Images for Convolutional Neural Networks by Thundiyil, S., Thungamani, M., Hariprasad, S.A.

“…An open image database of Electroencephalogram (EEG) plays a vital role in developing deep learning algorithms for conducting research in EEG signal…”
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