Development of an Advanced Automated ECG Analysis Software for Medical Research

The client faces difficulties in reliably processing continuous ECG recordings obtained from mobile cardiac telemetry, with current applications exhibiting limited accuracy, noise interference, and inefficient workflows that delay medical review. There is a need for a solution that automates ECG analysis with high precision, minimizes manual effort, and integrates seamlessly into clinical and preclinical research environments.

A mid-sized medical technology company specializing in remote cardiac monitoring solutions and digital health tools, aiming to enhance ECG analysis workflows.

• Develop a software system capable of automatic, high-accuracy analysis of continuous ECG recordings for clinical and research applications.
• Achieve noise reduction by integrating proprietary algorithms and patented technology, aiming to eliminate at least 95% of noise in ECG signals.
• Enable configuration of automated workflows for large-scale ECG data uploads and processing to streamline medical review.
• Ensure rapid processing times—approximately 30 seconds per 24-hour ECG recording—and facilitate review, editing, and report generation within 10-20 minutes.
• Implement a robust multi-server architecture for raw data collection and storage, supporting high throughput and data integrity.
• Support multiple file formats and ECG device outputs, including HL7, EDF, and other industry standards, with compatibility for various Holter monitor manufacturers.
• Generate comprehensive reports in graphical or tabular PDF formats and export data as CSV files for further analysis.
• Design the system to comply with healthcare regulations such as 21 CFR Part 11, ensuring data security and auditability.
• Enhance diagnostic accuracy by classifying beat types, offering a more comprehensive assessment of patient heart conditions.
• Create a cross-platform solution compatible with Windows and other OSs to maximize accessibility.

• Automated processing of long-duration ECG recordings with signal noise elimination.
• Configuration options for workflow automation, batch uploads, and large data set management.
• Rapid analysis module capable of processing 24-hour ECG traces in approximately 30 seconds.
• Capability to review, edit, and annotate processed ECG data before report finalization.
• Generation of comprehensive reports in graphical or tabular PDF formats, with export options to CSV.
• Multiserver architecture with data collection from biosensors and secure storage solutions.
• Support for multiple ECG file formats and device compatibility, including Holter monitor outputs.
• Implementation of beat classification algorithms for detailed arrhythmia and heart condition analysis.
• Cross-platform support ensuring usability across different operating systems.
• Security features aligned with healthcare compliance standards, including user authentication and audit trails.

• Medical biosensors and Holter monitor devices for raw data acquisition
• Healthcare data standards such as HL7, ISHNE, and EDF for interoperability
• Existing hospital or clinical data management systems for reporting and storage
• Secure cloud or on-premises storage solutions for processed data
• Laboratory and research data analysis tools for export and further processing

• Processing time of approximately 30 seconds per 24-hour ECG recording
• The system must support high volumes of data uploads and processing workflows
• Security compliant with healthcare standards, including user authentication, data encryption, and audit logs
• System availability and reliability suitable for clinical environments
• Scalability to accommodate future increases in data volume and feature expansion
• Cross-platform compatibility to ensure broad accessibility

The new ECG analysis software aims to significantly improve diagnostic accuracy by eliminating approximately 95% of noise in ECG signals, reducing analysis time from hours to minutes, and enabling automated workflows that process large data sets efficiently. By supporting comprehensive report generation and classification of heart activities, the system will enhance clinical decision-making, streamline research workflows, and ensure compliance with regulatory standards, ultimately leading to faster patient assessments and improved clinical outcomes.