Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation.

Background: Data sharing in healthcare is vital for advancing research and personalized medicine. However, the process is hindered by privacy, ethical, and legal challenges associated with patient data. Synthetic data generation emerges as a promising solution, replicating statistical properties of real data while enhancing privacy protection.

Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies.

Generative Adversarial Networks (GANs) have emerged as a powerful tool in artificial intelligence, particularly for unsupervised learning. This systematic review analyzes GAN applications in healthcare, focusing on image and signal-based studies across various clinical domains. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we reviewed 72 relevant journal articles.

A systematic review of Automated prediction of sudden cardiac deathusing ECG signals.

Sudden Cardiac Death (SCD) stands as a life-threatening cardiac event capable of swiftly claiming lives. Researchers have devised numerous models aimed at automatically predicting SCD through a combination of diverse feature extraction techniques and classifiers. We did a rigorous review of research publications ranging from 2011 to 2023, with a specific focus on the automated prediction of SCD, a growing health concern on a global scale.

A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images.

: Despite recent advances in research, cancer remains a significant public health concern and a leading cause of death. Among all cancer types, lung cancer is the most common cause of cancer-related deaths, with most cases linked to non-small cell lung cancer (NSCLC). Accurate classification of NSCLC subtypes is essential for developing treatment strategies.

Black-white hole pattern: an investigation on the automated chronic neuropathic pain detection using EEG signals.

Electroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In this work, we developed a novel feature extraction function, black-white hole pattern (BWHPat) which dynamically selects the most suitable pattern from 14 options. We developed BWHPat in a four-phase feature engineering model, involving multileveled feature extraction, feature selection, classification, and cortex map generation.

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.

Background And Objective: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time.

A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization.

Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the maternal uterine contractions (UC) and fetal heart rate (FHR). Fetal distress, which may require therapeutic intervention, can be diagnosed using baseline FHR and its reaction to uterine contractions.

Lobish: Symbolic Language for Interpreting Electroencephalogram Signals in Language Detection Using Channel-Based Transformation and Pattern.

Electroencephalogram (EEG) signals contain information about the brain's state as they reflect the brain's functioning. However, the manual interpretation of EEG signals is tedious and time-consuming. Therefore, automatic EEG translation models need to be proposed using machine learning methods.

Automated explainable wavelet-based sleep scoring system for a population suspected with insomnia, apnea and periodic leg movement.

Sleep is an integral and vital component of human life, contributing significantly to overall health and well-being, but a considerable number of people worldwide experience sleep disorders. Sleep disorder diagnosis heavily depends on accurately classifying sleep stages. Traditionally, this classification has been performed manually by trained sleep technologists that visually inspect polysomnography records.

ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.

Unlabelled: In this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelopmental disorder, is being studied alongside its comorbidity, conduct disorder (CD), a behavioral disorder. Because ADHD and CD share commonalities, distinguishing them is difficult, thus increasing the risk of misdiagnosis. It is crucial that these two conditions are not mistakenly identified as the same because the treatment plan varies depending on whether the patient has CD or ADHD.

Comparison of Explainable Artificial Intelligence Model and Radiologist Review Performances to Detect Breast Cancer in 752 Patients.

Objectives: Breast cancer is a type of cancer caused by the uncontrolled growth of cells in the breast tissue. In a few cases, erroneous diagnosis of breast cancer by specialists and unnecessary biopsies can lead to various negative consequences. In some cases, radiologic examinations or clinical findings may raise the suspicion of breast cancer, but subsequent detailed evaluations may not confirm cancer.

A cohort study on the predictive capability of body composition for diabetes mellitus using machine learning.

Purpose: We applied machine learning to study associations between regional body fat distribution and diabetes mellitus in a population of community adults in order to investigate the predictive capability. We retrospectively analyzed a subset of data from the published Fasa cohort study using individual standard classifiers as well as ensemble learning algorithms.

Methods: We measured segmental body composition using the Tanita Analyzer BC-418 MA (Tanita Corp, Japan).

Automated characterization and detection of fibromyalgia using slow wave sleep EEG signals with glucose pattern and D'hondt pooling technique.

Fibromyalgia is a soft tissue rheumatism with significant qualitative and quantitative impact on sleep macro and micro architecture. The primary objective of this study is to analyze and identify automatically healthy individuals and those with fibromyalgia using sleep electroencephalography (EEG) signals. The study focused on the automatic detection and interpretation of EEG signals obtained from fibromyalgia patients.

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration.

Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, cardiac magnetic resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities.This study introduces a deep model called ELRL-MD that combines ensemble learning and reinforcement learning (RL) for effective myocarditis diagnosis from CMR images.

FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech.

Background: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures.

Automated anxiety detection using probabilistic binary pattern with ECG signals.

Background And Aim: Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals.

Automated detection of depression using wavelet scattering networks.

Today, depression is a common problem that affects many people all over the world. It can impact a person's mood and quality of life unless identified and treated immediately. Due to the hectic and stressful modern life seems to be, depression has become a leading cause of mental health illnesses.

A systematic review on automatic identification of insomnia.

Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual's sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals.