Advanced tools for ambulatory ECG and respiratory analysis

The electrocardiogram or ECG is a relatively easy-to-record signal that contains an enormous amount of potentially useful information. It is currently mostly being used for screening purposes. For example, pre-participation cardiovascular screening of young athletes has been endorsed by both scientific organisations and sporting governing bodies. A typical cardiac examination is taken in a hospital environment and lasts 10 seconds. This is often sufficient to detect major pathologies, yet this small sample size of the heart?s functioning can be deceptive when used to evaluate one?s general condition. A solution for this problem is to monitor the patient outside of the hospital, during a longer period of time. Due to the extension of the analysis period, the detection rate of cardiac events can be highly increased, compared to the cardiac exam in the hospital. However, it also increases the likelihood of the signals being exposed to noise, which could decrease the diagnostic capabilities of the signals. Therefore, in the first part of this work, we present novel quality indication algorithms for cardiac and respiratory signals which could aid in cardiac-, respiratory- and cardiorespiratory analysis. These algorithms have shown good results on newly labelled datasets that were recorded with different recording devices. Additionally, we have shown that a transfer learning approach could be used to optimize an artefact detection algorithm that was trained with contact ECG towards non-contact ECG. When signal quality is ensured, most often the next step in cardiac analysis is the detection of heartbeats. This sounds like a straightforward task, and in many cases it is, but in some situations it could be a challenge. Hence, for such situations it is recommended to visually inspect and review each signal before further analysis. Many of the existing ECG analysis toolboxes assume that all heartbeats are correctly annotated and as such, do not provide any correction tools. Furthermore, the ones that do provide these, are not user friendly. Hence, in the second part of this work, we present a toolbox that can be used to detect heartbeats, visualize these together with the raw signal, and correct possible wrong annotations. Furthermore, we extended this toolbox for beat-to-beat variability of repolarization (BVR) analysis. We used this extension to investigate the temporal evolution in BVR before spontaneous non-sustained ventricular tachycardia (nsVT) in patients with ischaemic heart disease (IHD). Our preliminary results suggest that temporal changes in pre-arrhythmic BVR could be used to predict imminent nsVT events in IHD patients. In the last part of this work, we used the quality indication algorithm and R-peak detection and correction tool to investigate the strength of the cardiorespiratory coupling during exercise. The presented pipeline could be used similarly for other applications. Lastly, we have shown that the combination of ECG criteria with demographic and body composition features can be used to accurately estimate left ventricular mass in endurance athletes.