ECG pathologies detection

Our system offers an advanced solution for automatic detection and visualization of pathologies in ECG data.

The problem

Detecting pathologies in human ECG data is a complex and time-consuming process that requires highly skilled medical professionals. Manually reviewing ECG strips and identifying anomalies can be tedious and requires significant time and expertise. Existing methods often lack accuracy and the ability to provide an intuitive visualization for anomalies. Clinicians need tools to quickly and accurately identify irregularities like atrial fibrillation, premature ventricular contractions, and other cardiac anomalies while maintaining a clear view of the normal beats.

The solution

Our pathology detection system allows users to upload ECG files in various formats, which are then processed to detect normal and anomalous beats. The results are displayed as marked ECG strips with clear annotations for each type of beat (e.g., normal, premature ventricular contraction, atrial premature beat). This streamlined process aids in early diagnosis and reduces the workload for clinicians.

Our team was responsible for

Identifying and gathering requirements
UI/UX
Implementing the web user interface
Developing and training Machine Learning model

The client

This solution is developed for healthcare providers, medical researchers, and institutions focused on cardiovascular health. It can assist clinicians in detecting and visualizing abnormalities in electrocardiogram (ECG) data, aiding in timely diagnosis and treatment.

Frontend technologies

web UI – Angular

How we used ML

We have developed and trained our own machine learning model to effectively detect and analyze ECG data using diverse datasets. By utilizing powerful machine learning tools, we build a model that can identify patterns and anomalies in ECG signals. Specialized frameworks aid in data preprocessing and evaluating our models’ performance. Optimized libraries ensure efficient data handling and numerical computations, while advanced signal processing techniques allow for a detailed analysis of ECG waveforms. Our system supports multiple performance metrics, including precision, recall, and F1-score, achieving an impressive accuracy rate of 98%.