Seizure Detection Algorithms

Software algorithms designed to process, filter, and analyze raw EEG signals from medical-grade devices. The system performs artifact removal, feature extraction, and signal normalization for clinical analysis.

Software platform that uses artificial intelligence algorithms to measure, quantify, and analyze complex patterns in EEG data. The system provides customizable analytics and real-time visualization for clinical and research applications.

Software solution for automated detection and classification of cardiac arrhythmias from continuous monitoring data. Utilizes pattern recognition algorithms to identify irregular heart rhythms and alert clinicians to significant events. Compatible with standard cardiac monitoring equipment.

Specialized software for processing and interpreting EEG, EMG, and evoked potential recordings. Includes automated analysis algorithms and reporting features.

Automated software for identifying epileptiform discharges in EEG recordings. Assists clinicians in seizure disorder diagnosis and monitoring.

An artificial intelligence-powered diagnostic assistance tool that analyzes electrocardiogram waveforms and provides interpretive suggestions to clinicians. The system employs machine learning algorithms trained on extensive clinical ECG datasets.

EEG biofeedback system for non-pharmacological sleep management and digital health monitoring. Integrates electroencephalography with real-time feedback to optimize sleep quality across clinical, community, and home settings. Combines hardware devices with data analytics and AI-driven algorithms for personalized sleep health interventions.

A computer-aided diagnostic software application that processes electrocardiogram signals and generates preliminary diagnostic interpretations. The software integrates machine learning algorithms to assist physicians in identifying cardiac abnormalities.

A software system that automatically processes multiple physiological signals to generate valuable clinical insights for prevention and health monitoring. It operates securely with personalized algorithms to simplify continuous monitoring in various care settings.

Proprietary AI algorithms that continuously classify heartbeats and detect the onset and offset of all cardiac arrhythmias. The system provides real-time analysis with the highest diagnostic yield for detecting ventricular and supraventricular arrhythmias.

EEG-based software system to assist clinical diagnosis and treatment planning. Utilizes pattern recognition and normative database comparisons to identify abnormal brain activity.

Comprehensive software for EEG signal processing, artifact detection, and data analysis. Facilitates clinical interpretation and automated diagnostic assistance.