Popular GitHub Repositories for Bipolar Disorder

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract)

Detecting Autism Spectrum Disorder in Children With Computer?Vision - Adapting facial recognition models to detect Autism Spectrum?Disorder

MICCAI 2022 (Oral): Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Code for processing and managing data for EEG-based emotion recognition of individuals with and without Autism. EEG and other clinical data were collected in StonyBrook Social Competence Treatment Lab, for data request evaluation please contact professor Matthew D. Lerner matthew.lerner@stonybrook.edu

The world's first open-source bipolar & depression detector powered by your Apple Health data.

Machine Learning (ML) research within medicine and healthcare represents one of the most challenging domains for both engineers and medical specialists. One of the most desired tasks to be accomplished using ML applications is represented by disease detection. A good example of such a task is the detection of genetic abnormalities like Down syndrome, Klinefelter syndrome or Hemophilia. Usually, clinicians are doing chromosome analysis using the karyotype to detect such disorders. The main contribution of the current article consists of introducing a new approach called KaryML Framework, which is extending our previous research: KarySOM: An Unsupervised Learning based Approach for Human Karyotyping using Self-Organizing Maps . Our major goal is to provide a new method for an automated karyotyping system using unsupervised techniques. Addit

Unsupervised fingerprinting of disordered solids leading to analogical materials discovery.

This repo contains the code for "Voice Disorder Analysis: A Transformer-based Approach", accepted at Interspeech 2024

Named Entity Recognition for Disorder, Species, Chemical or Drug, Gene and Protein, Enzyme, Anatomy, Biological Process, Molecular Function, Cellular Component, Pathway and microRNAs.

AI art in teaching emotional intelligence to kids with ASD. Interactive prototypes for a smartwatch and a responsive website. Created with Figma.

Pytorch implementation of a GRU-based RNN for Sentiment Analysis in Mental Disorder Online Communitites.

Classification of intellectual and developmental disorder subjects and healthy controls using EEG signals

Parkinson’s disease is a central nervous system disorder. Its symptoms occur because of low dopamine levels in the brain. Four Primary symptoms are tremor, rigidity, slow movement and balance problems. Till now no cure for Parkinson’s Disease is known, treatment aims to reduce the effects of the symptoms.

Word2Vec word embedding analysis on Autism Spectrum Disorder (ASD) articles abstracts available on Pubmed.

This repository contains the contents of a Master's degree in Cognitive Science thesis project concerned with assessing the generalizability of machine learning models for depression detection in transcribed clinical interviews with patients diagnosed with chronic and first-episode major depressive disorder (MDD).

Autism Companion: Empowering Neurodiverse Children with AI Technology Our AI platform creates personalized support for children with Autism Spectrum Disorder. Using generative AI, we develop custom social stories and conversation simulations tailored to each child's unique sensitivities

This project aims to develop a robust classification model using test-takers' demographics and questionnaire responses from the ASD screening dataset to accurately identify individuals with Autistic Spectrum Disorder (ASD) through optimization of performance metrics.

A Python based project, which involves prediction autism in children using speech data using MFCC features (Training and Testing Data is Self Collected from NGOs and used with guardian permission)

Welcome to the Federated Learning for Rare Genetic Disorder Classification project! This repository demonstrates the application of Federated Learning (FL) techniques to classify rare genetic disorders using Electronic Health Record (EHR) data while addressing two critical challenges: privacy and limited data availability.

EE B.SC Final Project A @ Technion. Our goal is to classify mice displaying ASD symptoms, using audio-analysis of their squeaks and Machine-Learning methods. Part of a research aiming to use these results to diagnose ASD in infants at an early age and in a precise manner. In collaboration with Dr. Dror Lederman (HIT) and Prof. Hava Golan (BGU).