Robust atrial fibrillation monitoring utilizing graphene aerogel-based nano-tattoo

Ergen O.

Materials Letters, vol.291, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 291
  • Publication Date: 2021
  • Doi Number: 10.1016/j.matlet.2021.129525
  • Journal Name: Materials Letters
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Wearable-electronics, Graphene-aerogels, Nanogenerators, Carbon materials, Porous materials, Artificial intelligence
  • Istanbul Technical University Affiliated: Yes


© 2021 Elsevier B.V.A frequent cardiac rhythm disorder, atrial fibrillation (AF), is associated with considerable cardiovascular risk, morbidity, and mortality. AF can lead to a stroke and heart failure by a factor of five. Early detection of AF is critical to prevent severe heart failure, however, AF remains undetected in patients as check-ups are too brief and continuous monitoring of cardiac rhythm is often limited. Because of recent advancements in technology, we are now able to monitor cardiac rhythm more consistently in patients with wearable devices. Many wearable devices are wrist-wearable and use photoplethysmography (PPG) sensors to detect AF due to their accessibility and applicability. However, acquiring a clean PPG signal directly from the wrist is challenging and subject to various motion artifacts which greatly interferes with the AF detection capabilities of these sensors. Even though, the PPG signal is often correlated with accelerometer data, deep learning algorithms, filtering methods, etc., it is challenging to overcome the motion artifact limitations. For this reason, new devices, immune to motion artifacts, are necessary. In this purpose, we developed a new aerogel-based nano-tattoo that acquires robust heart rate signals directly from the wrist, while reliably detecting AF, during extreme upper extremity motions, such as arm swing, repetitive elbow flexion/extension, waving, etc., using artificial intelligence based neural networks.