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Item type:Publication, Access status: without files , Catch Your Breath! Vital Sign Sensing With Radar(2023-02-02); ; ; ; Vital signs are most essential in medical care to evaluate a patient’s health situation. Today, electronic devices reliably measure the most important vital signs: heartbeat and respiration. Electrocardiography (ECG) measures the electrophysiological heart activity, while breathing can be detected with impedance pneumography (IP), which evaluates the periodic change of thorax impedance as we breathe in and out. While continuous monitoring of vital signs provides valuable medical information that can improve diagnostics, enable adaptive therapy, and generate new medical knowledge, there are also disadvantages. Medical procedures like ECG or IP require cables and electrodes, thus considerably reducing a patient’s mobility. Furthermore, electrodes cause skin irritation if applied too long, which often limits the continuous monitoring of vital signs to situations of absolute medical necessity.Publicationtype: Journal Article169 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Accurate heart beat detection with doppler radar using bidirectional GRU networkHeart rate is one of the most critical and important vital signs in healthcare. While electrocardiography (ECG) is gold-standard procedure for heart rate monitoring, contactless monitoring is preferred in many applications like long-term monitoring. Radar systems enable contactless sensing by measuring small movements on the chest induced by the heart beat. In this paper, we present a machine learning-based method using a bidirectional gated recurrent unit (bi-GRU) network for accurate heartbeat detection. Band-pass filtered in-phase (I) and quadrature (Q) signals in heart sound and pulse wave frequency ranges were fused. The proposed method achieves a high F1 score of 98.06% for heart beat detection, thus outperforming the state-of-the-art method with an F1 score of 95.62% in the resting scenario. In the tilt-up scenario with the tilt table, F1 score is significantly improved by 10%. Besides, a median inter-beat intervals (IBIs) RMSE of only 22.07 ms in the resting scenario is realized.Publicationtype: Conference Paper113 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Influence of sensor position and body movements on radar-based heart rate monitoringPublicationtype: Conference Paper75 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks(IEEE, 2019-02); ; ; ; Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In clinical routine, hundreds of images are acquired for each patient which requires automatic plaque detection for fast and accurate decision support. So far, automatic approaches rely on classic machine learning methods and deep learning solutions have rarely been studied. Given the success of deep learning methods with other imaging modalities, a thorough understanding of deep learning-based plaque detection for future clinical decision support systems is required. We address this issue with a new dataset consisting of in-vivo patient images labeled by three trained experts. Using this dataset, we employ state-of-the-art deep learning models that directly learn plaque classification from the images. For improved performance, we study different transfer learning approaches. Furthermore, we investigate the use of cartesian and polar image representations and employ data augmentation techniques tailored to each representation. We fuse both representations in a multi-path architecture for more effective feature exploitation. Last, we address the challenge of plaque differentiation in addition to detection. Overall, we find that our combined model performs best with an accuracy of 91.7%, a sensitivity of 90.9% and a specificity of 92.4%. Our results indicate that building a deep learning-based clinical decision support system for plaque detection is feasiblePublicationtype: Journal Article178