ECGbased

ECGbased refers to techniques and systems that rely on electrocardiography signals for clinical, monitoring, and research applications, as well as for biometric identification in some contexts. Electrocardiography records the electrical activity of the heart over time using surface electrodes and can be performed as a 12-lead clinical exam or as single-lead recordings in wearable devices. ECG data provide information on heart rate, rhythm, conduction, and waveform morphology.

ECG signals face challenges such as noise and artifacts from movement, poor electrode contact, and baseline wander. Standard processing involves preprocessing to remove noise, segmentation into beats or fixed-length windows, and normalization. Feature extraction may target time-domain metrics (heart rate, RR intervals, PR interval, QRS duration, QT and QTc), or morphological characteristics of the P, QRS, and T waves; frequency-domain features can also be used. More recently, machine learning and deep learning methods are applied to either engineered features or raw waveforms to classify rhythms, detect ischemia, predict events, or monitor health status.

Applications span clinical and consumer devices. In medicine, ECG-based algorithms support arrhythmia detection (including atrial fibrillation), monitor perioperative risk, and aid ongoing surveillance in ambulatory or intensive care settings. In biometric security, ECG-based recognition systems aim to identify individuals from their cardiac waveform, though deployment faces variability due to posture, activity, and device differences. Public datasets, such as those in PhysioNet and other ECG repositories, underpin much of the research and benchmarking in this area.

Limitations include variability across individuals and devices, the need for high-quality data, and concerns about privacy and regulatory compliance. Ongoing work addresses robustness, standardization of measurements, and integration with other physiological signals to improve reliability.