arXiv:2510.27692v1 Announce Type: new 
Abstract: Non-contact electrocardiogram (ECG) reconstruction from radar signals offers a promising approach for unobtrusive cardiac monitoring. We present LifWavNet, a lifting wavelet network based on a multi-resolution analysis and synthesis (MRAS) model for radar-to-ECG reconstruction. Unlike prior models that use fixed wavelet approaches, LifWavNet employs learnable lifting wavelets with lifting and inverse lifting units to adaptively capture radar signal features and synthesize physiologically meaningful ECG waveforms. To improve reconstruction fidelity, we introduce a multi-resolution short-time Fourier transform (STFT) loss, that enforces consistency with the ground-truth ECG in both temporal and spectral domains. Evaluations on two public datasets demonstrate that LifWavNet outperforms state-of-the-art methods in ECG reconstruction and downstream vital sign estimation (heart rate and heart rate variability). Furthermore, intermediate feature visualization highlights the interpretability of multi-resolution decomposition and synthesis in radar-to-ECG reconstruction. These results establish LifWavNet as a robust framework for radar-based non-contact ECG measurement.

LifWavNet هو تطوير رائد في إعادة بناء تخطيط القلب الكهربائي (ECG) بدون اتصال باستخدام إشارات الرادار، مما يمهد الطريق لمراقبة القلب بشكل غير مزعج. يستخدم هذا النموذج المبتكر موجات رفع قابلة للتعلم، مما يسمح بالتقاط أكثر تكيفًا ودقة لإشارات الرادار مقارنة بالطرق التقليدية. هذه الخطوة مهمة لأنها تعزز إمكانية المراقبة الصحية عن بُعد، مما يسهل على المرضى تلقي الرعاية دون الشعور بعدم الراحة من طرق ECG التقليدية.

LifWavNet es un desarrollo innovador en la reconstrucción de electrocardiogramas (ECG) sin contacto utilizando señales de radar, lo que allana el camino para un monitoreo cardíaco discreto. Este modelo utiliza ondas de elevación aprendibles, lo que permite una captura más adaptativa y precisa de las señales de radar en comparación con los métodos tradicionales. Este avance es significativo ya que mejora el potencial de monitoreo de salud a distancia, facilitando que los pacientes reciban atención sin la incomodidad de los métodos de ECG tradicionales.

LifWavNet est un développement révolutionnaire dans la reconstruction d'électrocardiogrammes (ECG) sans contact utilisant des signaux radar, ouvrant la voie à une surveillance cardiaque discrète. Ce modèle innovant utilise des ondelettes de levée apprenables, permettant une capture des signaux radar plus adaptative et précise par rapport aux méthodes traditionnelles. Cette avancée est significative car elle améliore le potentiel de surveillance de la santé à distance, facilitant ainsi le soin des patients sans l'inconfort des méthodes ECG traditionnelles.

LifWavNet is a groundbreaking development in non-contact electrocardiogram (ECG) reconstruction using radar signals, paving the way for unobtrusive cardiac monitoring. This innovative model utilizes learnable lifting wavelets, allowing for a more adaptive and accurate capture of radar signals compared to traditional methods. This advancement is significant as it enhances the potential for remote health monitoring, making it easier for patients to receive care without the discomfort of traditional ECG methods.

LifWavNet: Lifting Wavelet-based Network for Non-contact ECG Reconstruction from Radar

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