AIMC Topic: Algorithms

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Spatio-temporal CNN-BiLSTM dynamic approach to emotion recognition based on EEG signal.

Computers in biology and medicine
In this paper, a hybrid CNN-BiLSTM model for EEG-based emotion detection system is presented. The proposed technique is developed by extracting features using Power Spectral Density (PSD) signal. The proposed approach is carried out by combining CNN ...

WAND: Wavelet Analysis-Based Neural Decomposition of MRS Signals for Artifact Removal.

NMR in biomedicine
Accurate quantification of metabolites in magnetic resonance spectroscopy (MRS) is challenged by low signal-to-noise ratio (SNR), overlapping metabolites, and various artifacts. Particularly, unknown and unparameterized baseline effects obscure the q...

Eigenhearts: Cardiac diseases classification using eigenfaces approach.

Computers in biology and medicine
In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the integration of data science techniques in this field presents significant challenges, ...

Explainable artificial intelligence in forensic DNA analysis: Alleles identification in challenging electropherograms using supervised machine learning methods.

Forensic science international. Genetics
Challenging samples in capillary electrophoresis (CE)-based short tandem repeat (STR) analysis often produce artefactual signals that cannot be completely filtered out by expert electropherogram (EPG) reading systems, complicating allele interpretati...

MEF-Net: Multi-scale and edge feature fusion network for intracranial hemorrhage segmentation in CT images.

Computers in biology and medicine
Intracranial Hemorrhage (ICH) refers to cerebral bleeding resulting from ruptured blood vessels within the brain. Delayed and inaccurate diagnosis and treatment of ICH can lead to fatality or disability. Therefore, early and precise diagnosis of intr...

Unsupervised discovery of clinical disease signatures using probabilistic independence.

Journal of biomedical informatics
OBJECTIVE: This study uses probabilistic independence to disentangle patient-specific sources of disease and their signatures in Electronic Health Record (EHR) data.

Unsupervised detection of sub-sequence anomalies in epilepsy EEG.

Computers in biology and medicine
Seizures in electroencephalogram (EEG) data constitute a special case of sub-sequence anomalies in multivariate data with numerous challenges. These challenges include the irregular patterns exhibited even by the same individual, making seizures diff...

PESI-MS combined with AI to build a prediction model for lymph node metastasis of papillary thyroid cancer.

Pathology, research and practice
OBJECTIVE: Construct a prediction model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) using Probe Electrospray Ionization Mass Spectrometry (PESI - MS) combined with artificial intelligence (AI), to assist in the preoperative p...

Visceral Fat Quantified by a Fully Automated Deep-Learning Algorithm and Risk of Incident and Recurrent Diverticulitis.

Diseases of the colon and rectum
BACKGROUND: Obesity is a risk factor for diverticulitis. However, it remains unclear whether visceral fat area, a more precise measurement of abdominal fat, is associated with the risk of diverticulitis.

An Intelligent Model of Segmentation and Classification Using Enhanced Optimization-Based Attentive Mask RCNN and Recurrent MobileNet With LSTM for Multiple Sclerosis Types With Clinical Brain MRI.

NMR in biomedicine
In healthcare sector, magnetic resonance imaging (MRI) images are taken for multiple sclerosis (MS) assessment, classification, and management. However, interpreting an MRI scan requires an exceptional amount of skill because abnormalities on scans a...