Beat-by-beat signal isolation, including all P waves & QRS complexes, with onsets and offsets.
Contract Research Organizations (CROs) are crucial in driving forward the development of medical therapies and drug research. The challenge of handling complex and voluminous data, especially cardiac data from electrocardiograms (ECG/EKG), demands precise and efficient analysis methods. Traditional ECG analysis techniques are often labor-intensive and slow, creating significant bottlenecks that impact scalability and speed. Both of which are essential factors in meeting the stringent timelines of clinical trials.
We offer real-time tabulation of ECG data, labeled beat-by-beat and isolated from noise. This platform is tailor-made to satisfy the high demands of clinical trial environments. Quick data turnaround and high accuracy are paramount and now possible with our ECG Neural Net platform.
At the core of our innovation is the capability to automate the processing of extensive ECG data swiftly. Traditional manual and semi-manual analysis methods can extend trial durations significantly, delaying critical outcomes. In contrast, our Neural Network can analyze large volumes of ECG data within minutes, reducing data analysis time.
Our platform can analyze 200,000 heartbeats and provide a beat-by-beat analysis in just 5-10 minutes. This speed is especially beneficial for CROs managing multiple concurrent trials, allowing them to deliver results more efficiently. Users can input raw ECG files into the Neural Network, which processes and labels each heartbeat with high precision. The output includes a CSV file detailing peaks, onsets, and offsets of the PQRST wave, along with a PDF report summarizing key trends and events.
Our Neural Network automates ECG data analysis, making research faster and reduces cost for long-term analysis. This efficiency offers a clinical trial CRO solution to optimize resources. Move staff and budgets from data processing to more critical areas like patient recruitment, trial design, and data interpretation.
Accuracy and consistency in data analysis are essential for adhering to regulatory standards. Our Neural Network provides a standardized approach to all ECG analysis run through our platform. This will reduce the risk of human error and variability in data interpretation. This is essential for clinical trial CRO software to comply with regulatory guidelines and the reliability of trial results.
Our Neural Network's scalability is a significant asset for pharmaceutical Contract Research Organizations managing multiple simultaneous trials. It efficiently handles extensive datasets without performance degradation. Pharmaceutical and preclinical Contract Research Organizations can utilize longer ECG readings across more patients and analyze them faster. This facilitates a quicker transition to clinical trials.
Our ECG labeling Neural Network can integrate smoothly into existing clinical trial management systems. You can use the UI on our site by making an account or API call into your own system. This seamless integration makes it an attractive CRO clinical solution to upgrade existing technological capabilities without disrupting ongoing operations.
The AI-driven nature of our Neural Network ensures it continuously enhances its performance with use. As it processes more data, it becomes increasingly skilled at identifying patterns and anomalies. Its accuracy is continuously improving and the value of its insights. This is beneficial for long-term clinical studies and progressive research areas.
Neural Cloud Solutions Inc. develops AI and Neural Network models for complex signal processing challenges. Our flagship technology - our “X-Factor” - is a Signal Processing Neural Network (NN). This technology expertly extracts key features of signals, enabling unprecedented insights.Our versatile algorithms are industry and device agnostic, capable of identifying and analyzing distinctive traits within any signal data. ECG labeling and analysis is just one of the many uses for our Signal Processing Neural Network.Our models learn hidden structures of any signal and classify important features. This empowers professionals to make informed decisions and uncover new digital biomarkers.
