AI-powered healthcare platform with ECG analysis, voice symptom collection, and doctor portal
An advanced ECG anomaly detection system using deep learning. This repository contains a CNN autoencoder trained on the PTBDB dataset to identify abnormal heart rhythms. It employs various loss functions for model optimization and provides comprehensive visualizations of the results.
Проект машинного обучения для анализа электрокардиограмм (ЭКГ) с использованием сиамских нейронных сетей для обучения с малым количеством примеров (few-shot learning). Этот проект реализует подход глубокого обучения для анализа сигналов ЭКГ и обнаружения сердечных аномалий.
Zero-Shot ECG Generalization using Morphology-Rhythm Disentanglement and Mamba State Space Models. Features a production-ready Clinical Dashboard
Newton–Puiseux for CVNNs: complete toolkit for uncertainty mining, confidence calibration and local symbolic-numeric analysis on ECG (MIT-BIH) and wireless IQ data (RadioML 2016.10A).
Biomedical Signal & Image Processing Lab Projects.
A production-grade deep learning framework for zero-shot ECG classification that achieves state-of-the-art generalization through morphology-rhythm disentanglement and efficient long-range sequence modeling with Mamba/SSM.
A MATLAB-based ECG Emotion Recognition system using the DREAMER dataset. Performs preprocessing, R-peak detection, HRV feature extraction, FFT analysis, and percentile-based emotion classification.
Interactive data visualization dashboard built with Dash and Plotly, featuring military equipment transfers, ECG signal analysis, healthcare documentation NLP, and military base mapping.
Spectral analysis of ECG signals based on Fast Fourier Transform and Power Spectral Density
A desktop application for visualizing multi-channel biological signals (ECG, EMG, EEG). Built with PyQt5, it supports reading from CSV/MAT files, real-time playback simulation, and interactive signal manipulation.
ECG signal processing pipeline for Atrial Fibrillation detection and Heart Rate Variability (HRV) analysis using the MIT-BIH Atrial Fibrillation Database.
This project presents a system for automatic detection and segmentation of QRS complexes in ECG signals, combined with a mobile application for interactive cardiac analysis and reporting.
Implementácia metód Isolation Forest, One-Class SVM a Autoencoder na detekciu anomálií pri nedostatku anomálnych dát na datasetoch NASA Battery, NAB a ECG5000.
A comprehensive healthcare data and medical image processing application for managing patient health data, analyzing biomedical signals, processing medical images, and creating interactive visualizations.
Heartbeat detection from ECG signals using filtering and R-peak analysis in MATLAB.
Production-ready ML pipeline for sleep apnea detection from ECG signals (AUROC: 0.755). Features HRV/QRS extraction, signal quality gating, and patient-level AHI estimation on 70 PhysioNet patients.
AI-Driven Cardiac Monitoring System for ECG analysis, heart rate variability, and arrhythmia detection using Flask, NeuroKit2, and Machine Learning.
Open-source decoder & reverse-engineered file-format spec for Meditech CardioMera (FC1) Holter ECG recordings — parse ECG.DAT / FC1_PRG.DAT and export EDF+. Cross-platform Python alternative to CardioVisions.
ECG signal preprocessing, HRV feature extraction, and unsupervised beat clustering using scipy and scikit-learn
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