Background: Electrocardiogram (ECG) signal conditioning is a vital step in the ECG signal processing chain that ensures effective noise removal and accurate feature extraction. Objective: This study evaluates the performance of the FDA 510 (k) cleared HeartKey Signal Conditioning and QRS peak detection algorithms on a range of annotated public and proprietary ECG databases (HeartKey is a UK Registered Trademark of B-Secur Ltd). Methods: Seven hundred fifty-one raw ECG files from a broad range of use cases were individually passed through the HeartKey signal processing engine. The algorithms include several advanced filtering steps to enable significant noise removal and accurate identification of the QRS complex. QRS detection statistics were generated against the annotated ECG files. Results: HeartKey displayed robust performance across 14 ECG databases (seven public, seven proprietary), covering a range of healthy and unhealthy patient data, wet and dry electrode types, various lead configurations, hardware sources, and stationary/ambulatory recordings from clinical and non-clinical settings. Over the NSR, MIT-BIH, AHA, and MIT-AF public databases, average QRS Se and PPV values of 98.90% and 99.08% were achieved. Adaptable performance (Se 93.26%, PPV 90.53%) was similarly observed on the challenging NST database. Crucially, HeartKey's performance effectively translated to the dry electrode space, with an average QRS Se of 99.22% and PPV of 99.00% observed over eight dry electrode databases representing various use cases, including two challenging motion-based collection protocols. Conclusion: HeartKey demonstrated robust signal conditioning and QRS detection performance across the broad range of tested ECG signals. It should be emphasized that in no way have the algorithms been altered or trained to optimize performance on a given database, meaning that HeartKey is potentially a universal solution capable of

Introduction: Stress has been linked to numerous health conditions, including heart disease, diabetes, and mental health issues. By monitoring changes in physiological signals, such as heart rate (HR) and heart rate variability (HRV), wearable biosensing technology allows acute stress to be non-invasively tracked over long periods, providing valuable insights for preventative healthcare. Methods: This two-phase study comprised several protocols designed to induce varying levels of psychological stress in participants (N=39). HR and HRV metrics, derived from electrocardiogram (ECG) data collected throughout the protocol on the single lead HeartKey ® Chest Module, were used by the HeartKey Stress algorithm to generate a relative stress score (0-100), which was validated against two clinically recognized methodologies for assessing patient stress: i) state-trait anxiety index (STAI), a questionnaire that subjectively measured the individual’s perceptual stress after each stage of the protocol, and ii) electrodermal activity (EDA), which continuously monitored conductive changes at the skin’s surface with an Empatica ® E4 wrist wearable. Results: Over both phases, participant STAI scores increased significantly during stress protocols (49.9 ± 23.3) relative to the baseline (30.0 ± 10.0). Mean HR showed a similar significant increase ( p < 0.001), and HRV gradually decreased throughout the testing protocol. HeartKey Stress scores derived from HR and HRV data showed a strong correlation to STAI scores. Furthermore, the HeartKey Stress trend closely replicated that of the EDA data. Conclusions: HeartKey Stress algorithm consistently generated accurate and reliable stress scores in response to events of induced, acute psychological stress. The results suggest that the algorithm has potential utility for continuous clinical monitoring of patients with stress-related illnesses.