Cardio-respiratory wavelet phase coherence at three ambient temperatures

Bjerkan, Juliane; Alatawi, Sultan; Ugland, Nora; Elstad, Maja; Stefanovska, Aneta

doi:10.1109/esgco55423.2022.9931379

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data for this study came from a HD and controls dataset published by researchers at Lancaster University (Bjerkan et al, 2021). The dataset provides recorded EEG, ECG, and fNIRS data for 69 Slovenian participants who were recruited from the Neurological Clinic in Ljubljana, Slovenia.…”

Section: Dataset Descriptionmentioning

confidence: 99%

The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning

Maddury

2024

Front. Signal Process.

View full text Add to dashboard Cite

Introduction: The early detection of Huntington’s disease (HD) can substantially improve patient quality of life. Current HD diagnosis methods include complex biomarkers such as clinical and imaging factors; however, these methods have high time and resource demands.Methods: Quantitative biomedical signaling has the potential for exposing abnormalities in HD patients. In this project, we attempted to explore biomedical signaling for HD diagnosis in high detail. We used a dataset collected at a clinic with 27 HD-positive patients, 36 controls, and 6 unknowns with EEG, ECG, and fNIRS. We first preprocessed the data and then presented a comprehensive feature extraction procedure for statistical, Hijorth, slope, wavelet, and power spectral features. We then applied several shallow machine learning techniques to classify HD-positives from controls.Results: We found the highest accuracy was achieved by the extremely randomized trees algorithm, with an ROC AUC of 0.963 and accuracy of 91.353%.Discussion: The results provide improved performance over competing methodologies and also show promise for biomedical signals for early prognosis of HD.

show abstract

Section: Dataset Descriptionmentioning

confidence: 99%

The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning

Maddury

2024

Front. Signal Process.

View full text Add to dashboard Cite

show abstract

Cardio-respiratory wavelet phase coherence at three ambient temperatures

Cited by 1 publication

References 3 publications

The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning

The performance of domain-based feature extraction on EEG, ECG, and fNIRS for Huntington’s disease diagnosis via shallow machine learning

Contact Info

Product

Resources

About