A Lightweight Robust Approach for Automatic Heart Murmurs and Clinical Outcomes Classification from Phonocardiogram Recordings - Lu, Hui; Yip, Julia; Steigleder, Tobias; Grießhammer, Stefan; Heckel, Maria; Jami, Jitin; Eskofier, Björn; Ostgathe, Christoph; Kölpin, Alexander (2022 Computing in Cardiology (CinC), 2022)
Cardiac auscultation provides an efficient and costeffective way for cardiac disease pre-screening. The George B. Moody PhysioNet Challenge 2022 aimed to detect heart murmurs and clinical outcomes with heart sound recordings from multiple auscultation locations. Our team HearHeart proposed a lightweight convolutional neural network (CNN) to detect heart murmurs and a random forest model to classify clinical outcomes. 128 Melspectrogram features and wide features like the sociodemographic data and statistical features are extracted. Different techniques are employed to migrate the data imbalance and model the overfitting problem. We used two data augmentation methods, noise injection and spectrogram augmentation in time and frequency domain to increase the training samples and avoid overfitting during training. Besides, weighted loss functions are applied to both tasks to deal with data imbalance. In the end, we ensembled the models from cross-validation and used voting for the final classification. We achieved a murmur score of 0.791, and a clinical outcome score of 11731.64 on 5-fold cross-validation in the hidden validation set. While on the hidden test set, we achieved a murmur score of 0.780, and a clinical outcome score of 12110, placing our team 1st and 10th in the challenge tasks, respectivel
Keywords: Heart; Training; Frequency-domain analysis; Feature extraction; Data models; Recording; Convolutional neural networks

Automated Pipeline for Regional Epicardial Adipose Tissue Distribution Analysis in the Left Atrium - Lluch, Èric; Castañeda, Eduardo; Weiss, Maximilian E.R.; Vizitiu, Anamaria; Jacob, Athira; Jami, Jitin; Audigier, Chloé; Meister, Felix; Mihalef, Viorel; Passerini, Tiziano (Lecture Notes in Computer Science, 2025)
Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting approximately 3% of the global population and rising to 11% in individuals over 80 years old. The distribution of epicardial adipose tissue (EAT) around the atria plays a significant role in the pathophysiology of AF, yet its regional analysis remains underexplored. This paper introduces an automated pipeline capable of segmenting 14 atrial regions from patient CT angiograms, a notable advancement over previous attempts to automate the creation of synthetic anatomical or physics-based twins. Our fully automatic pipeline requires no human supervision and provides the regional EAT quantification successfully on 91.9% of the analyzed patients for which the LA and its structures were automatically segmented (1797 out of 1954 patients), including the automatic segmentation of the aorta and pericardium to assess EAT volume around the left atrium. By applying principal component analysis of the regional EAT to analyze a separate dataset of 84 individuals who were clinically labeled, we identified a distinct group that contained all the patients with AF. This finding suggests that there is a significant link between the pattern of fat distribution and AF. This novel approach has the potential to enhance our understanding of AF and improve patient outcomes by enabling large-scale, regional fat analysis in an automated manner on all patients with accurate segmentation of left atrial structures.
CRIS-Team Scopus Importer:2025-05-16
Keywords: atrial fibrillation; automatic pipeline; Epicardial adipose tissue

Tracking the visitor: An optical indoor system for visitor research in museums - Köferl, Franz; Jami, Jitin; Li, Jindong; Zanca, Dario; Eskofier, Björn; Zürl, Matthias (AI in Museums: Reflections, Perspectives and Applications, 2023)
The analysis of the success of any exhibition depends on the visitor experience. The data required for analysis is usually painstakingly collected by hand. We propose a large-scale optical tracking pipeline to estimate visitor data such as visit trajectory, duration, and, potentially, other personal parameters like age, weight, and sex, yet remain ethically acceptable by obtaining visitor consent. We further show, in preliminary results, that the edge device has a localization error of 0.64 meters and an average precision of 0.2. With this work-in-progress, we intend to ensure a viable alternative to current data collection processes in museum research.
CRIS-Team Scopus Importer:2024-12-16