To apply different signal processing techniques on the ECG signal for detecting and analysing the ECG’s for the early detection of atrial and ventricular arrhythmias of cardiovascular diseases.
This a dynamically developing field where most of the at-the-edge research focuses not on topological features of ECGs but mostly on the information content and its evolution with time.
There was already achieved quite a big progress in several areas related to the heart and brain as those two provide easily measurable biosignal data: ECG and EEG.
I do recommend you to study the following fields:
1) Various entropy measures applied in assessing the properties of complex systems through their signature present in the biosignals.
2) Research on anesthesia depth evaluated from EEGs recordings.
3) Human gait studies.
4) Epileptic studies.
5) Heart arrhythmia studies.
6) There is existing one publication in Nature that uses deal learning techniques to predict arrhythmias from ECGs done on human data. They got 89% of sensitivity and specificity as far as I remember.
7) My research was done on the rabbit model, unluckily still not published, it reached 93% sensitivity and specificity up to one hour prior to the onset of the arrhythmias.
8) There is existing a bunch of paper using machine learning techniques but with not so high success.
9) There is published a bunch of good papers in postural changes of HR and sympathetic vs parasympathetic innervation activation.
We can go on like that for hours as the field is growing rapidly and everyone is trying something else.
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