Computing in Cardiology

Traditionally the machine learning assisted quality assessment of biomedical signals (such as electrocardiogram - ECG, photoplethysmography - PPG) have classified a signal segment quality as ”good” or ”bad” and used this assessment to determine if the segment is usable for further processing steps, such as heart beat estimation. In principle, this is a suitable approach and can be justified by its straightforward implementation and applicability. However, in the case of body sensor networks with multiple simultaneously operating units, such as IMUs (Inertial Measurement Units) there is a need to select the best performing axes for further processing, instead of processing the data among all axes (which can be computationally intensive). For a single IMU, there are already six separate acceleration and angular velocity axes to be evaluated. In this paper, instead of classifying the signal segments simply as ”good” or ”bad” quality we propose a learning to rank based approach for the quality assessment of cardiac signals, which is able to determine the relative importance of a signal axis or waveform. We illustrate that the method can generalize between multiple human experts annotated ground truths in automated best axis selection and ranking of signal segments based on their quality.</p

Computing in Cardiology 2016

Cardiac, respiratory, and patient body motion artifacts degrade the image quality and quantitative accuracy of the nuclear medicine imaging which may lead to incorrect diagnosis, unnecessary treatment and insufficient therapy. We present a new miniaturized system including joint micro electromechanical (MEMS) accelerometer and gyroscope sensors for simultaneous extraction of cardiac and respiratory signals. We employ two tri-axial joint MEMS sensors for selecting an optimal trigger point in a cardiac and respiratory cycle. The 6-axis motion sensing helps to detect candidate features for cardiac and respiratory gating in Positron emission tomography (PET) imaging. The aim of this study was to validate MEMS-derived signals against traditional Real-time Position Management (RPM) and electrocardiography (ECG) measurement systems in 4 healthy volunteers. High agreement and correlation were found between cardiac and respiratory cycle intervals. These promising first results warrant for further investigations. </p

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table