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Electrocardiography (ECG) is a key non-invasive diagnostic tool for cardiovascular diseases which is increasingly supported by algorithms based on machine learning. Major obstacles for the development of automatic ECG interpretation algorithms are both the lack of public datasets and well-defined benchmarking procedures to allow comparison s of different algorithms. To address these issues, we put forward PTB-XL, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length. The ECG-waveform data was annotated by up to two cardiologists as a multi-label dataset, where diagnostic labels were further aggregated into super and subclasses. the dataset covers a broad range of diagnostic classes including, in particular, a large fraction of healthy records. the combination with additional metadata on demographics, additional diagnostic statements, diagnosis likelihoods, manually annotated signal properties as well as suggested folds for splitting training and test sets turns the dataset into a rich resource for the development and the evaluation of automatic ECG interpretation algorithms.

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The structure of high-frequency components of electric and magnetic signals from the heart during the depolarisation phase is investigated. After averaging and broadband filtering with a binomial bandpass filter (37 Hz-90 Hz), the fragmentation of the QRS-complex is quantified. The number of extrema M and a new score value S are calculated from the signals of three electrical leads and one magnetic lead of 23 healthy subjects, 23 patients with coronary heart disease (CHD) without reported event of ventricular tachycardia or fibrillation at the time of measurement, and eight patients with CHD who have suffered from malignant tachycardia. For the parameter M, the sensitivity and specificity for healthy subjects against patients with CHD and ventricular tachycardia for the magnetic lead (the best electric lead) are 100% (75%) and 100% (100%). For the magnetic lead (best electric lead) and parameter S, the sensitivity and specificity are 100% (75%) and 95.6% (100%).

Electromagnetic waves can propagate through the body and are reflected at interfaces between materials with different dielectric properties. Therefore the reason for using ultrawideband (UWB) radar for probing the human body in the frequency range from 100 MHz up to 10 GHz is obvious and suggests an ability to monitor the motion of organs within the human body as well as obtaining images of internal structures. The specific advantages of UWB sensors are high temporal and spatial resolutions, penetration into object, low integral power, and compatibility with established narrowband systems. The sensitivity to ultralow power signals makes them suitable for human medical applications including mobile and continuous noncontact supervision of vital functions. Since no ionizing radiation is used, and due to the ultralow specific absorption rate applied, UWB techniques permit noninvasive sensing with no potential risks. This research aims at the synergetic use of UWB sounding combined with magnetic resonance imaging (MRI) to gain complementary information for improved functional diagnosis and imaging, especially to accelerate and enhance cardiac MRI by applying UWB radar as a noncontact navigator of myocardial contraction. To this end a sound understanding of how myocardial's mechanic is rendered by reflected and postprocessed UWB radar signals must be achieved. Therefore, we have executed the simultaneous acquisition and evaluation of radar signals with signals from a high-resolution electrocardiogram. The noncontact UWB illumination was done from several radiographic standard positions to monitor selected superficial myocardial areas during the cyclic physiological myocardial deformation in three different respiratory states. From our findings we could conclude that UWB radar can serve as a navigator technique for high and ultrahigh field magnetic resonance imaging and can be beneficial preserving the high resolution capability of this imaging modality. Furthermore it can potentially be used to support standard electrocardiography (ECG) analysis by complementary information where sole ECG analysis fails, e.g., electromechanical dissociation.